Supporting Decision-Making in the Design of Production ...

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ISBN 978-91-7485-443-5ISSN 1651-4238

Address: P.O. Box 883, SE-721 23 Västerås. SwedenAddress: P.O. Box 325, SE-631 05 Eskilstuna. SwedenE-mail: [email protected] Web: www.mdh.se

Supporting Decision-Making in the Design of Production SystemsA Discrete Event Simulation perspective

Erik Flores-García

Mälardalen University Doctoral Dissertation 299

This doctoral thesis in Innovation and Design addresses the use of DES for support-ing decision-making during the design of production systems involving process innovations. Manufacturing companies increasingly require introducing process innovations to achieve increased competitiveness. agn of production systems is considered a means to fulfill process innovations. Yet, manufacturing companies find it difficult to support decision-making during the design of production systems involving process innovations. One way to support decision-making during the design of production systems is through the use of Discrete Event Simulation (DES).

This thesis adopts a case study method to draw empirical data from three produc-tion system design projects at one manufacturing company of the heavy vehicle industry. The findings identify conditions of use, challenges, requirements, and activities not previously reported in literature essential for the use of DES.

This thesis provides five novel findings essential for supporting decision-making during the design of production systems involving process innovations. In addition, this thesis presents a framework contributing to the use of Discrete Event Simula-tion for supporting decision-making at manufacturing companies. Based on this framework, manufacturing companies can supervise formal activities involving the use of DES in the design of production systems, anticipate and respond to equivocality and uncertainty inherent to process innovations.

Erik Flores-Garcia is a Ph.D candidate in Innovation and Design at the School of Innovation, Design, and Engineering at Mälardalen University. He is part of the INNOFACTURE+ Industrial Research School. Erik holds a M.Sc. in Production and Logistics from Mälardalen Uni-versity, and a B.Sc. in Mechatronics from the Monterrey Institute of Technology and Higher Education (ITESM). Before his Ph.D. studies Erik worked in the automotive and automation industries for several years.

Mälardalen University Press DissertationsNo. 299

SUPPORTING DECISION-MAKING IN THEDESIGN OF PRODUCTION SYSTEMS

A DISCRETE EVENT SIMULATION PERSPECTIVE

Erik Flores-García

2019

School of Innovation, Design and Engineering


SUPPORTING DECISION-MAKING IN THEDESIGN OF PRODUCTION SYSTEMS

A DISCRETE EVENT SIMULATION PERSPECTIVE

Erik Flores-García

2019

School of Innovation, Design and Engineering

1

Copyright © Erik Flores-García, 2019 ISBN 978-91-7485-443-5ISSN 1651-4238Printed by E-Print AB, Stockholm, Sweden

Copyright © Erik Flores-García, 2019 ISBN 978-91-7485-443-5ISSN 1651-4238Printed by E-Print AB, Stockholm, Sweden

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SUPPORTING DECISION-MAKING IN THE DESIGN OF PRODUCTION SYSTEMSA DISCRETE EVENT SIMULATION PERSPECTIVE

Erik Flores-García

Akademisk avhandling

som för avläggande av filosofie doktorsexamen i innovation och design vidAkademin för innovation, design och teknik kommer att offentligen försvaras

fredagen den 29 november 2019, 13.00 i Filen, Mälardalens högskola, Eskilstuna.

Fakultetsopponent: Docent Thomas Ditlev Brunø, Aalborg University

Akademin för innovation, design och teknik


SUPPORTING DECISION-MAKING IN THE DESIGN OF PRODUCTION SYSTEMSA DISCRETE EVENT SIMULATION PERSPECTIVE

Erik Flores-García

Akademisk avhandling

som för avläggande av filosofie doktorsexamen i innovation och design vidAkademin för innovation, design och teknik kommer att offentligen försvaras

fredagen den 29 november 2019, 13.00 i Filen, Mälardalens högskola, Eskilstuna.

Fakultetsopponent: Docent Thomas Ditlev Brunø, Aalborg University

Akademin för innovation, design och teknik

3

AbstractManufacturing companies are introducing process innovations, namely new production processes or technologies, to achieve increased competitiveness. Production systems design can ensure the fulfillment of process innovations. However, literature shows that the staff responsible for the design of production systems face unfamiliar circumstances, lack of consensus or understanding (equivocality), and absence of information (uncertainty). Hence, manufacturing companies find it difficult to support decision-making in the design of production systems leading to increased competitiveness. One way to support decision-making during production systems design is through discrete-event simulation (DES). However, there is limited understanding of the application of DES in decision-making support, in this context.

Therefore, the purpose of this thesis is to support decision-making through DES in the design of production systems involving process innovations. To this end, the thesis reviews the current understanding of production system design, including decision-making and DES. This thesis adopts a qualitative case study method to extract empirical data from three production systems design projects of a manufacturing company in the heavy vehicle industry.

The thesis offers several contributions. Firstly, the findings identify the conditions of use, challenges, requirements, and activities essential for the utilization of DES during production system design related to process innovations. These important findings are critical for supporting decision-making when manufacturing companies renew their production processes. Secondly, this thesis reveals that determining the conditions of use of DES for supporting decision-making rests on the structuredness of a decision (e.g. its degree of equivocality or analyzability), and the quantitative or qualitative nature or DES models. Thirdly, the results describe four novel findings about the challenges undermining the use of DES including equivocality, uncertainty, and the lack of a structured approach and the absence of resources for DES use. Fourthly, the results reveal three requirements necessary for the use of DES including analyzing information consensus, specifying the activities of conceptual models, and coordinating DES models with the information needs. Fifthly, this thesis provides three valuable findings describing additional activities in the design of production systems related to defining the objectives of DES models, and facilitating a structured approach and the management of resources for the use of DES.

This thesis present a framework that contributes to the use of DES for decision-making support at manufacturing companies. Based on this framework, managers of those companies can supervise formal activities involving the use of DES in production systems design.

ISBN 978-91-7485-443-5ISSN 1651-4238

AbstractManufacturing companies are introducing process innovations, namely new production processes or technologies, to achieve increased competitiveness. Production systems design can ensure the fulfillment of process innovations. However, literature shows that the staff responsible for the design of production systems face unfamiliar circumstances, lack of consensus or understanding (equivocality), and absence of information (uncertainty). Hence, manufacturing companies find it difficult to support decision-making in the design of production systems leading to increased competitiveness. One way to support decision-making during production systems design is through discrete-event simulation (DES). However, there is limited understanding of the application of DES in decision-making support, in this context.

Therefore, the purpose of this thesis is to support decision-making through DES in the design of production systems involving process innovations. To this end, the thesis reviews the current understanding of production system design, including decision-making and DES. This thesis adopts a qualitative case study method to extract empirical data from three production systems design projects of a manufacturing company in the heavy vehicle industry.

The thesis offers several contributions. Firstly, the findings identify the conditions of use, challenges, requirements, and activities essential for the utilization of DES during production system design related to process innovations. These important findings are critical for supporting decision-making when manufacturing companies renew their production processes. Secondly, this thesis reveals that determining the conditions of use of DES for supporting decision-making rests on the structuredness of a decision (e.g. its degree of equivocality or analyzability), and the quantitative or qualitative nature or DES models. Thirdly, the results describe four novel findings about the challenges undermining the use of DES including equivocality, uncertainty, and the lack of a structured approach and the absence of resources for DES use. Fourthly, the results reveal three requirements necessary for the use of DES including analyzing information consensus, specifying the activities of conceptual models, and coordinating DES models with the information needs. Fifthly, this thesis provides three valuable findings describing additional activities in the design of production systems related to defining the objectives of DES models, and facilitating a structured approach and the management of resources for the use of DES.

This thesis present a framework that contributes to the use of DES for decision-making support at manufacturing companies. Based on this framework, managers of those companies can supervise formal activities involving the use of DES in production systems design.

ISBN 978-91-7485-443-5ISSN 1651-4238

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Supporting Decision-Making in the Design of Production Systems A Discrete Event Simulation Perspective

Supporting Decision-Making in the Design of Production Systems A Discrete Event Simulation Perspective

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ABSTRACT Manufacturing companies are introducing process innovations, namely new production processes or technologies, to achieve increased competitiveness. Production system design can ensure the fulfillment of process innovations. However, literature shows that the staff responsible for the design of production systems face unfamiliar circumstances, lack of consensus or understanding (equivocality), and absence of information (uncertainty). Hence, manufacturing companies find it difficult to support decision-making in the design of production systems leading to increased competitiveness. One way to support decision-making during production system design is through discrete event simulation (DES). However, there is limited understanding of the application of DES for supporting decision-making, in this context.

Therefore, the purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. To this end, the thesis reviews the current understanding of production system design, including decision-making and DES. This thesis adopts a qualitative case study method to extract empirical data from three production system design projects of a manufacturing company in the heavy vehicle industry.

The thesis offers several contributions. Firstly, this thesis reveals that determining the conditions of use of DES for supporting decision-making rests on the structuredness of a decision (e.g. its degree of equivocality or analyzability), and the quantitative or qualitative nature of DES models. Secondly, the results describe four novel findings about the challenges undermining the use of DES including equivocality, uncertainty, and the lack of a structured approach and the absence of resources for DES use. Thirdly, the results reveal three requirements necessary for the use of DES including analyzing information consensus, specifying the activities of conceptual models, and coordinating DES models with the information needs. Fourthly, this thesis provides three valuable findings describing additional activities in the design of production systems related to defining the objectives of DES models, and facilitating a structured approach and the management of resources for the use of DES. Fifthly, this thesis argues that manufacturing companies adopting the continuous use of DES during production system design benefit from qualitative analysis, and DES can be a powerful means of sharing concerns, testing ideas, and reaching consensus, which is essential when involving process innovations.

This thesis present a framework that contributes to the use of DES for supporting decision-making at manufacturing companies. Based on this framework, managers of those companies can supervise formal activities involving the use of DES in production system design.

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ABSTRACT Manufacturing companies are introducing process innovations, namely new production processes or technologies, to achieve increased competitiveness. Production system design can ensure the fulfillment of process innovations. However, literature shows that the staff responsible for the design of production systems face unfamiliar circumstances, lack of consensus or understanding (equivocality), and absence of information (uncertainty). Hence, manufacturing companies find it difficult to support decision-making in the design of production systems leading to increased competitiveness. One way to support decision-making during production system design is through discrete event simulation (DES). However, there is limited understanding of the application of DES for supporting decision-making, in this context.

Therefore, the purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. To this end, the thesis reviews the current understanding of production system design, including decision-making and DES. This thesis adopts a qualitative case study method to extract empirical data from three production system design projects of a manufacturing company in the heavy vehicle industry.

The thesis offers several contributions. Firstly, this thesis reveals that determining the conditions of use of DES for supporting decision-making rests on the structuredness of a decision (e.g. its degree of equivocality or analyzability), and the quantitative or qualitative nature of DES models. Secondly, the results describe four novel findings about the challenges undermining the use of DES including equivocality, uncertainty, and the lack of a structured approach and the absence of resources for DES use. Thirdly, the results reveal three requirements necessary for the use of DES including analyzing information consensus, specifying the activities of conceptual models, and coordinating DES models with the information needs. Fourthly, this thesis provides three valuable findings describing additional activities in the design of production systems related to defining the objectives of DES models, and facilitating a structured approach and the management of resources for the use of DES. Fifthly, this thesis argues that manufacturing companies adopting the continuous use of DES during production system design benefit from qualitative analysis, and DES can be a powerful means of sharing concerns, testing ideas, and reaching consensus, which is essential when involving process innovations.

This thesis present a framework that contributes to the use of DES for supporting decision-making at manufacturing companies. Based on this framework, managers of those companies can supervise formal activities involving the use of DES in production system design.

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SAMMANFATTNING Tillverkningsföretag introducerar processinnovationer, det vill säga nya processer och tekniker, för att stärka sin konkurrenskraft. Produktionssystemets utformning kan hjälpa processinnovationerna att nå sitt mål. Litteraturen visar dock att den personal som ansvarar för att utforma produktionssystem får problem med omständigheter de inte är förtrogna med, brist på samförstånd eller förståelse (mångtydighet) samt brist på information (osäkerhet). Detta gör att det är svårt för tillverkningsföretag att fatta beslut om utformning av produktionssystem som sedan leder till stärkt konkurrenskraft. Ett sätt att hjälpa beslutsfattandet vid utformning av produktionssystem är genom DES (discrete event simulation). Förståelsen är dock begränsad av hur DES kan användas till hjälp för beslutsfattandet i det här sammanhanget.

Syftet med den här avhandlingen är därför att underlätta beslutsfattandet med DES inom utformning av produktionssystem som involverar processinnovation. Med detta som målsättning görs en översikt av den nuvarande förståelsen av hur produktionssystem kan utformas, inklusive beslutsfattande och DES. Avhandlingen använder en kvalitativ fallstudie för att utvinna empiriska data från tre utformningsprojekt för produktionssystem på ett tillverkningsföretag inom den tunga fordonsindustrin.

Avhandlingen gör ett flertal bidrag. För det första visar den att fastställandet av användningsförhållanden för DES till stöd för beslutsfattande vilar på hur strukturerat ett beslut är (t.ex. hur mångtydigt eller analyserbart det är), och DES-modellernas kvantitativa eller kvalitativa egenskaper. För det andra beskriver resultaten fyra nya fynd om de utmaningar som undergräver användningen av DES, inklusive mångtydighet, osäkerhet, bristen på strukturerat arbetssätt och frånvaron av resurser för DES. För det tredje visar resultaten tre nödvändiga krav för att DES ska kunna användas, inklusive en analys av informationssamförstånd, att aktiviteter för konceptuella modeller specificeras, och att DES-modellerna samordnas med informationsbehov. För det fjärde redovisar avhandlingen tre värdefulla fynd som beskriver ytterligare aktiviteter inom utformning av produktionssystem i relation till definierandet av DES-modellernas målsättningar, och som underlättar ett strukturerat arbetssätt och hantering av resurser för DES. För det femte argumenterar avhandlingen för att tillverkningsföretag som löpande tillämpar DES för utformning av produktionssystem drar nytta av kvalitativ analys, och att DES kan vara en kraftfull metod för att kommunicera problem, pröva idéer och nå samförstånd, vilket är centralt för processinnovation.

Avhandlingen presenterar ett ramverk som bidrar till användningen av DES till stöd för beslutsfattande på tillverkningsföretag. Utifrån det ramverket kan chefer på dessa företag överse formella aktiviteter där DES används inom utformning av produktionssystem.

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SAMMANFATTNING Tillverkningsföretag introducerar processinnovationer, det vill säga nya processer och tekniker, för att stärka sin konkurrenskraft. Produktionssystemets utformning kan hjälpa processinnovationerna att nå sitt mål. Litteraturen visar dock att den personal som ansvarar för att utforma produktionssystem får problem med omständigheter de inte är förtrogna med, brist på samförstånd eller förståelse (mångtydighet) samt brist på information (osäkerhet). Detta gör att det är svårt för tillverkningsföretag att fatta beslut om utformning av produktionssystem som sedan leder till stärkt konkurrenskraft. Ett sätt att hjälpa beslutsfattandet vid utformning av produktionssystem är genom DES (discrete event simulation). Förståelsen är dock begränsad av hur DES kan användas till hjälp för beslutsfattandet i det här sammanhanget.

Syftet med den här avhandlingen är därför att underlätta beslutsfattandet med DES inom utformning av produktionssystem som involverar processinnovation. Med detta som målsättning görs en översikt av den nuvarande förståelsen av hur produktionssystem kan utformas, inklusive beslutsfattande och DES. Avhandlingen använder en kvalitativ fallstudie för att utvinna empiriska data från tre utformningsprojekt för produktionssystem på ett tillverkningsföretag inom den tunga fordonsindustrin.

Avhandlingen gör ett flertal bidrag. För det första visar den att fastställandet av användningsförhållanden för DES till stöd för beslutsfattande vilar på hur strukturerat ett beslut är (t.ex. hur mångtydigt eller analyserbart det är), och DES-modellernas kvantitativa eller kvalitativa egenskaper. För det andra beskriver resultaten fyra nya fynd om de utmaningar som undergräver användningen av DES, inklusive mångtydighet, osäkerhet, bristen på strukturerat arbetssätt och frånvaron av resurser för DES. För det tredje visar resultaten tre nödvändiga krav för att DES ska kunna användas, inklusive en analys av informationssamförstånd, att aktiviteter för konceptuella modeller specificeras, och att DES-modellerna samordnas med informationsbehov. För det fjärde redovisar avhandlingen tre värdefulla fynd som beskriver ytterligare aktiviteter inom utformning av produktionssystem i relation till definierandet av DES-modellernas målsättningar, och som underlättar ett strukturerat arbetssätt och hantering av resurser för DES. För det femte argumenterar avhandlingen för att tillverkningsföretag som löpande tillämpar DES för utformning av produktionssystem drar nytta av kvalitativ analys, och att DES kan vara en kraftfull metod för att kommunicera problem, pröva idéer och nå samförstånd, vilket är centralt för processinnovation.

Avhandlingen presenterar ett ramverk som bidrar till användningen av DES till stöd för beslutsfattande på tillverkningsföretag. Utifrån det ramverket kan chefer på dessa företag överse formella aktiviteter där DES används inom utformning av produktionssystem.

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ACKNOWLEDGEMENTS

This research is funded by KK-stiftelsen (the Knowledge Foundation), INNOFACTURE Research School, the participating companies, and Mälardalen University. The research work is also a part of the initiative for Excellence in Production Research (XPRES), which is a collaboration between Mälardalen University, the Royal Institute of Technology, and Swerea.

I would like to express my gratitude to my supervising team that has been there from way before I began pursuing a PhD. Jessica, I am grateful for your friendship, counsel, critique, and all opportunities you have given me. Mats, I am forever in your debt for convincing me to enroll to the Innofacture Research School. It was a fantastic ride. Magnus, thank you for your support during this journey. You three are the reason I became a PhD student in the first place. Having had the opportunity to work with you has changed my life in ways I had not anticipated.

Innofactures, you have made this journey worthwhile. Thank you Anna, Lina, Mats, Peter, Narges, Ali, Mohsin, Bhanoday, Natalia, Jonathan, Fredrik, Christer, Sasha, Siavash, Farhad, Maryam, Daniel, Joel, and Catarina. I am grateful for you friendship, Anna G, Karin, Nina, Koteshwar, Helena, Peter, Anders Hellström, Anders Berglund, and Glenn. You all have made a difference. Likewise, I am fortunate for having had the opportunity to perform research at Volvo Construction Equipment. I am grateful for the trust of all managers and engineers from Arvika, Braås, Eskilstuna, Hallsberg, Konz, Shippensburg, Pederneiras, and Wroclaw who enriched my learning with their insight. There are too many of you to name. Thank you all.

I am enormously grateful to my mother, father, and brother for their endless love, support, amazing opportunities, and all sacrifices they made to make this day happen. I would like to express my deepest gratitude to my better half, Sanna, for her encouragement, patience, and love. I thank her for being there all the way.

Lastly, this thesis would not have been realized without the support of the Swedish weather that kept me indoors.

Erik Flores-García

Eskilstuna, October 2019

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ACKNOWLEDGEMENTS

This research is funded by KK-stiftelsen (the Knowledge Foundation), INNOFACTURE Research School, the participating companies, and Mälardalen University. The research work is also a part of the initiative for Excellence in Production Research (XPRES), which is a collaboration between Mälardalen University, the Royal Institute of Technology, and Swerea.

I would like to express my gratitude to my supervising team that has been there from way before I began pursuing a PhD. Jessica, I am grateful for your friendship, counsel, critique, and all opportunities you have given me. Mats, I am forever in your debt for convincing me to enroll to the Innofacture Research School. It was a fantastic ride. Magnus, thank you for your support during this journey. You three are the reason I became a PhD student in the first place. Having had the opportunity to work with you has changed my life in ways I had not anticipated.

Innofactures, you have made this journey worthwhile. Thank you Anna, Lina, Mats, Peter, Narges, Ali, Mohsin, Bhanoday, Natalia, Jonathan, Fredrik, Christer, Sasha, Siavash, Farhad, Maryam, Daniel, Joel, and Catarina. I am grateful for you friendship, Anna G, Karin, Nina, Koteshwar, Helena, Peter, Anders Hellström, Anders Berglund, and Glenn. You all have made a difference. Likewise, I am fortunate for having had the opportunity to perform research at Volvo Construction Equipment. I am grateful for the trust of all managers and engineers from Arvika, Braås, Eskilstuna, Hallsberg, Konz, Shippensburg, Pederneiras, and Wroclaw who enriched my learning with their insight. There are too many of you to name. Thank you all.

I am enormously grateful to my mother, father, and brother for their endless love, support, amazing opportunities, and all sacrifices they made to make this day happen. I would like to express my deepest gratitude to my better half, Sanna, for her encouragement, patience, and love. I thank her for being there all the way.

Lastly, this thesis would not have been realized without the support of the Swedish weather that kept me indoors.

Erik Flores-García

Eskilstuna, October 2019

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PUBLICATIONS

APPENDED PAPERS IN THE THESIS

Paper 1

Flores-García, E., Bruch, J., Wiktorsson, M., and Jackson, M. 2019. “Decision-Making Approaches in Process Innovations: An Explorative Case Study.” The Journal of Manufacturing Technology Management (In Press).

My contribution: I identified and formulated the problem, collected and analyzed data, tested my manuscript with senior researchers, and wrote and revised the paper after receiving feedback from Bruch, Wiktorsson, and Jackson.

Paper 2

Etxagibel, A. and Flores-García, E. 2017. “Approaching the Reduction of Uncertainty in Production System Design through Discrete Event Simulation.” In Proceedings of Simulation in Produktion und Logistik 2017 Conference, edited by S. Wenzel, and T. Peter, 229-238. Kassel University Press GmbH.

My contribution: I identified and formulated the problem, and jointly collected and analyzed data with Extagibel. Both authors contributed in equal parts to the writing of the paper.

Paper 3

Flores-García, E., Wiktorsson, M., Bruch, J., and Jackson, M. 2019. “Challenges of Discrete Event Simulation in the Early Stages of Production System Design.” International Journal of Industrial Engineering: Theory, Applications, and Practice (In Press).

My contribution: I identified and formulated the problem, collected and analyzed data, and wrote and revised the paper after receiving feedback from Wiktorsson, Jackson, and Bruch.

Paper 4

Flores-García, E., Bruch, J., Wiktorsson, M., and Jackson, M. 2019. “What Guides Information Consensus? Approaching the Reduction of Equivocality in Process Innovations”. International Journal of Manufacturing Research (In Press).

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PUBLICATIONS

APPENDED PAPERS IN THE THESIS

Paper 1

Flores-García, E., Bruch, J., Wiktorsson, M., and Jackson, M. 2019. “Decision-Making Approaches in Process Innovations: An Explorative Case Study.” The Journal of Manufacturing Technology Management (In Press).


Paper 2

Etxagibel, A. and Flores-García, E. 2017. “Approaching the Reduction of Uncertainty in Production System Design through Discrete Event Simulation.” In Proceedings of Simulation in Produktion und Logistik 2017 Conference, edited by S. Wenzel, and T. Peter, 229-238. Kassel University Press GmbH.

My contribution: I identified and formulated the problem, and jointly collected and analyzed data with Extagibel. Both authors contributed in equal parts to the writing of the paper.

Paper 3

Flores-García, E., Wiktorsson, M., Bruch, J., and Jackson, M. 2019. “Challenges of Discrete Event Simulation in the Early Stages of Production System Design.” International Journal of Industrial Engineering: Theory, Applications, and Practice (In Press).

My contribution: I identified and formulated the problem, collected and analyzed data, and wrote and revised the paper after receiving feedback from Wiktorsson, Jackson, and Bruch.

Paper 4

Flores-García, E., Bruch, J., Wiktorsson, M., and Jackson, M. 2019. “What Guides Information Consensus? Approaching the Reduction of Equivocality in Process Innovations”. International Journal of Manufacturing Research (In Press).

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Paper 5

Flores-García, E., Zúñiga, E. R., Bruch, J., Moris, M. U. and Syberfeldt, A. 2018. “Simulation-Based Optimization for Facility Layout Design in Conditions of High Uncertainty”. Procedia CIRP, Vol. 72 No. pp. 334-339.

My contribution: I identified and formulated the problem, and jointly collected and analyzed data with Zúñiga. Zúñiga and I wrote and revised the paper after receiving feedback from Bruch, Moris, and Syberfeldt.

Paper 6

Flores-García, E., Wiktorsson, M., Jackson, M., and Bruch, J. 2015. “Simulation in the Production System Design Process of Assembly Systems.” In Proceedings of the 2015 Winter Simulation Conference, edited by L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, 2124–2135. Huntington Beach, CA: IEEE Press.

My contribution: Wiktorsson, Jackson, and Bruch helped identify and formulate the problem. I collected and analyzed data, and wrote and revised the manuscript after receiving feedback from Wiktorsson, Jackson, and Bruch.

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Paper 5

Flores-García, E., Zúñiga, E. R., Bruch, J., Moris, M. U. and Syberfeldt, A. 2018. “Simulation-Based Optimization for Facility Layout Design in Conditions of High Uncertainty”. Procedia CIRP, Vol. 72 No. pp. 334-339.

My contribution: I identified and formulated the problem, and jointly collected and analyzed data with Zúñiga. Zúñiga and I wrote and revised the paper after receiving feedback from Bruch, Moris, and Syberfeldt.

Paper 6

Flores-García, E., Wiktorsson, M., Jackson, M., and Bruch, J. 2015. “Simulation in the Production System Design Process of Assembly Systems.” In Proceedings of the 2015 Winter Simulation Conference, edited by L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, 2124–2135. Huntington Beach, CA: IEEE Press.

My contribution: Wiktorsson, Jackson, and Bruch helped identify and formulate the problem. I collected and analyzed data, and wrote and revised the manuscript after receiving feedback from Wiktorsson, Jackson, and Bruch.

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ADDITIONAL PUBLICATIONS

Sannö, A., Öberg, A. E., Flores-Garcia, E. and Jackson, M. 2019. “Increasing the Impact of Industry–Academia Collaboration through Co-Production.” Technology Innovation Management Review, Vol. 9 No. 4, pp. 37-47. Flores-García, E. 2019. “Exploring DES Use in Production System Design When Implementing Process Innovations.” In Proceedings of the 2019 Winter Simulation Conference, edited by N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y-J. Son National Harbor, Maryland (Accepted). Andersson, S., Flores-García E., and Bruch, J. 2019. “Enabling Problem-Based Education in Collaboration with Manufacturing Companies.” In Proceeding of the 26th EurOMA Conference, Helsinki, Finland.

Zúñiga, E. R., Flores-García, E., Moris, M. U. and A. Syberfeldt. 2019. “Challenges of Simulation-based Optimization for Facility Layout Design of Production Systems.” In Proceedings of the 17th International Conference on Manufacturing Research, Belfast, U.K.

Flores-García, E., Wiktorsson, M., Bruch, J., and Jackson, M. 2018. “Revisiting Challenges in Using Discrete Event Simulation in Early Stages of Production System Design.” In Proceedings of the 2018 International Conference on Advances in Production Management Systems, 534-540. Korea, Seoul: Springer International Publishing. Bärring, M., Johansson, B., Flores-García, E., Bruch, J., and Wahlstrom, M. 2018. “Challenges of Data Acquisition for Simulation Models of Production Systems in Need of Standards.” In Proceedings of the 2018 Winter Simulation Conference, edited by M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, 691-702. Gothenburg, Sweden: IEEE. Flores-García, E. 2017. “Supporting Production System Design Decisions through Discrete Event Simulation.” Licentiate Thesis. Mälardalen University. Flores-García, E., Bruch, J., Wiktorsson, M. and Jackson, M. 2016. “Towards a Reduction of Uncertainty in Production System Design Decisions.” In Proceedings of the 2016 Swedish Production Symposium, Lund, Sweden. Flores-García, E., Jackson, M., and Wiktorsson, M. 2014. “A Virtual Verification Approach Towards Evaluating a Multi-Product Assembly Systems.” In Proceedings of the 2014 Swedish Production Symposium 2014, Göteborg, Sweden.

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ADDITIONAL PUBLICATIONS

Sannö, A., Öberg, A. E., Flores-Garcia, E. and Jackson, M. 2019. “Increasing the Impact of Industry–Academia Collaboration through Co-Production.” Technology Innovation Management Review, Vol. 9 No. 4, pp. 37-47. Flores-García, E. 2019. “Exploring DES Use in Production System Design When Implementing Process Innovations.” In Proceedings of the 2019 Winter Simulation Conference, edited by N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y-J. Son National Harbor, Maryland (Accepted). Andersson, S., Flores-García E., and Bruch, J. 2019. “Enabling Problem-Based Education in Collaboration with Manufacturing Companies.” In Proceeding of the 26th EurOMA Conference, Helsinki, Finland.

Zúñiga, E. R., Flores-García, E., Moris, M. U. and A. Syberfeldt. 2019. “Challenges of Simulation-based Optimization for Facility Layout Design of Production Systems.” In Proceedings of the 17th International Conference on Manufacturing Research, Belfast, U.K.

Flores-García, E., Wiktorsson, M., Bruch, J., and Jackson, M. 2018. “Revisiting Challenges in Using Discrete Event Simulation in Early Stages of Production System Design.” In Proceedings of the 2018 International Conference on Advances in Production Management Systems, 534-540. Korea, Seoul: Springer International Publishing. Bärring, M., Johansson, B., Flores-García, E., Bruch, J., and Wahlstrom, M. 2018. “Challenges of Data Acquisition for Simulation Models of Production Systems in Need of Standards.” In Proceedings of the 2018 Winter Simulation Conference, edited by M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, 691-702. Gothenburg, Sweden: IEEE. Flores-García, E. 2017. “Supporting Production System Design Decisions through Discrete Event Simulation.” Licentiate Thesis. Mälardalen University. Flores-García, E., Bruch, J., Wiktorsson, M. and Jackson, M. 2016. “Towards a Reduction of Uncertainty in Production System Design Decisions.” In Proceedings of the 2016 Swedish Production Symposium, Lund, Sweden. Flores-García, E., Jackson, M., and Wiktorsson, M. 2014. “A Virtual Verification Approach Towards Evaluating a Multi-Product Assembly Systems.” In Proceedings of the 2014 Swedish Production Symposium 2014, Göteborg, Sweden.

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TABLE OF CONTENTS ABSTRACT ......................................................................................................... III

SAMMANFATTNING ........................................................................................... V

ACKNOWLEDGEMENTS ................................................................................ VII

PUBLICATIONS ................................................................................................. IX

1 INTRODUCTION ............................................................................................ 1

1.1 RESEARCH BACKGROUND ......................................................................................1

1.2 RESEARCH PROBLEM ...............................................................................................3

1.3 PURPOSE AND RESEARCH QUESTIONS .............................................................5

1.4 SCOPE AND OUTLINE OF THE THESIS ................................................................6

2 FRAME OF REFERENCE ............................................................................ 9

2.1 PRODUCTION SYSTEM DESIGN .............................................................................9

2.1.1 Defining Production Systems ..................................................................................9

2.1.2 Designing Production Systems ............................................................................ 10

2.1.3 Equivocality and Uncertainty in Production System Design ............................ 13

2.1.4 Decisions in Production System Design ............................................................. 14

2.1.5 Decision-Making in Production System Design ................................................ 16

2.2 DES IN PRODUCTION SYSTEM DESIGN ........................................................... 19

2.2.1 Defining DES in Production System Design ...................................................... 19

2.2.2 Challenges of using DES in Production System Design .................................. 21

2.3 SUMMARY OF FRAME OF REFERENCE ............................................................ 23

3 RESEARCH METHOD ................................................................................ 25

3.1 RESEARCH APPROACH ......................................................................................... 25

3.2 RESEARCH PROCESS ............................................................................................ 26

3.3 RESEARCH DESIGN ................................................................................................ 28

3.4 EMPIRICAL BACKGROUND AND DESCRIPTION OF PROJECTS................. 30

3.5 DATA COLLECTION ................................................................................................. 32

3.6 DATA ANALYSIS ....................................................................................................... 38

3.7 QUALITY OF RESEARCH ........................................................................................ 41

4 SUMMARY OF APPENDED PAPERS ..................................................... 45

4.1 PAPER 1 ...................................................................................................................... 45

4.2 PAPER 2 ...................................................................................................................... 48

4.3 PAPER 3 ...................................................................................................................... 50

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TABLE OF CONTENTS ABSTRACT ......................................................................................................... III

SAMMANFATTNING ........................................................................................... V

ACKNOWLEDGEMENTS ................................................................................ VII

PUBLICATIONS ................................................................................................. IX

1 INTRODUCTION ............................................................................................ 1

1.1 RESEARCH BACKGROUND ......................................................................................1

1.2 RESEARCH PROBLEM ...............................................................................................3

1.3 PURPOSE AND RESEARCH QUESTIONS .............................................................5

1.4 SCOPE AND OUTLINE OF THE THESIS ................................................................6

2 FRAME OF REFERENCE ............................................................................ 9

2.1 PRODUCTION SYSTEM DESIGN .............................................................................9

2.1.1 Defining Production Systems ..................................................................................9

2.1.2 Designing Production Systems ............................................................................ 10

2.1.3 Equivocality and Uncertainty in Production System Design ............................ 13

2.1.4 Decisions in Production System Design ............................................................. 14

2.1.5 Decision-Making in Production System Design ................................................ 16

2.2 DES IN PRODUCTION SYSTEM DESIGN ........................................................... 19

2.2.1 Defining DES in Production System Design ...................................................... 19

2.2.2 Challenges of using DES in Production System Design .................................. 21

2.3 SUMMARY OF FRAME OF REFERENCE ............................................................ 23

3 RESEARCH METHOD ................................................................................ 25

3.1 RESEARCH APPROACH ......................................................................................... 25

3.2 RESEARCH PROCESS ............................................................................................ 26

3.3 RESEARCH DESIGN ................................................................................................ 28

3.4 EMPIRICAL BACKGROUND AND DESCRIPTION OF PROJECTS................. 30

3.5 DATA COLLECTION ................................................................................................. 32

3.6 DATA ANALYSIS ....................................................................................................... 38

3.7 QUALITY OF RESEARCH ........................................................................................ 41

4 SUMMARY OF APPENDED PAPERS ..................................................... 45

4.1 PAPER 1 ...................................................................................................................... 45

4.2 PAPER 2 ...................................................................................................................... 48

4.3 PAPER 3 ...................................................................................................................... 50

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4.4 PAPER 4 ...................................................................................................................... 52

4.5 PAPER 5 ...................................................................................................................... 54

4.6 PAPER 6 ...................................................................................................................... 56

5 ANALYSIS ..................................................................................................... 59

5.1 DETERMINING THE CONDITIONS OF USE OF DES IN PRODUCTION SYSTEM DESIGN ...................................................................................................... 59

5.2 CHALLENGES UNDERMINING DES USE IN PRODUCTION SYSTEM DESIGN ....................................................................................................................... 61

5.3 REQUIREMENTS IN PRODUCTION SYSTEM DESIGN FOR DES USE ........ 64

5.4 ACTIVITIES IN PRODUCTION SYSTEM DESIGN FACILITATING THE USE OF DES ........................................................................................................................ 66

6 PROPOSING A FRAMEWORK ................................................................. 69

7 CONCLUSION .............................................................................................. 77

7.1 CONTRIBUTION TO THEORY ................................................................................ 77

7.2 IMPLICATIONS TO PRACTICE ............................................................................... 79

7.3 LIMITATIONS AND FUTURE RESEARCH ............................................................ 80

8 REFERENCES .............................................................................................. 83

Appendix – Reflections on My PhD Journey ............................................. 97

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4.4 PAPER 4 ...................................................................................................................... 52

4.5 PAPER 5 ...................................................................................................................... 54

4.6 PAPER 6 ...................................................................................................................... 56

5 ANALYSIS ..................................................................................................... 59

5.1 DETERMINING THE CONDITIONS OF USE OF DES IN PRODUCTION SYSTEM DESIGN ...................................................................................................... 59

5.2 CHALLENGES UNDERMINING DES USE IN PRODUCTION SYSTEM DESIGN ....................................................................................................................... 61

5.3 REQUIREMENTS IN PRODUCTION SYSTEM DESIGN FOR DES USE ........ 64

5.4 ACTIVITIES IN PRODUCTION SYSTEM DESIGN FACILITATING THE USE OF DES ........................................................................................................................ 66

6 PROPOSING A FRAMEWORK ................................................................. 69

7 CONCLUSION .............................................................................................. 77

7.1 CONTRIBUTION TO THEORY ................................................................................ 77

7.2 IMPLICATIONS TO PRACTICE ............................................................................... 79

7.3 LIMITATIONS AND FUTURE RESEARCH ............................................................ 80

8 REFERENCES .............................................................................................. 83

Appendix – Reflections on My PhD Journey ............................................. 97

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1

1 INTRODUCTION This chapter establishes the importance of the research area, supporting decision-making in production system design, and describes the need for simulation-based tools including Discrete Event Simulation. The research problem is formulated, the objective of the thesis is defined, and four research questions are presented. The chapter concludes with the scope and outline of the thesis.

1.1 RESEARCH BACKGROUND The manufacturing industry faces growing challenges. Aggressive international competitors, shortened product lifecycles, and growing product diversity derail the continued success of prevailing manufacturing practices (Giffi et al. 2016). Amid these challenges, researchers recognize process innovation as a critical determinant for sustained manufacturing competitiveness (Milewski et al. 2015). Process innovations include new production processes or technologies, which are significantly different from existing ones (OECD/Eurostat 2018; Sjödin 2019). These comprise new equipment, material, or reengineering of operational processes focused on how products are manufactured (Piening and Salge 2015). Unlike small-scale adjustments in production, process innovation brings widespread changes to a production system with implications that span across the industry, such as the launch of a next-generation production process or technology (Pisano 1997).

Process innovations are essential for increasing the competitiveness of manufacturing companies (Terjesen and Patel 2017). At an operational level, manufacturing companies can benefit from process innovations by upgrading outdated equipment and production processes, improving the quality, flexibility, and speed of production, and reducing labor, material or time (Krzeminska and Eckert 2015; Piening and Salge 2015). At a strategic level, process innovations create strong competitive barriers that lead to an increased market share, diversify or extend product offerings, and establish new business models (Holweg 2008; Kurkkio et al. 2011; Parida et al. 2017). Manufacturing companies interested in obtaining new production processes or technologies perform activities related to process innovations throughout the lifecycle of a production system (Lager and Frishammar 2010; Sjödin 2019). Yet, researchers underscore the importance of production system design to ensure the actual fulfillment of process innovations at manufacturing companies (Bellgran and Säfsten 2010).

Production system design involves the planning and specification of activities that transform raw materials into finished products (Gino and Pisano 2008; Chryssolouris 2013).The design of production systems is increasingly requiring technical expertise, the ability to satisfy business

1

1 INTRODUCTION This chapter establishes the importance of the research area, supporting decision-making in production system design, and describes the need for simulation-based tools including Discrete Event Simulation. The research problem is formulated, the objective of the thesis is defined, and four research questions are presented. The chapter concludes with the scope and outline of the thesis.

1.1 RESEARCH BACKGROUND The manufacturing industry faces growing challenges. Aggressive international competitors, shortened product lifecycles, and growing product diversity derail the continued success of prevailing manufacturing practices (Giffi et al. 2016). Amid these challenges, researchers recognize process innovation as a critical determinant for sustained manufacturing competitiveness (Milewski et al. 2015). Process innovations include new production processes or technologies, which are significantly different from existing ones (OECD/Eurostat 2018; Sjödin 2019). These comprise new equipment, material, or reengineering of operational processes focused on how products are manufactured (Piening and Salge 2015). Unlike small-scale adjustments in production, process innovation brings widespread changes to a production system with implications that span across the industry, such as the launch of a next-generation production process or technology (Pisano 1997).

Process innovations are essential for increasing the competitiveness of manufacturing companies (Terjesen and Patel 2017). At an operational level, manufacturing companies can benefit from process innovations by upgrading outdated equipment and production processes, improving the quality, flexibility, and speed of production, and reducing labor, material or time (Krzeminska and Eckert 2015; Piening and Salge 2015). At a strategic level, process innovations create strong competitive barriers that lead to an increased market share, diversify or extend product offerings, and establish new business models (Holweg 2008; Kurkkio et al. 2011; Parida et al. 2017). Manufacturing companies interested in obtaining new production processes or technologies perform activities related to process innovations throughout the lifecycle of a production system (Lager and Frishammar 2010; Sjödin 2019). Yet, researchers underscore the importance of production system design to ensure the actual fulfillment of process innovations at manufacturing companies (Bellgran and Säfsten 2010).

Production system design involves the planning and specification of activities that transform raw materials into finished products (Gino and Pisano 2008; Chryssolouris 2013).The design of production systems is increasingly requiring technical expertise, the ability to satisfy business

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2

objectives, and comprehending the interrelation of machines, people, processes, products and technologies for delivering products (Koren et al. 2017). Amid this, manufacturing companies find it difficult to commit to decisions during the design of production systems that will lead to increased competitiveness (Machuca et al. 2011). For example, failing to support decisions during the design of a production system, or basing decisions on erroneous interpretations, leads to technical difficulties, increased costs, and missed market opportunities (Säfsten et al. 2014). Therefore, research emphasizes the importance of supporting decision-making in production system design (Cochran, Foley, et al. 2016).

Acknowledging the need to support decisions in production system design, researchers have considered various simulation techniques including Discrete Event Simulation (DES) (McGinnis and Rose 2017; Mourtzis 2019; Trigueiro de Sousa Junior et al. 2019). DES may be useful to support decision-making in the design of production systems (Negahban and Smith 2014; Kasie et al. 2017). Three reasons justify this claim. First, DES models represent explicitly the interconnectedness of elements (e.g. machines, buffers, resources, or assembly stations) in production systems (Robinson 2008c). This is important because the challenge of designing production systems involves understanding the consequences of changes in one part of the production system and its effects on the subsystems or other processes (Frishammar et al. 2011). Second, DES analyzes dynamically the effect of changes, in the short and long run, of a production system through system capacity, resource utilization, throughput, and other relevant performance metrics (Caggiano and Teti 2018). This is essential for testing and improving process innovations in production system design, and avoiding expensive budget overruns, delays, and quality problems that may cause significant disruption of the production process and possibly result in plant down time (Reichstein and Salter 2006; Jalonen 2011). Third, DES facilitates gathering information and knowledge about new production processes or technologies without disturbing real-life production systems (Mourtzis 2019). This is important because manufacturing companies require significant information and knowledge for adapting process innovations to fit their production systems (Robertson et al. 2012; Sjödin 2019).

DES provides important insights for supporting decision-making in production system design relevant to process innovations (Javahernia and Sunmola 2017). The use of DES is subject to achieving a shared understanding about a production system and acquiring information leading to the support of decision-making (Greasley 2008; Ivers et al. 2016). However, literature shows that the staff responsible for the design of production systems involving process innovations face unfamiliar circumstances, a lack of consensus or understanding (equivocality), and

2

objectives, and comprehending the interrelation of machines, people, processes, products and technologies for delivering products (Koren et al. 2017). Amid this, manufacturing companies find it difficult to commit to decisions during the design of production systems that will lead to increased competitiveness (Machuca et al. 2011). For example, failing to support decisions during the design of a production system, or basing decisions on erroneous interpretations, leads to technical difficulties, increased costs, and missed market opportunities (Säfsten et al. 2014). Therefore, research emphasizes the importance of supporting decision-making in production system design (Cochran, Foley, et al. 2016).

Acknowledging the need to support decisions in production system design, researchers have considered various simulation techniques including Discrete Event Simulation (DES) (McGinnis and Rose 2017; Mourtzis 2019; Trigueiro de Sousa Junior et al. 2019). DES may be useful to support decision-making in the design of production systems (Negahban and Smith 2014; Kasie et al. 2017). Three reasons justify this claim. First, DES models represent explicitly the interconnectedness of elements (e.g. machines, buffers, resources, or assembly stations) in production systems (Robinson 2008c). This is important because the challenge of designing production systems involves understanding the consequences of changes in one part of the production system and its effects on the subsystems or other processes (Frishammar et al. 2011). Second, DES analyzes dynamically the effect of changes, in the short and long run, of a production system through system capacity, resource utilization, throughput, and other relevant performance metrics (Caggiano and Teti 2018). This is essential for testing and improving process innovations in production system design, and avoiding expensive budget overruns, delays, and quality problems that may cause significant disruption of the production process and possibly result in plant down time (Reichstein and Salter 2006; Jalonen 2011). Third, DES facilitates gathering information and knowledge about new production processes or technologies without disturbing real-life production systems (Mourtzis 2019). This is important because manufacturing companies require significant information and knowledge for adapting process innovations to fit their production systems (Robertson et al. 2012; Sjödin 2019).

DES provides important insights for supporting decision-making in production system design relevant to process innovations (Javahernia and Sunmola 2017). The use of DES is subject to achieving a shared understanding about a production system and acquiring information leading to the support of decision-making (Greasley 2008; Ivers et al. 2016). However, literature shows that the staff responsible for the design of production systems involving process innovations face unfamiliar circumstances, a lack of consensus or understanding (equivocality), and

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3

absence of information (uncertainty) (Milewski et al. 2015; Piening and Salge 2015; Parida et al. 2017; Terjesen and Patel 2017; Sjödin 2019).

Research indicates that uncertainty and equivocality lead to greater risks, reduced operational outcomes, strained resources, and uncoordinated activities in production system design (Tatikonda and Montoya-Weiss 2001; Stock and Tatikonda 2008; Olson et al. 2014; Eriksson et al. 2016). Therefore, literature about production system design focuses on reducing the effects of uncertainty (Bellgran and Säfsten 2010)—for example, through the development of methods focused on acquiring information from the design of production systems, converting uncertainty into a calculated risk (Egilmez and Süer 2014; Gaubinger et al. 2015). Despite these efforts, the study of uncertainty and equivocality in process innovations still is critical and limited (Parida et al. 2017; Terjesen and Patel 2017; Sjödin 2019). These problems bring into question the sufficiency of current understanding about the use of DES for supporting decision-making during production system design. To address this dearth of understanding, this thesis responds to the need for supporting decision-making essential for process innovations that renew the production processes of manufacturing companies (Parida et al. 2017).

1.2 RESEARCH PROBLEM This section presents four knowledge gaps related to supporting decision-making under uncertainty and equivocality in the design of production systems involving process innovations, and their relation to DES.

First, the literature provides a diverse range of approaches for supporting decision-making (Elbanna et al. 2013; Thakur and Mangla 2019). These include simulation-based techniques such as DES (Medyna et al. 2012; Negahban and Smith 2014). Recent publications advice against the use of a single decision-making approach in production system design (Dane et al. 2012; Calabretta et al. 2017; Gershwin 2018). Instead, research shows that the choice of a decision-making approach should be grounded in its conditions of use (Julmi 2019). This argument implies that, under certain conditions, a decision-making approach may be superior to others (Dane and Pratt 2007). This claim originates from prior studies indicating the diverse needs of staff during production system design, which may range from standardized operating procedures enabling insight to integrating knowledge or conciliating conflicts (Eldabi et al. 2002; Luoma 2016; Hocaoglu 2018). Unless decision-making approaches are aligned with their conditions of use, the results could be disappointing (Luoma 2016). Therefore, it is vital to know when a decision-making approach is suitable (Zack 2001b; Eling et al. 2014). Current research proposes the increased use of simulation-based techniques for supporting decision-making in the design of production systems including DES (Vieira et al. 2018; Mourtzis 2019; Trigueiro de Sousa Junior et al. 2019). However, it remains unclear

3

absence of information (uncertainty) (Milewski et al. 2015; Piening and Salge 2015; Parida et al. 2017; Terjesen and Patel 2017; Sjödin 2019).

Research indicates that uncertainty and equivocality lead to greater risks, reduced operational outcomes, strained resources, and uncoordinated activities in production system design (Tatikonda and Montoya-Weiss 2001; Stock and Tatikonda 2008; Olson et al. 2014; Eriksson et al. 2016). Therefore, literature about production system design focuses on reducing the effects of uncertainty (Bellgran and Säfsten 2010)—for example, through the development of methods focused on acquiring information from the design of production systems, converting uncertainty into a calculated risk (Egilmez and Süer 2014; Gaubinger et al. 2015). Despite these efforts, the study of uncertainty and equivocality in process innovations still is critical and limited (Parida et al. 2017; Terjesen and Patel 2017; Sjödin 2019). These problems bring into question the sufficiency of current understanding about the use of DES for supporting decision-making during production system design. To address this dearth of understanding, this thesis responds to the need for supporting decision-making essential for process innovations that renew the production processes of manufacturing companies (Parida et al. 2017).

1.2 RESEARCH PROBLEM This section presents four knowledge gaps related to supporting decision-making under uncertainty and equivocality in the design of production systems involving process innovations, and their relation to DES.

First, the literature provides a diverse range of approaches for supporting decision-making (Elbanna et al. 2013; Thakur and Mangla 2019). These include simulation-based techniques such as DES (Medyna et al. 2012; Negahban and Smith 2014). Recent publications advice against the use of a single decision-making approach in production system design (Dane et al. 2012; Calabretta et al. 2017; Gershwin 2018). Instead, research shows that the choice of a decision-making approach should be grounded in its conditions of use (Julmi 2019). This argument implies that, under certain conditions, a decision-making approach may be superior to others (Dane and Pratt 2007). This claim originates from prior studies indicating the diverse needs of staff during production system design, which may range from standardized operating procedures enabling insight to integrating knowledge or conciliating conflicts (Eldabi et al. 2002; Luoma 2016; Hocaoglu 2018). Unless decision-making approaches are aligned with their conditions of use, the results could be disappointing (Luoma 2016). Therefore, it is vital to know when a decision-making approach is suitable (Zack 2001b; Eling et al. 2014). Current research proposes the increased use of simulation-based techniques for supporting decision-making in the design of production systems including DES (Vieira et al. 2018; Mourtzis 2019; Trigueiro de Sousa Junior et al. 2019). However, it remains unclear

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when a decision-making approach should take precedence (Dane et al. 2012; Matzler et al. 2014; Calabretta et al. 2017; Tsioptsias et al. 2018). Therefore, it is essential to determine the conditions that lead to the use of DES for supporting decision-making during production system design.

Second, the use of DES in the design of production systems is subject to numerous challenges, undermining the effective support of decision-making (Greasley 2008; Balci 2012; Xu et al. 2015; Akpan and Shanker 2017; Robinson 2019). Literature argues for an increased understanding of the challenges of DES faced by staff during production system design (Mikalef and Krogstie 2018). Studies describing the challenges of using DES in the design of production systems give precedence to the aspects in the design, development, or deployment of DES models such as real-time simulation-based problem solving, or synchronized factory models (Fowler and Rose 2004; Goodall et al. 2019; Nagahara et al. 2019). Studies assume that the input parameters of the DES models are known from the outset, and the process by which their values are determined can be described (Wynn et al. 2011; Yemane and Colledani 2019). Research shows that the nature of inputs and processes necessary for DES models is known from small-scale modifications in production system design (Trigueiro de Sousa Junior et al. 2019); however, this knowledge is insufficient under equivocality and uncertainty conditions associated with process innovations (Stevens 2014; Mikalef and Krogstie 2018). Failing to address this knowledge gap may result in the dismissal of the use of DES in the design of production systems altogether, and forsaking the test of what-if scenarios or correcting decisions when they are inexpensive (Rossi et al. 2019). In addition, addressing this gap contributes to comprehending the use of simulation in production system design, and the way it enables or obstructs decision-making for increased manufacturing performance (Luoma 2016).

Third, literature on production system design emphasizes the importance of requirements that specify critical aspects defining the possibilities and limitations of a production system (Heisel and Meitzner 2006). Existing literature underscores the need for these requirements during the design of production systems, including structured process, cross functionality, holistic perspective, and long-term view (Rösiö and Säfsten 2013). In addition, prior studies describe the requirements for reducing uncertainty and equivocality inherent to process innovations (Frishammar et al. 2011). These requirements in the use of DES during the design of production systems involving process innovations have not been investigated yet. Disregarding the requirements leading to the reduction of equivocality and uncertainty during the design of production systems can lead to problems. Doing so will increase the risk that managers fail to commit to decisions, or commit to failed choices based on impulsive, uncoordinated, or ineffective responses (Bryan and Farrell 2008; Ramasesh and Browning 2014). This problem is further increased when utilizing DES to support decision-making

4

when a decision-making approach should take precedence (Dane et al. 2012; Matzler et al. 2014; Calabretta et al. 2017; Tsioptsias et al. 2018). Therefore, it is essential to determine the conditions that lead to the use of DES for supporting decision-making during production system design.

Second, the use of DES in the design of production systems is subject to numerous challenges, undermining the effective support of decision-making (Greasley 2008; Balci 2012; Xu et al. 2015; Akpan and Shanker 2017; Robinson 2019). Literature argues for an increased understanding of the challenges of DES faced by staff during production system design (Mikalef and Krogstie 2018). Studies describing the challenges of using DES in the design of production systems give precedence to the aspects in the design, development, or deployment of DES models such as real-time simulation-based problem solving, or synchronized factory models (Fowler and Rose 2004; Goodall et al. 2019; Nagahara et al. 2019). Studies assume that the input parameters of the DES models are known from the outset, and the process by which their values are determined can be described (Wynn et al. 2011; Yemane and Colledani 2019). Research shows that the nature of inputs and processes necessary for DES models is known from small-scale modifications in production system design (Trigueiro de Sousa Junior et al. 2019); however, this knowledge is insufficient under equivocality and uncertainty conditions associated with process innovations (Stevens 2014; Mikalef and Krogstie 2018). Failing to address this knowledge gap may result in the dismissal of the use of DES in the design of production systems altogether, and forsaking the test of what-if scenarios or correcting decisions when they are inexpensive (Rossi et al. 2019). In addition, addressing this gap contributes to comprehending the use of simulation in production system design, and the way it enables or obstructs decision-making for increased manufacturing performance (Luoma 2016).

Third, literature on production system design emphasizes the importance of requirements that specify critical aspects defining the possibilities and limitations of a production system (Heisel and Meitzner 2006). Existing literature underscores the need for these requirements during the design of production systems, including structured process, cross functionality, holistic perspective, and long-term view (Rösiö and Säfsten 2013). In addition, prior studies describe the requirements for reducing uncertainty and equivocality inherent to process innovations (Frishammar et al. 2011). These requirements in the use of DES during the design of production systems involving process innovations have not been investigated yet. Disregarding the requirements leading to the reduction of equivocality and uncertainty during the design of production systems can lead to problems. Doing so will increase the risk that managers fail to commit to decisions, or commit to failed choices based on impulsive, uncoordinated, or ineffective responses (Bryan and Farrell 2008; Ramasesh and Browning 2014). This problem is further increased when utilizing DES to support decision-making

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in the design of production systems. In the absence of clear requirements for uncertainty and equivocality reduction, manufacturing companies experience limited success when utilizing simulation-based tools, including DES, when supporting decision-making (Hayes et al. 2004; Lu and Botha 2006; Kurkkio et al. 2011; Andersen et al. 2017).

Fourth, literature on production system design prescribes the use of simulation-based tools, including DES, for decision-making (Manzini et al. 2018). However, studies on production system design do not detail the activities leading to the use of simulation-based techniques beyond the evaluation of alternatives (Andersen et al. 2017). Empirical evidence shows that loose specification of activities in production system design related to DES account for the low utilization of this tool in decision-making (Melão and Pidd 2003). This situation is problematic for two reasons. First, staff responsible for the design of production systems may fail to gain insight resulting from DES supporting decision-making during production system design. In such instances, DES models may incorrectly represent real-life scenarios, identify bottlenecks or enhance system performance (Ali and Seifoddini 2006). Second, the lack of specifications among activities in production system design and DES show the extent to which manufacturing companies are ill-equipped to adopt simulation-based tools and emerging technologies facilitating increased competitiveness (Kasie et al. 2017).

1.3 PURPOSE AND RESEARCH QUESTIONS Previous studies have contributed to increased understanding of production system design and DES. However, the discussion in the previous subsection underscores the need for further research into supporting decision-making by DES in production system design under the conditions of uncertainty and equivocality associated with process innovations. This knowledge may be essential for implementing the next generation of production processes or technologies at manufacturing companies. Therefore, the purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. This thesis addresses the following research questions:

Research Question 1: How do manufacturing companies specify the conditions of use when utilizing DES to support decision-making during the design of production systems involving process innovations?

Research Question 2: What challenges faced during the design of production systems involving process innovations undermine the use of DES for supporting decision-making?

Research Question 3: What requirements must be considered in the design of production systems involving process innovations when using DES to support decision-making for reducing uncertainty and equivocality?

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in the design of production systems. In the absence of clear requirements for uncertainty and equivocality reduction, manufacturing companies experience limited success when utilizing simulation-based tools, including DES, when supporting decision-making (Hayes et al. 2004; Lu and Botha 2006; Kurkkio et al. 2011; Andersen et al. 2017).

Fourth, literature on production system design prescribes the use of simulation-based tools, including DES, for decision-making (Manzini et al. 2018). However, studies on production system design do not detail the activities leading to the use of simulation-based techniques beyond the evaluation of alternatives (Andersen et al. 2017). Empirical evidence shows that loose specification of activities in production system design related to DES account for the low utilization of this tool in decision-making (Melão and Pidd 2003). This situation is problematic for two reasons. First, staff responsible for the design of production systems may fail to gain insight resulting from DES supporting decision-making during production system design. In such instances, DES models may incorrectly represent real-life scenarios, identify bottlenecks or enhance system performance (Ali and Seifoddini 2006). Second, the lack of specifications among activities in production system design and DES show the extent to which manufacturing companies are ill-equipped to adopt simulation-based tools and emerging technologies facilitating increased competitiveness (Kasie et al. 2017).

1.3 PURPOSE AND RESEARCH QUESTIONS Previous studies have contributed to increased understanding of production system design and DES. However, the discussion in the previous subsection underscores the need for further research into supporting decision-making by DES in production system design under the conditions of uncertainty and equivocality associated with process innovations. This knowledge may be essential for implementing the next generation of production processes or technologies at manufacturing companies. Therefore, the purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. This thesis addresses the following research questions:

Research Question 1: How do manufacturing companies specify the conditions of use when utilizing DES to support decision-making during the design of production systems involving process innovations?

Research Question 2: What challenges faced during the design of production systems involving process innovations undermine the use of DES for supporting decision-making?

Research Question 3: What requirements must be considered in the design of production systems involving process innovations when using DES to support decision-making for reducing uncertainty and equivocality?

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Research Question 4: How should additional activities be included in the design of production systems involving process innovations, to facilitate the use of DES for supporting decision-making?

1.4 SCOPE AND OUTLINE OF THE THESIS Support for various types of decisions are required to design a production system (e.g. products to be designed, or accounting for environmental factors). Therefore, the first step toward accomplishing the purpose of this thesis is scoping the nature of decisions. This thesis focuses on the decisions that determine the internal composition of a production system, namely the decision areas of production system design (Hayes et al. 2004; Machuca et al. 2011; Choudhari et al. 2012). The rationale behind this choice is grounded in prior studies, which posit that these decisions determine the capability of a production system to deliver a certain level of competitiveness (Choudhari et al. 2010). This choice of decisions delimit the research background and the frame of reference of this thesis. In addition, this thesis excludes decisions targeting the external environment of manufacturing companies such as markets.

This thesis interprets the design of a production system as a limited part of production system development (Bellgran and Säfsten 2010). Therefore, it does not consider the activities involving the design of products or the operation of production systems (Pisano 1997). Likewise, the realization, startup, operation, operation refinement, and termination are excluded from the design of production systems (Wiktorsson et al. 2000; Attri and Grover 2012).

This thesis focuses on process innovations during production system design at manufacturing companies (Milewski et al. 2015). Accordingly, the process innovations are limited to new production processes or technologies that are significantly different from existing ones (Pisano 1997; Sjödin 2019). This thesis excludes input materials, task specifications, and work flow mechanisms that have been traditionally perceived as additional constituents of process innovations (Reichstein and Salter 2006).

This thesis adopts organization theory (Galbraith 1973; Daft and Lengel 1986) for understanding the two salient challenges of process innovations affecting decision-making, namely equivocality and uncertainty. Organization theory provides the groundwork necessary for understanding the logic behind the activities, challenges, and requirements for reducing uncertainty and equivocality (Koufteros et al. 2005; Eriksson et al. 2016).

Current advances in DES present an exciting and diverse outlook for production system design (Turner et al. 2016; Vieira et al. 2018; Goodall et al. 2019). The literature on DES considered in this thesis is limited to publications highlighting its use in production system design projects (Negahban and Smith 2014; Law 2015), and the interaction between the

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Research Question 4: How should additional activities be included in the design of production systems involving process innovations, to facilitate the use of DES for supporting decision-making?

1.4 SCOPE AND OUTLINE OF THE THESIS Support for various types of decisions are required to design a production system (e.g. products to be designed, or accounting for environmental factors). Therefore, the first step toward accomplishing the purpose of this thesis is scoping the nature of decisions. This thesis focuses on the decisions that determine the internal composition of a production system, namely the decision areas of production system design (Hayes et al. 2004; Machuca et al. 2011; Choudhari et al. 2012). The rationale behind this choice is grounded in prior studies, which posit that these decisions determine the capability of a production system to deliver a certain level of competitiveness (Choudhari et al. 2010). This choice of decisions delimit the research background and the frame of reference of this thesis. In addition, this thesis excludes decisions targeting the external environment of manufacturing companies such as markets.

This thesis interprets the design of a production system as a limited part of production system development (Bellgran and Säfsten 2010). Therefore, it does not consider the activities involving the design of products or the operation of production systems (Pisano 1997). Likewise, the realization, startup, operation, operation refinement, and termination are excluded from the design of production systems (Wiktorsson et al. 2000; Attri and Grover 2012).

This thesis focuses on process innovations during production system design at manufacturing companies (Milewski et al. 2015). Accordingly, the process innovations are limited to new production processes or technologies that are significantly different from existing ones (Pisano 1997; Sjödin 2019). This thesis excludes input materials, task specifications, and work flow mechanisms that have been traditionally perceived as additional constituents of process innovations (Reichstein and Salter 2006).

This thesis adopts organization theory (Galbraith 1973; Daft and Lengel 1986) for understanding the two salient challenges of process innovations affecting decision-making, namely equivocality and uncertainty. Organization theory provides the groundwork necessary for understanding the logic behind the activities, challenges, and requirements for reducing uncertainty and equivocality (Koufteros et al. 2005; Eriksson et al. 2016).

Current advances in DES present an exciting and diverse outlook for production system design (Turner et al. 2016; Vieira et al. 2018; Goodall et al. 2019). The literature on DES considered in this thesis is limited to publications highlighting its use in production system design projects (Negahban and Smith 2014; Law 2015), and the interaction between the

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staff responsible for the design of a production system and simulation specialists (Robinson 2008b; Chwif et al. 2013). This thesis analyzes the design, development, and deployment of such models (Fowler and Rose 2004). However, this thesis gives precedence to the use of DES for supporting decision-making in production system design.

The empirical data presented herein is based on the findings from one manufacturing company in the Swedish heavy vehicle industry. The manual assembly of customized products characterizes this industrial sector. Therefore, the results of this thesis may not be transferred directly to all manufacturing companies. Industrial managers may find the need to customize the results of this thesis to their respective sectors. Justification for the selection of projects and its implications on the generalization of results are discussed in Chapter 3.

The rest of the chapters are organized as follows. Chapter 2 presents the frame of reference for this thesis. Chapter 3 describes the research method. Chapter 4 presents a summary of the appended papers. Chapter 5 analyzes the findings of this thesis and answers the research questions. Chapter 6 presents a framework for using DES to support decision-making in the design of production systems involving process innovations. Chapter 7 outlines the conclusions of this thesis, its contributions to theory and practice, and the prospects of future research. Finally, the six papers and the PhD journey of the author are appended to this thesis.

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staff responsible for the design of a production system and simulation specialists (Robinson 2008b; Chwif et al. 2013). This thesis analyzes the design, development, and deployment of such models (Fowler and Rose 2004). However, this thesis gives precedence to the use of DES for supporting decision-making in production system design.

The empirical data presented herein is based on the findings from one manufacturing company in the Swedish heavy vehicle industry. The manual assembly of customized products characterizes this industrial sector. Therefore, the results of this thesis may not be transferred directly to all manufacturing companies. Industrial managers may find the need to customize the results of this thesis to their respective sectors. Justification for the selection of projects and its implications on the generalization of results are discussed in Chapter 3.

The rest of the chapters are organized as follows. Chapter 2 presents the frame of reference for this thesis. Chapter 3 describes the research method. Chapter 4 presents a summary of the appended papers. Chapter 5 analyzes the findings of this thesis and answers the research questions. Chapter 6 presents a framework for using DES to support decision-making in the design of production systems involving process innovations. Chapter 7 outlines the conclusions of this thesis, its contributions to theory and practice, and the prospects of future research. Finally, the six papers and the PhD journey of the author are appended to this thesis.

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2 FRAME OF REFERENCE This chapter presents the frame of reference of this thesis. The theoretical considerations of this chapter include two areas relevant to the purpose of this thesis production system design and DES.

2.1 PRODUCTION SYSTEM DESIGN Production system design entails changes that lead to the modification of an existing production system, or developing an entirely new one to accommodate products or processes different from the ones that are currently in existence (Cochran, Foley, et al. 2016). The importance of this activity is further emphasized by the commitment of resources to a production system during its design, its impact beyond the design activity (Wiktorsson 2000; Inman et al. 2013), and by its limited ability to enforce major changes once the production system is operational (Bruch 2012). The first step to understanding production system design is to define production.

2.1.1 Defining Production Systems Colloquially, the terms manufacturing and production are indistinguishable. However, this thesis subscribes to the clear differentiation of the terms argued for in the past (Bellgran and Säfsten 2010; Bruch 2012; Rösiö 2012). This argument contends that manufacturing is hierarchically superior to production. In this understanding, manufacturing is organized for production, but it includes additional functions to achieve that end, such as sales, design, and shipping (CIRP 1990). Production is the act or process of physically creating a product from its material constituents (CIRP 1990).

Modern production requires a number of elements to transform raw materials into finished products. A system perspective is used to describe the reality of today’s manufacturing companies and provide a holistic understanding, as well as a hierarchical classification of all elements involved in production (Bellgran and Säfsten 2010). The holistic consideration permits a comprehensive assessment of all elements within the production, their interrelation, and the dynamics necessary for the consistently successful designing of production systems (Hubka and Eder 1988).

Hierarchically, a system’s perspective bounds the elements within a production system, and sets a limit between the system and its environment. Furthermore, the hierarchical classification of a production system clearly identifies the inputs and outputs necessary to specify how a product should be made (Avila et al. 2018). No consensus exists among scholars on the constituents of a production system. However, past findings have identified human, computer and information, technical, material handling, and building premises as the systems within production (Rösiö 2012).

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2 FRAME OF REFERENCE This chapter presents the frame of reference of this thesis. The theoretical considerations of this chapter include two areas relevant to the purpose of this thesis production system design and DES.

2.1 PRODUCTION SYSTEM DESIGN Production system design entails changes that lead to the modification of an existing production system, or developing an entirely new one to accommodate products or processes different from the ones that are currently in existence (Cochran, Foley, et al. 2016). The importance of this activity is further emphasized by the commitment of resources to a production system during its design, its impact beyond the design activity (Wiktorsson 2000; Inman et al. 2013), and by its limited ability to enforce major changes once the production system is operational (Bruch 2012). The first step to understanding production system design is to define production.

2.1.1 Defining Production Systems Colloquially, the terms manufacturing and production are indistinguishable. However, this thesis subscribes to the clear differentiation of the terms argued for in the past (Bellgran and Säfsten 2010; Bruch 2012; Rösiö 2012). This argument contends that manufacturing is hierarchically superior to production. In this understanding, manufacturing is organized for production, but it includes additional functions to achieve that end, such as sales, design, and shipping (CIRP 1990). Production is the act or process of physically creating a product from its material constituents (CIRP 1990).

Modern production requires a number of elements to transform raw materials into finished products. A system perspective is used to describe the reality of today’s manufacturing companies and provide a holistic understanding, as well as a hierarchical classification of all elements involved in production (Bellgran and Säfsten 2010). The holistic consideration permits a comprehensive assessment of all elements within the production, their interrelation, and the dynamics necessary for the consistently successful designing of production systems (Hubka and Eder 1988).

Hierarchically, a system’s perspective bounds the elements within a production system, and sets a limit between the system and its environment. Furthermore, the hierarchical classification of a production system clearly identifies the inputs and outputs necessary to specify how a product should be made (Avila et al. 2018). No consensus exists among scholars on the constituents of a production system. However, past findings have identified human, computer and information, technical, material handling, and building premises as the systems within production (Rösiö 2012).

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2.1.2 Designing Production Systems Production system design is understood as the conception and planning of the overall set of elements and events constituting a production system, together with the rules for their relationship in time and space (CIRP 1990). In this vein, designing a production system involves integrating the arrangement and operation of machines, tools, material, people, and information into a smoothly functioning whole (Cochran and Dobbs 2001). The design of production systems entails changes or modifications to existing manufacturing capabilities, which may vary from incremental improvements to process innovations (Yamamoto and Bellgran 2013; Bruch and Bellgran 2014). The ability to competently design production systems is a key facilitator of competitiveness, and helps manufacturing companies overcome strong competitors, meet ever-increasing demands for new product variants, and realize rapidly emerging technologies (Koren and Shpitalni 2010).

Arguably, the design of a production system is not solely constituted by the definition of its elements (Alfieri et al. 2013). The literature proposes four requirements when designing a production system—a process for designing a production system, a holistic perspective, a long-term view, and a project organization (Rösiö and Bruch 2018). The first requirement includes the consideration of production system design as a process. This process refers to both the series of actions that lead to its conception and the coordination of work among activities included during production system design (Gu et al. 2001). This process is understood as a tool needed to manage the design activities that lead to successful production systems (Cochran, Hendricks, et al. 2016). The process of designing a production system includes different sequential phases that represent groups of related activities, which range from the identification of a need to satisfying that need (Schuh et al. 2009).

The process of designing a production system is reported in the literature by different authors (Wu 2001; Bellgran and Säfsten 2010; Kurkkio et al. 2011; Sjödin et al. 2011; Bruch and Bellgran 2013; Attri and Grover 2015; Rösiö and Bruch 2018). However, the exact terminology, number of phases, and number of intermediate solution levels vary greatly. Despite their differences, they agree that the design of a production system is based on the use of a stage gate model. In addition, the literature agrees on a sequence of requirement specifications, generating a system solution, implementing the physical manufacturing system, operating the system, and continuously evaluating its ability to fulfill the requirements, leading to an objective (Andersen et al. 2017). The design of production systems is described as an iterative activity involving the analysis of requirements, definition of solutions, evaluation of the results, and modification of the solutions based on the outcome of those results (Bellgran and Säfsten 2010). Recently, studies have synthesized prior findings describing the

10

2.1.2 Designing Production Systems Production system design is understood as the conception and planning of the overall set of elements and events constituting a production system, together with the rules for their relationship in time and space (CIRP 1990). In this vein, designing a production system involves integrating the arrangement and operation of machines, tools, material, people, and information into a smoothly functioning whole (Cochran and Dobbs 2001). The design of production systems entails changes or modifications to existing manufacturing capabilities, which may vary from incremental improvements to process innovations (Yamamoto and Bellgran 2013; Bruch and Bellgran 2014). The ability to competently design production systems is a key facilitator of competitiveness, and helps manufacturing companies overcome strong competitors, meet ever-increasing demands for new product variants, and realize rapidly emerging technologies (Koren and Shpitalni 2010).

Arguably, the design of a production system is not solely constituted by the definition of its elements (Alfieri et al. 2013). The literature proposes four requirements when designing a production system—a process for designing a production system, a holistic perspective, a long-term view, and a project organization (Rösiö and Bruch 2018). The first requirement includes the consideration of production system design as a process. This process refers to both the series of actions that lead to its conception and the coordination of work among activities included during production system design (Gu et al. 2001). This process is understood as a tool needed to manage the design activities that lead to successful production systems (Cochran, Hendricks, et al. 2016). The process of designing a production system includes different sequential phases that represent groups of related activities, which range from the identification of a need to satisfying that need (Schuh et al. 2009).

The process of designing a production system is reported in the literature by different authors (Wu 2001; Bellgran and Säfsten 2010; Kurkkio et al. 2011; Sjödin et al. 2011; Bruch and Bellgran 2013; Attri and Grover 2015; Rösiö and Bruch 2018). However, the exact terminology, number of phases, and number of intermediate solution levels vary greatly. Despite their differences, they agree that the design of a production system is based on the use of a stage gate model. In addition, the literature agrees on a sequence of requirement specifications, generating a system solution, implementing the physical manufacturing system, operating the system, and continuously evaluating its ability to fulfill the requirements, leading to an objective (Andersen et al. 2017). The design of production systems is described as an iterative activity involving the analysis of requirements, definition of solutions, evaluation of the results, and modification of the solutions based on the outcome of those results (Bellgran and Säfsten 2010). Recently, studies have synthesized prior findings describing the

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activities and phases in production system design (Rösiö and Bruch 2018). From this perspective, the design of production systems includes four phases: an informal start, a pre-study, conceptual design, and detailed design. Figure 2.1 presents the aforementioned process of designing a production system, including its phases and activities.

• Observing ongoing activities• Adopting a long-term perspective and identifying possible future change drivers• Exploration and generation of ideas

• Informal discussions

• Formal project initiation• Clarification of the problem and definition of project objectives• Creation of the project directive• Evaluation of the current production system• Assembly and disassembly of products• Anticipation of product design changes and their impact on the production system• Scenario analysis• Analyzing change drivers• Developing requirements specification

Pre-Study

ConceptualDesign

• Creation of preliminary production system concept• Performing Process FMEA• Virtual analysis• Risk analysis• Refinement of the production system concept• Developing concept study report and request for quotation

DetailedDesign

• Selection of the most suitable production system concepts based on the quotes or the equipment suppliers• Equipment supplier selection• Specification of the production system solution• Risk analysis update• Planning for realization/implementation

Phase Activities

Informal Start

Figure 2.1 – Process of designing a production system, including phases and activities, according to Rösiö and Bruch (2018).

The second requirement of designing a production system involves a holistic perspective. The design requires a comprehensive view of all subsystems, their elements, and their relations (Bennett and Forrester 1993). A holistic perspective is necessary to minimize the risk of sub-optimizing or giving precedence to one element over the others during the production system design (Rossi et al. 2019). The holistic consideration permits a

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activities and phases in production system design (Rösiö and Bruch 2018). From this perspective, the design of production systems includes four phases: an informal start, a pre-study, conceptual design, and detailed design. Figure 2.1 presents the aforementioned process of designing a production system, including its phases and activities.

• Observing ongoing activities• Adopting a long-term perspective and identifying possible future change drivers• Exploration and generation of ideas

• Informal discussions

• Formal project initiation• Clarification of the problem and definition of project objectives• Creation of the project directive• Evaluation of the current production system• Assembly and disassembly of products• Anticipation of product design changes and their impact on the production system• Scenario analysis• Analyzing change drivers• Developing requirements specification

Pre-Study

ConceptualDesign

• Creation of preliminary production system concept• Performing Process FMEA• Virtual analysis• Risk analysis• Refinement of the production system concept• Developing concept study report and request for quotation

DetailedDesign

• Selection of the most suitable production system concepts based on the quotes or the equipment suppliers• Equipment supplier selection• Specification of the production system solution• Risk analysis update• Planning for realization/implementation

Phase Activities

Informal Start

Figure 2.1 – Process of designing a production system, including phases and activities, according to Rösiö and Bruch (2018).

The second requirement of designing a production system involves a holistic perspective. The design requires a comprehensive view of all subsystems, their elements, and their relations (Bennett and Forrester 1993). A holistic perspective is necessary to minimize the risk of sub-optimizing or giving precedence to one element over the others during the production system design (Rossi et al. 2019). The holistic consideration permits a

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comprehensive assessment of all elements within production, their interrelation, and the dynamics necessary for the consistently successful design of a production system (Hubka and Eder 1988).

A third requirement relates to applying a long-term view when designing a production system. A long-term view is required to secure economic feasibility for future product portfolios and market situations (Miltenburg 2005). Managers must consider future product variants, families, changes in demand or production location (Andersen et al. 2016). A long-term view is essential in the design of production systems involving process innovations (Milewski et al. 2015). Managers are compelled to meet current production needs and future productivity growth (Chryssolouris 2013).

The fourth requirement includes project organization. It is characterized by a cross functionality with a clear and close cooperation with equipment suppliers and a leader guiding the design of a production system (Sjödin et al. 2011). The organization during the design of a production system frequently involves a core group of individuals (Pisano 1997). This group possesses a broad understanding of and specialized skills in relevant areas, which may ensure the continuity and stability of designing production systems (O’Connor and McDermott 2004).

The literature indicates that manufacturing companies experience severe limitations when designing production systems, despite there being numerous publications in this domain. Ad hoc practices, lack of structured approaches, and the lack of skill to design production systems are frequently reported in the literature (Duda 2000; Rösiö and Bruch 2018). Indeed, trial and error remains the most frequent way of designing production systems at manufacturing companies (European Factories of the Future Research Association 2016). Bellgran and Säfsten (2010) and Cochran et al. (2002) reason that these shortcomings are explicated by a prioritization of product design capabilities over production system design process capabilities, as a means of securing competitiveness.

The hit-and-miss approach toward characterizing the design of production systems poses a severe limitation to the competitiveness of manufacturing companies. Furthermore, manufacturing companies with limited capabilities for designing production systems are finding it increasingly difficult to introduce next-generation production processes and technologies (Pisano and Shih 2009). In these circumstances, the staff responsible for production system design must not fall back on experience. Information and knowledge necessary for decision-making in the design of production systems involving process innovations may not always be accessible when needed (Liu and Hart 2011). In addition, staff with a lack of sufficient information tend to commit to investments that may lead to inferior technologies or production processes (Parida et al. 2017). A growing body of evidence recommends that manufacturing companies set on fulfilling the benefits of process

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comprehensive assessment of all elements within production, their interrelation, and the dynamics necessary for the consistently successful design of a production system (Hubka and Eder 1988).

A third requirement relates to applying a long-term view when designing a production system. A long-term view is required to secure economic feasibility for future product portfolios and market situations (Miltenburg 2005). Managers must consider future product variants, families, changes in demand or production location (Andersen et al. 2016). A long-term view is essential in the design of production systems involving process innovations (Milewski et al. 2015). Managers are compelled to meet current production needs and future productivity growth (Chryssolouris 2013).

The fourth requirement includes project organization. It is characterized by a cross functionality with a clear and close cooperation with equipment suppliers and a leader guiding the design of a production system (Sjödin et al. 2011). The organization during the design of a production system frequently involves a core group of individuals (Pisano 1997). This group possesses a broad understanding of and specialized skills in relevant areas, which may ensure the continuity and stability of designing production systems (O’Connor and McDermott 2004).

The literature indicates that manufacturing companies experience severe limitations when designing production systems, despite there being numerous publications in this domain. Ad hoc practices, lack of structured approaches, and the lack of skill to design production systems are frequently reported in the literature (Duda 2000; Rösiö and Bruch 2018). Indeed, trial and error remains the most frequent way of designing production systems at manufacturing companies (European Factories of the Future Research Association 2016). Bellgran and Säfsten (2010) and Cochran et al. (2002) reason that these shortcomings are explicated by a prioritization of product design capabilities over production system design process capabilities, as a means of securing competitiveness.

The hit-and-miss approach toward characterizing the design of production systems poses a severe limitation to the competitiveness of manufacturing companies. Furthermore, manufacturing companies with limited capabilities for designing production systems are finding it increasingly difficult to introduce next-generation production processes and technologies (Pisano and Shih 2009). In these circumstances, the staff responsible for production system design must not fall back on experience. Information and knowledge necessary for decision-making in the design of production systems involving process innovations may not always be accessible when needed (Liu and Hart 2011). In addition, staff with a lack of sufficient information tend to commit to investments that may lead to inferior technologies or production processes (Parida et al. 2017). A growing body of evidence recommends that manufacturing companies set on fulfilling the benefits of process

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innovations develop the ability to design production systems, and identify and reduce uncertainty and equivocality (Stock and Tatikonda 2008; Kurkkio et al. 2011; Säfsten et al. 2014; Milewski et al. 2015; Piening and Salge 2015; Ahlskog et al. 2017; Sjödin 2019).

2.1.3 Equivocality and Uncertainty in Production System Design

Current understanding of equivocality and uncertainty associated with process innovations in production system design is rooted in organization theory (Galbraith 1973). This theory describes uncertainty and equivocality as two distinct constructs with a common origin affecting decision-making (Frishammar et al. 2011). The concept of uncertainty can be pictured in different ways among actors or team members involved in the design of a production system (Frishammar et al. 2011). This situation has led to the categorization of different types of uncertainty (Gerwin 1987; Milliken 1987; Clarkson and Eckert 2005; Lane and Maxfield 2005b). For example, uncertainty can be expressed in a numerical form, or in terms of subjective probabilities or odds to succeed (Yates 1990). Alternately, uncertainty may also relate to the implicit variation of a system or its environment (Oberkampf et al. 2002).

Literature on process innovations describes uncertainty as the difference between available information and the information necessary to perform a task (Parida et al. 2017). This definition overreaches production system design to express both the probability that assumptions made during design may be incorrect and that facts necessary for design are entirely unknown (Courtney 2003). It is generally agreed upon that uncertainty is inherent to the design of production systems involving process innovations (Kurkkio et al. 2011; Frishammar et al. 2012; Milewski et al. 2015; Piening and Salge 2015). In this context, uncertainty originates from the fact that events in the future do not follow the course of past events or the absence of information about process innovations (Jalonen 2011; Gaubinger et al. 2015).

A concept related to, but different from, uncertainty is that of equivocality. Equivocality concerns the existence of multiple and conflicting interpretations adopted by a group of individuals (Daft and Macintosh 1981; Zack 2001a), and originates from a lack of consensus and understanding (Eriksson et al. 2016). Unlike uncertainty, equivocality is a concept that has received far less attention in academic literature (Sjödin et al. 2016). Equivocality is especially problematic because staff members often possess insufficient information and rely on imprecise propositions to make decisions (Frishammar et al. 2011; Stevens 2014).

Manufacturing companies may reduce uncertainty and equivocality by focusing on information processing activities (Sjödin et al. 2016). Reducing uncertainty is achieved primarily through information acquisition and

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innovations develop the ability to design production systems, and identify and reduce uncertainty and equivocality (Stock and Tatikonda 2008; Kurkkio et al. 2011; Säfsten et al. 2014; Milewski et al. 2015; Piening and Salge 2015; Ahlskog et al. 2017; Sjödin 2019).

2.1.3 Equivocality and Uncertainty in Production System Design

Current understanding of equivocality and uncertainty associated with process innovations in production system design is rooted in organization theory (Galbraith 1973). This theory describes uncertainty and equivocality as two distinct constructs with a common origin affecting decision-making (Frishammar et al. 2011). The concept of uncertainty can be pictured in different ways among actors or team members involved in the design of a production system (Frishammar et al. 2011). This situation has led to the categorization of different types of uncertainty (Gerwin 1987; Milliken 1987; Clarkson and Eckert 2005; Lane and Maxfield 2005b). For example, uncertainty can be expressed in a numerical form, or in terms of subjective probabilities or odds to succeed (Yates 1990). Alternately, uncertainty may also relate to the implicit variation of a system or its environment (Oberkampf et al. 2002).

Literature on process innovations describes uncertainty as the difference between available information and the information necessary to perform a task (Parida et al. 2017). This definition overreaches production system design to express both the probability that assumptions made during design may be incorrect and that facts necessary for design are entirely unknown (Courtney 2003). It is generally agreed upon that uncertainty is inherent to the design of production systems involving process innovations (Kurkkio et al. 2011; Frishammar et al. 2012; Milewski et al. 2015; Piening and Salge 2015). In this context, uncertainty originates from the fact that events in the future do not follow the course of past events or the absence of information about process innovations (Jalonen 2011; Gaubinger et al. 2015).

A concept related to, but different from, uncertainty is that of equivocality. Equivocality concerns the existence of multiple and conflicting interpretations adopted by a group of individuals (Daft and Macintosh 1981; Zack 2001a), and originates from a lack of consensus and understanding (Eriksson et al. 2016). Unlike uncertainty, equivocality is a concept that has received far less attention in academic literature (Sjödin et al. 2016). Equivocality is especially problematic because staff members often possess insufficient information and rely on imprecise propositions to make decisions (Frishammar et al. 2011; Stevens 2014).

Manufacturing companies may reduce uncertainty and equivocality by focusing on information processing activities (Sjödin et al. 2016). Reducing uncertainty is achieved primarily through information acquisition and

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analysis that, if successful, significantly increases the chances of a fruitful project (Ullman 2010). Therefore, staff responsible for the design of a production system must gather and share information, and conduct work analyses to answer questions related to equipment specifications, the design of process flowcharts, and the broader manufacturing environment (Schuman and Brent 2005).

When real-world scenarios are imperfectly understood (i.e. in equivocal conditions), additional information may not actually resolve misunderstandings. Instead, activities that reduce equivocality focus on the exchange of subjective interpretations, consensus, and shared understanding (Daft and Weick 1984; Weick 1995). To reduce equivocality in the design of a production system, staff must make collective decisions regarding the possible solution pathways (McEvily and Marcus 2005), and initiate activities focused on joint problem solving and development of ideas for alternate solutions (Dyer and Nobeoka 2000). While the reduction of uncertainty requires the increase of information for improved decision accuracy, the reduction of equivocality requires guiding future actions (Zack 2007). Table 2.1 outlines the characteristics of uncertainty and equivocality according to Frishammar et al. (2011).

Table 2.1 – Characteristics of uncertainty and equivocality according to Frishammar et al. (2011).

Uncertainty Equivocality

Concept definition

Difference between existing and information necessary to complete a task

Multiple and conflicting interpretations among project participants

Key problem Lack of information Lack of consensus and understanding

Reduction activities

Information acquisition and analysis

Exchange of subjective interpretations and generating consensus

2.1.4 Decisions in Production System Design Manufacturing companies cannot assume that designing production systems involving new production processes or technologies will naturally lead to increased competitiveness (Olson et al. 2014). Designing production systems involving new production process or technology are important for competition if these significantly affects the competitive advantage of a

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analysis that, if successful, significantly increases the chances of a fruitful project (Ullman 2010). Therefore, staff responsible for the design of a production system must gather and share information, and conduct work analyses to answer questions related to equipment specifications, the design of process flowcharts, and the broader manufacturing environment (Schuman and Brent 2005).

When real-world scenarios are imperfectly understood (i.e. in equivocal conditions), additional information may not actually resolve misunderstandings. Instead, activities that reduce equivocality focus on the exchange of subjective interpretations, consensus, and shared understanding (Daft and Weick 1984; Weick 1995). To reduce equivocality in the design of a production system, staff must make collective decisions regarding the possible solution pathways (McEvily and Marcus 2005), and initiate activities focused on joint problem solving and development of ideas for alternate solutions (Dyer and Nobeoka 2000). While the reduction of uncertainty requires the increase of information for improved decision accuracy, the reduction of equivocality requires guiding future actions (Zack 2007). Table 2.1 outlines the characteristics of uncertainty and equivocality according to Frishammar et al. (2011).

Table 2.1 – Characteristics of uncertainty and equivocality according to Frishammar et al. (2011).

Uncertainty Equivocality

Concept definition

Difference between existing and information necessary to complete a task

Multiple and conflicting interpretations among project participants

Key problem Lack of information Lack of consensus and understanding

Reduction activities

Information acquisition and analysis

Exchange of subjective interpretations and generating consensus

2.1.4 Decisions in Production System Design Manufacturing companies cannot assume that designing production systems involving new production processes or technologies will naturally lead to increased competitiveness (Olson et al. 2014). Designing production systems involving new production process or technology are important for competition if these significantly affects the competitive advantage of a

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manufacturing company or its structure (Arana-Solares et al. 2019). Research underscores the importance of decisions made during the design of production systems, as they are essential for achieving increased competitiveness.

Decisions made during the design of production systems are crucial because they determine what a production system will look like and how a production system will operate (Choudhari et al. 2010). In the design of a production system, decisions involve a specific commitment to action and resources across different departments in a manufacturing company (Mintzberg et al. 1976). Careful consideration of decisions during the design of a production system is important because commitment to actions and tasks inevitably leads to compromises and trade-offs (Skinner 1969; Pooya and Faezirad 2017).

Literature argues for the need of a linkage between decisions in production system design and the manufacturing strategy of a manufacturing company (Eisenhardt and Zbaracki 1992). This argument stems from the realization that, while best practices may be shared across industrial sectors, there exists no one-size-fits-all solution when designing production systems (Attri and Grover 2012). Therefore, manufacturing companies must decide over a set of choices determining the purpose of a production system (Miltenburg 2005), and how the interrelation of elements in production will facilitate an increased competitive advantage (Díaz Garrido et al. 2007).

Literature suggests that manufacturing companies require strategic objectives that prioritize a limited number of tasks, to achieve a competitive advantage (Ward et al. 2007). Strategic objectives include clearly defined goals that rank an overall competitiveness that is higher than that provided by local solutions (Petrick and Provance 2005; Machuca et al. 2011). Measures including cost, quality, flexibility, and delivery generally specify the strategic objectives of manufacturing companies (Berry et al. 1991; Olhager 1993; Slack and Lewis 2002).

Previous studies have identified the decisions configuring a production system (Díaz Garrido et al. 2007; Choudhari et al. 2010). Literature refers to these decisions as decision areas (Clark and Wheelwright 1993). Decision areas are essential to production systems because choices made in this domain influence the strategic objectives and competitiveness of a manufacturing company (Miltenburg 2005; Soosay et al. 2016). In addition, manufacturing companies that actively consider how to organize production based on these decisions perform better (Pisano 1997; Reichstein and Salter 2006). Decision areas are grouped into structural and infrastructural categories (Hayes and Wheelwright 1984). Structural decision areas consider the long-term impact of choices and involve major capital investments. Infrastructural decisions are often of a tactical nature, arise from a decision-making process, and demand minor investments.

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manufacturing company or its structure (Arana-Solares et al. 2019). Research underscores the importance of decisions made during the design of production systems, as they are essential for achieving increased competitiveness.

Decisions made during the design of production systems are crucial because they determine what a production system will look like and how a production system will operate (Choudhari et al. 2010). In the design of a production system, decisions involve a specific commitment to action and resources across different departments in a manufacturing company (Mintzberg et al. 1976). Careful consideration of decisions during the design of a production system is important because commitment to actions and tasks inevitably leads to compromises and trade-offs (Skinner 1969; Pooya and Faezirad 2017).

Literature argues for the need of a linkage between decisions in production system design and the manufacturing strategy of a manufacturing company (Eisenhardt and Zbaracki 1992). This argument stems from the realization that, while best practices may be shared across industrial sectors, there exists no one-size-fits-all solution when designing production systems (Attri and Grover 2012). Therefore, manufacturing companies must decide over a set of choices determining the purpose of a production system (Miltenburg 2005), and how the interrelation of elements in production will facilitate an increased competitive advantage (Díaz Garrido et al. 2007).

Literature suggests that manufacturing companies require strategic objectives that prioritize a limited number of tasks, to achieve a competitive advantage (Ward et al. 2007). Strategic objectives include clearly defined goals that rank an overall competitiveness that is higher than that provided by local solutions (Petrick and Provance 2005; Machuca et al. 2011). Measures including cost, quality, flexibility, and delivery generally specify the strategic objectives of manufacturing companies (Berry et al. 1991; Olhager 1993; Slack and Lewis 2002).

Previous studies have identified the decisions configuring a production system (Díaz Garrido et al. 2007; Choudhari et al. 2010). Literature refers to these decisions as decision areas (Clark and Wheelwright 1993). Decision areas are essential to production systems because choices made in this domain influence the strategic objectives and competitiveness of a manufacturing company (Miltenburg 2005; Soosay et al. 2016). In addition, manufacturing companies that actively consider how to organize production based on these decisions perform better (Pisano 1997; Reichstein and Salter 2006). Decision areas are grouped into structural and infrastructural categories (Hayes and Wheelwright 1984). Structural decision areas consider the long-term impact of choices and involve major capital investments. Infrastructural decisions are often of a tactical nature, arise from a decision-making process, and demand minor investments.

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Decisions in the design of production systems must specify how the designed production system adjusts to demands from its external environment, and develop the production system’s internal capabilities (Miller 1992). Managing the fit between environment and internal capabilities has proven a difficult task for manufacturing companies (Ruffini et al. 2000). Research encourages the congruence between decisions involving the external environment of a production system and its internal capabilities (Choudhari et al. 2013). This congruence is essential to harness a successful and competitive production system design.

Literature urges the analysis of the internal and external fits of decisions during production system design (Hayes and Wheelwright 1984). External fit refers how manufacturing companies match their decisions with external settings (Choudhari et al. 2010). Conversely, internal fit requires that decisions made for different parts of a production system be mutually supportive (Gurumurthy and Kodali 2008). When introducing new production technologies and organizational processes, the external and internal fits determine the capabilities of a production system and its relationship with customers and markets (Miller 1992). Research findings argue that alignment between the external and internal fits is necessary to achieve competitiveness (Bates et al. 1995; Sun and Hong 2002). According to da Silveira (2005), decisions involving an external fit comprise product range, customer order size, and level of schedule changes, while choices regarding internal fit relate to production processes, production volumes, and key manufacturing tasks.

The literature contends that specifying the decisions that lead to increased competitiveness is essential (Soosay et al. 2016). Of equal importance, publications contend that staff responsible for the design of production systems must evaluate how the decisions are made (Brynjolfsson and McElheran 2016). To this end, manufacturing companies are presented with diverse decision-making approaches appropriate for designing production systems.

2.1.5 Decision-Making in Production System Design Studies reveal that staff frequently encounter difficulties when identifying decision-making approaches in the design of production systems involving process innovations (Eriksson et al. 2016; Terjesen and Patel 2017). These difficulties originate when staff responsible for implementing process innovations face unfamiliar circumstances (Jalonen 2011; Stevens 2014; Gaubinger et al. 2015). In particular, the staff must deal with a lack of consensus and understanding (equivocality), and absence of rules or processes facilitating the information analysis (analyzability) (Frishammar et al. 2011; Kurkkio et al. 2011; Milewski et al. 2015; Piening and Salge 2015). Amid this, the literature offers distinct decision-making approaches relevant to the design of production systems (Jonassen 2012).

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Decisions in the design of production systems must specify how the designed production system adjusts to demands from its external environment, and develop the production system’s internal capabilities (Miller 1992). Managing the fit between environment and internal capabilities has proven a difficult task for manufacturing companies (Ruffini et al. 2000). Research encourages the congruence between decisions involving the external environment of a production system and its internal capabilities (Choudhari et al. 2013). This congruence is essential to harness a successful and competitive production system design.

Literature urges the analysis of the internal and external fits of decisions during production system design (Hayes and Wheelwright 1984). External fit refers how manufacturing companies match their decisions with external settings (Choudhari et al. 2010). Conversely, internal fit requires that decisions made for different parts of a production system be mutually supportive (Gurumurthy and Kodali 2008). When introducing new production technologies and organizational processes, the external and internal fits determine the capabilities of a production system and its relationship with customers and markets (Miller 1992). Research findings argue that alignment between the external and internal fits is necessary to achieve competitiveness (Bates et al. 1995; Sun and Hong 2002). According to da Silveira (2005), decisions involving an external fit comprise product range, customer order size, and level of schedule changes, while choices regarding internal fit relate to production processes, production volumes, and key manufacturing tasks.

The literature contends that specifying the decisions that lead to increased competitiveness is essential (Soosay et al. 2016). Of equal importance, publications contend that staff responsible for the design of production systems must evaluate how the decisions are made (Brynjolfsson and McElheran 2016). To this end, manufacturing companies are presented with diverse decision-making approaches appropriate for designing production systems.

2.1.5 Decision-Making in Production System Design Studies reveal that staff frequently encounter difficulties when identifying decision-making approaches in the design of production systems involving process innovations (Eriksson et al. 2016; Terjesen and Patel 2017). These difficulties originate when staff responsible for implementing process innovations face unfamiliar circumstances (Jalonen 2011; Stevens 2014; Gaubinger et al. 2015). In particular, the staff must deal with a lack of consensus and understanding (equivocality), and absence of rules or processes facilitating the information analysis (analyzability) (Frishammar et al. 2011; Kurkkio et al. 2011; Milewski et al. 2015; Piening and Salge 2015). Amid this, the literature offers distinct decision-making approaches relevant to the design of production systems (Jonassen 2012).

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A first approach involves normative decision-making, which is characterized by a logical step-by-step analysis involving quantitative assessment (Mintzberg et al. 1976). This approach includes process information that is clear, objective, and well-defined (Dean and Sharfman 1996). Normative decision-making is described as a slow and conscious process where information is logically decomposed and sequentially recombined to generate an output (Swamidass 1991; Papadakis et al. 1998; Jonassen 2012). Arguments against the use of normative decision-making include empirical studies that show that individuals are incapable of an objectively rational approach and possess limited capacities to process information (Simon 1997).Despite its alleged drawbacks, normative decision-making continues to be used by organizations and has frequently led to good outcomes (Metters et al. 2008; Klein et al. 2019). Currently, diverse research efforts point to the increased use of normative decision-making approaches for the analysis of large amounts of ill-defined data in production systems (Jain et al. 2017; Gürdür et al. 2019).

A second approach includes intuitive decision-making (Bendoly et al. 2006; Loch and Wu 2007; Gino and Pisano 2008; Elbanna et al. 2013; White 2016). Intuitive decision-making involves affectively charged judgements that arise through rapid, non-conscious, and holistic association of information (Dane and Pratt 2007). Intuitive decision-making is associated with having a strong hunch or feeling of knowing what is going to occur, and can be advantageous when professionals are confronted with time pressure and possess experience in a field (Bennett III 1998; Khatri and Ng 2000; Dane and Pratt 2007; Hodgkinson et al. 2009; Gore and Sadler-Smith 2011; Elbanna et al. 2013). Intuitive decision-making is not without criticism. For example, decision-makers who rely on intuition, in an uncertain environment, are more likely to forgo systematic analysis, and that increases the probability of unfavorable and unforeseen outcomes (Elbanna et al. 2013). In addition, decision-makers have a hard time explaining the reasons for a particular choice when adopting an intuitive decision-making approach (Dane et al. 2012).

A third alternative includes the use of mixed decision-making approaches (Tamura 2005; Hämäläinen et al. 2013). A mixed-method approach considers both quantitative data and intuition (Saaty 2008; Hämäläinen et al. 2013; Kubler et al. 2016; Thakur and Mangla 2019). Mixed decision-making approaches provide solutions to problems involving conflicting objectives or criteria affected by uncertainty (Kahraman et al. 2015). The main strength of this approach, as opposed to the purely intuitive method, lies in reducing personal bias and allowing the comparison of dissimilar alternatives, while integrating quantitative analysis (Saaty 2008). Literature presents various alternatives in relation to mixed decision-making approaches (Mardani et al. 2015), yet they have the common objective of

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A first approach involves normative decision-making, which is characterized by a logical step-by-step analysis involving quantitative assessment (Mintzberg et al. 1976). This approach includes process information that is clear, objective, and well-defined (Dean and Sharfman 1996). Normative decision-making is described as a slow and conscious process where information is logically decomposed and sequentially recombined to generate an output (Swamidass 1991; Papadakis et al. 1998; Jonassen 2012). Arguments against the use of normative decision-making include empirical studies that show that individuals are incapable of an objectively rational approach and possess limited capacities to process information (Simon 1997).Despite its alleged drawbacks, normative decision-making continues to be used by organizations and has frequently led to good outcomes (Metters et al. 2008; Klein et al. 2019). Currently, diverse research efforts point to the increased use of normative decision-making approaches for the analysis of large amounts of ill-defined data in production systems (Jain et al. 2017; Gürdür et al. 2019).

A second approach includes intuitive decision-making (Bendoly et al. 2006; Loch and Wu 2007; Gino and Pisano 2008; Elbanna et al. 2013; White 2016). Intuitive decision-making involves affectively charged judgements that arise through rapid, non-conscious, and holistic association of information (Dane and Pratt 2007). Intuitive decision-making is associated with having a strong hunch or feeling of knowing what is going to occur, and can be advantageous when professionals are confronted with time pressure and possess experience in a field (Bennett III 1998; Khatri and Ng 2000; Dane and Pratt 2007; Hodgkinson et al. 2009; Gore and Sadler-Smith 2011; Elbanna et al. 2013). Intuitive decision-making is not without criticism. For example, decision-makers who rely on intuition, in an uncertain environment, are more likely to forgo systematic analysis, and that increases the probability of unfavorable and unforeseen outcomes (Elbanna et al. 2013). In addition, decision-makers have a hard time explaining the reasons for a particular choice when adopting an intuitive decision-making approach (Dane et al. 2012).

A third alternative includes the use of mixed decision-making approaches (Tamura 2005; Hämäläinen et al. 2013). A mixed-method approach considers both quantitative data and intuition (Saaty 2008; Hämäläinen et al. 2013; Kubler et al. 2016; Thakur and Mangla 2019). Mixed decision-making approaches provide solutions to problems involving conflicting objectives or criteria affected by uncertainty (Kahraman et al. 2015). The main strength of this approach, as opposed to the purely intuitive method, lies in reducing personal bias and allowing the comparison of dissimilar alternatives, while integrating quantitative analysis (Saaty 2008). Literature presents various alternatives in relation to mixed decision-making approaches (Mardani et al. 2015), yet they have the common objective of

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helping deal with the evaluation, selection, and prioritization of problems by imposing a disciplined methodology (Kubler et al. 2016).

Literature focused on the design of production systems has relied on normative decision-making approaches (Bellgran and Säfsten 2010). The main concern of this stream of literature has been the identification of appropriate principles, processes, and structures needed to optimize the functioning of the production system (Gino and Pisano 2008). However, other research works show that normative decision-making approaches may not be adequate in all situations involving production system design (Gino and Pisano 2008; White 2016; Loch 2017).

Determining the rationale for selecting one decision-making approach remains a debated subject (Dane et al. 2012; Matzler et al. 2014; Luoma 2016; Calabretta et al. 2017). Recent publications proposed that the structuredness of decisions may constitute the main criteria for determining a decision-making approach (Julmi 2019). Decision structuredness refers to the degree of consensus and understanding (equivocality), and the existence of rules or processes facilitating the analysis of information (analyzability) in a decision (Dane and Pratt 2007). Grounded in organization theory, recent studies have proposed that the structuredness of decisions may provide an indication for understanding the correspondence between the choice of a decision-making approach and its conditions of use (Julmi 2019).

In the past, decisions have been classified along a continuum according to their structure (Shapiro and Spence 1997). This argument sustains that a decision may range from well- to ill-structured. Well-structured decisions include intellective tasks with a definite objective criterion of success within the definitions, rules, operations, and relationships of a particular conceptual system (Dane and Pratt 2007). A well-structured decision involves rules or procedures and unequivocal interpretations developed over time (March and Simon 1993; Luoma 2016). Therefore, it is argued that well-structured decisions relate to low equivocality and high analyzability, and that normative decision-making is appropriate because of its structured rules and computable information.

Ill-structured decisions involve judgmental tasks where there are no objective criteria, or demonstrable solutions (Dane and Pratt 2007). Ill-structured decisions originate from novel situations that do not include widely accepted rules that may help determine the degree to which a decision is correct or biased (Cyert and March 1992; Jacobides 2007; Luoma 2016). Consequently, ill-structured decisions correspond to high equivocality and low analyzability. Staff facing ill-structured decisions adopt intuitive decision-making, because intuition does not rely on rules to cope with a problem, but on integrating information holistically into coherent patterns (Dane and Pratt 2007).

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helping deal with the evaluation, selection, and prioritization of problems by imposing a disciplined methodology (Kubler et al. 2016).

Literature focused on the design of production systems has relied on normative decision-making approaches (Bellgran and Säfsten 2010). The main concern of this stream of literature has been the identification of appropriate principles, processes, and structures needed to optimize the functioning of the production system (Gino and Pisano 2008). However, other research works show that normative decision-making approaches may not be adequate in all situations involving production system design (Gino and Pisano 2008; White 2016; Loch 2017).

Determining the rationale for selecting one decision-making approach remains a debated subject (Dane et al. 2012; Matzler et al. 2014; Luoma 2016; Calabretta et al. 2017). Recent publications proposed that the structuredness of decisions may constitute the main criteria for determining a decision-making approach (Julmi 2019). Decision structuredness refers to the degree of consensus and understanding (equivocality), and the existence of rules or processes facilitating the analysis of information (analyzability) in a decision (Dane and Pratt 2007). Grounded in organization theory, recent studies have proposed that the structuredness of decisions may provide an indication for understanding the correspondence between the choice of a decision-making approach and its conditions of use (Julmi 2019).

In the past, decisions have been classified along a continuum according to their structure (Shapiro and Spence 1997). This argument sustains that a decision may range from well- to ill-structured. Well-structured decisions include intellective tasks with a definite objective criterion of success within the definitions, rules, operations, and relationships of a particular conceptual system (Dane and Pratt 2007). A well-structured decision involves rules or procedures and unequivocal interpretations developed over time (March and Simon 1993; Luoma 2016). Therefore, it is argued that well-structured decisions relate to low equivocality and high analyzability, and that normative decision-making is appropriate because of its structured rules and computable information.

Ill-structured decisions involve judgmental tasks where there are no objective criteria, or demonstrable solutions (Dane and Pratt 2007). Ill-structured decisions originate from novel situations that do not include widely accepted rules that may help determine the degree to which a decision is correct or biased (Cyert and March 1992; Jacobides 2007; Luoma 2016). Consequently, ill-structured decisions correspond to high equivocality and low analyzability. Staff facing ill-structured decisions adopt intuitive decision-making, because intuition does not rely on rules to cope with a problem, but on integrating information holistically into coherent patterns (Dane and Pratt 2007).

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2.2 DES IN PRODUCTION SYSTEM DESIGN

2.2.1 Defining DES in Production System Design As mentioned earlier, DES is one of the most common techniques for supporting decision-making in production system design (Jahangirian et al. 2010; Fordyce et al. 2015). DES is a highly flexible tool that enables evaluating different alternatives of system configurations and operating strategies to support decision-making in the design of production systems (Negahban and Smith 2014). The reason DES is a valuable tool for manufacturing companies today is because of the existence of useful software tools for creating simulation models, providing their inputs, executing them, and analyzing their outputs (McGinnis and Rose 2017).

DES is described as experimenting with a simplified imitation of a production system as it progresses through time, and it is commonly utilized for modeling queuing systems (Banks et al. 2000). These systems consist of entities processed through a series of stages, with queues forming between each stage if there is insufficient processing capacity (Robinson 2008c). The description above is deceptively simple, yet the use of DES for supporting decision-making in the design of production system is extensive and well-reported in literature (Negahban and Smith 2014).

Literature proposes that a critical factor for the successful use of DES is shared responsibility and active participation between DES specialists and stakeholders in an organization (Jahangirian et al. 2015). In the design of production systems, the former are charged with the technical aspects of DES models, while the latter possess the know-how about a real-life system and frequently own the problem that a DES model will solve (Balci 2012; Monks et al. 2016).

DES supports decision-making in the design of production systems, based on models that describe the causal relationships between control and performance variables (Law 2015). This relationship is said to be normative, and must fulfill a multi-stepped process (Banks et al. 2000; Robinson 2008c; Law 2015). There exist numerous representations of this process (Martin et al. 2018), yet the description of DES as a process according to Fowler and Rose (2004) is frequently referred to in literature.

Fowler and Rose (2004) propose that understanding DES as a process should involve the design, development, and deployment phases. These phase include a series of requirements necessary for DES to support decision-making in the design of production systems (Fowler and Rose 2004; Law 2015; Martin et al. 2018). The design phase consists of the development of a specification that includes project customers, goals and deliverables, the definition of a project team and plan for model development, and conceptual models. The development phase consists of

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2.2 DES IN PRODUCTION SYSTEM DESIGN

2.2.1 Defining DES in Production System Design As mentioned earlier, DES is one of the most common techniques for supporting decision-making in production system design (Jahangirian et al. 2010; Fordyce et al. 2015). DES is a highly flexible tool that enables evaluating different alternatives of system configurations and operating strategies to support decision-making in the design of production systems (Negahban and Smith 2014). The reason DES is a valuable tool for manufacturing companies today is because of the existence of useful software tools for creating simulation models, providing their inputs, executing them, and analyzing their outputs (McGinnis and Rose 2017).

DES is described as experimenting with a simplified imitation of a production system as it progresses through time, and it is commonly utilized for modeling queuing systems (Banks et al. 2000). These systems consist of entities processed through a series of stages, with queues forming between each stage if there is insufficient processing capacity (Robinson 2008c). The description above is deceptively simple, yet the use of DES for supporting decision-making in the design of production system is extensive and well-reported in literature (Negahban and Smith 2014).

Literature proposes that a critical factor for the successful use of DES is shared responsibility and active participation between DES specialists and stakeholders in an organization (Jahangirian et al. 2015). In the design of production systems, the former are charged with the technical aspects of DES models, while the latter possess the know-how about a real-life system and frequently own the problem that a DES model will solve (Balci 2012; Monks et al. 2016).

DES supports decision-making in the design of production systems, based on models that describe the causal relationships between control and performance variables (Law 2015). This relationship is said to be normative, and must fulfill a multi-stepped process (Banks et al. 2000; Robinson 2008c; Law 2015). There exist numerous representations of this process (Martin et al. 2018), yet the description of DES as a process according to Fowler and Rose (2004) is frequently referred to in literature.

Fowler and Rose (2004) propose that understanding DES as a process should involve the design, development, and deployment phases. These phase include a series of requirements necessary for DES to support decision-making in the design of production systems (Fowler and Rose 2004; Law 2015; Martin et al. 2018). The design phase consists of the development of a specification that includes project customers, goals and deliverables, the definition of a project team and plan for model development, and conceptual models. The development phase consists of

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the establishment of a number of model development options, including non-simulation alternatives, selecting the identifying entities and activities, as well as determining and collecting input data and the methods to be used for model verification and validation. Finally, the deployment phase consists of activities including experimentation, simulation output analysis, data maintenance and model integration, as well as presentation and use of simulation results for the intended customer base of the project.

An important activity for facilitating the use of DES when supporting decision-making is to determine the objective of the DES model (Heilala et al. 2010). Literature indicates that the objectives of DES models are either quantitative or qualitative (Eldabi et al. 2002). Quantitative DES models are predominant when supporting decision-making in the design of production systems (Negahban and Smith 2014). This type of models include optimization, evaluation, or selection of parameters by statistical means in the design of production systems (Kleijnen et al. 2011; Trigueiro de Sousa Junior et al. 2019).

Qualitative DES models focus on distilling the meaning, identifying the key components, understanding the problems, or generating insights (Gogi et al. 2016; Hocaoglu 2018). Qualitative DES models provide meaning to abstract outputs, enhance the understanding of a problem, and define what questions to ask or what variables to look for (Bayer et al. 2014). The process followed when developing qualitative models begins with identifying a problem of interest that is ill-defined, an iterative cycle of information exchange between the model and the individuals, which lead to the development of increased understanding (Eldabi et al. 2002).

Examples of qualitative DES models include visual representations of production systems focused on understanding the problem and decision-making (Akpan and Shanker 2017; Lindskog et al. 2017). This type of model proves a powerful tool for communication and is useful in two types of situations (Robinson 2008c). First, it is possible that an idea has already been proven, but has been deemed necessary to build a simulation model in order to convince senior managers and colleagues of its validity (Robinson et al. 2012). Second, many simulation studies have been performed in the light of differing opinions. Having opposing parties discuss around a simulation model can be a powerful means of sharing concerns, testing ideas, and reaching a consensus (Akpan and Brooks 2014).

An essential activity that combines the design of a production system and the use of DES for supporting decision-making, is the conceptual model (Chwif et al. 2013). Literature describes the conceptual model as the point of origin for the abstraction of a real or proposed systems and the place of return for the continuous iterations of a model to reach an increased level of understanding (Robinson 2008a). Conceptual models may assist in the use of any number of DES models when supporting decision-making, and

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the establishment of a number of model development options, including non-simulation alternatives, selecting the identifying entities and activities, as well as determining and collecting input data and the methods to be used for model verification and validation. Finally, the deployment phase consists of activities including experimentation, simulation output analysis, data maintenance and model integration, as well as presentation and use of simulation results for the intended customer base of the project.

An important activity for facilitating the use of DES when supporting decision-making is to determine the objective of the DES model (Heilala et al. 2010). Literature indicates that the objectives of DES models are either quantitative or qualitative (Eldabi et al. 2002). Quantitative DES models are predominant when supporting decision-making in the design of production systems (Negahban and Smith 2014). This type of models include optimization, evaluation, or selection of parameters by statistical means in the design of production systems (Kleijnen et al. 2011; Trigueiro de Sousa Junior et al. 2019).

Qualitative DES models focus on distilling the meaning, identifying the key components, understanding the problems, or generating insights (Gogi et al. 2016; Hocaoglu 2018). Qualitative DES models provide meaning to abstract outputs, enhance the understanding of a problem, and define what questions to ask or what variables to look for (Bayer et al. 2014). The process followed when developing qualitative models begins with identifying a problem of interest that is ill-defined, an iterative cycle of information exchange between the model and the individuals, which lead to the development of increased understanding (Eldabi et al. 2002).

Examples of qualitative DES models include visual representations of production systems focused on understanding the problem and decision-making (Akpan and Shanker 2017; Lindskog et al. 2017). This type of model proves a powerful tool for communication and is useful in two types of situations (Robinson 2008c). First, it is possible that an idea has already been proven, but has been deemed necessary to build a simulation model in order to convince senior managers and colleagues of its validity (Robinson et al. 2012). Second, many simulation studies have been performed in the light of differing opinions. Having opposing parties discuss around a simulation model can be a powerful means of sharing concerns, testing ideas, and reaching a consensus (Akpan and Brooks 2014).

An essential activity that combines the design of a production system and the use of DES for supporting decision-making, is the conceptual model (Chwif et al. 2013). Literature describes the conceptual model as the point of origin for the abstraction of a real or proposed systems and the place of return for the continuous iterations of a model to reach an increased level of understanding (Robinson 2008a). Conceptual models may assist in the use of any number of DES models when supporting decision-making, and

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facilitate the dialogue between production system designers and simulation specialists (Balci and Ormsby 2007). Table 2.2 outlines the activities of conceptual models and their descriptions.

Table 2.2 – Description of activities of a conceptual model, according to (Robinson 2008a).

Activities Description Understand the problem situation

Definition of the need to improve a problem situation

Determine the modeling objectives

Purpose expressed in terms of achievement, performance and constraints

Identify the model output

Model responses

Identify the model input Experimental factors. Data changes to achieve objective

Determine model scope Model boundaries in terms of entities, activities, queues and resources

Establish level of detail Specification of entities, activities, queues and resources

Assumptions Beliefs about real world being modeled Simplifications Reduction to basic essentials that enable rapid

model development

2.2.2 Challenges of using DES in Production System Design

Considerable research has been conducted in the effort to pinpoint the challenges of the use of DES for supporting decision-making in production system design (Fowler and Rose 2004; Wang and Chatwin 2005; Heilala et al. 2010; Fischbein and Yellig 2011; Mönch et al. 2011). Table 2.3 classifies these challenges based on Fowler and Rose (2004)’s work. This table includes three additional challenges not present in Fowler and Rose (2004): the development of simulation and production system knowledge, software diversity and lack of standardization (Wang and Chatwin 2005), and trade-off considerations and non-intuitive decisions (Mönch et al. 2011).

Studies describing the challenges of DES are predominantly centered on difficulties of developing DES models. These difficulties stem from the fact that a single DES model is often incapable of supporting all decision-making needs and performance of the model will be brought into question during the design of a production system (Fowler and Rose 2004). Additionally, the

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facilitate the dialogue between production system designers and simulation specialists (Balci and Ormsby 2007). Table 2.2 outlines the activities of conceptual models and their descriptions.

Table 2.2 – Description of activities of a conceptual model, according to (Robinson 2008a).

Activities Description Understand the problem situation

Definition of the need to improve a problem situation

Determine the modeling objectives

Purpose expressed in terms of achievement, performance and constraints

Identify the model output

Model responses

Identify the model input Experimental factors. Data changes to achieve objective

Determine model scope Model boundaries in terms of entities, activities, queues and resources

Establish level of detail Specification of entities, activities, queues and resources

Assumptions Beliefs about real world being modeled Simplifications Reduction to basic essentials that enable rapid

model development

2.2.2 Challenges of using DES in Production System Design

Considerable research has been conducted in the effort to pinpoint the challenges of the use of DES for supporting decision-making in production system design (Fowler and Rose 2004; Wang and Chatwin 2005; Heilala et al. 2010; Fischbein and Yellig 2011; Mönch et al. 2011). Table 2.3 classifies these challenges based on Fowler and Rose (2004)’s work. This table includes three additional challenges not present in Fowler and Rose (2004): the development of simulation and production system knowledge, software diversity and lack of standardization (Wang and Chatwin 2005), and trade-off considerations and non-intuitive decisions (Mönch et al. 2011).

Studies describing the challenges of DES are predominantly centered on difficulties of developing DES models. These difficulties stem from the fact that a single DES model is often incapable of supporting all decision-making needs and performance of the model will be brought into question during the design of a production system (Fowler and Rose 2004). Additionally, the

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difficulty of DES modeling arises from the need to build trustworthy DES models for factory management, in order to commit to decisions during production system design (Fischbein and Yellig 2011). This situation underscores the criticality of issues arising in determining which elements of a production system to represent in order to successfully implement the DES model for supporting decision-making (Wang and Chatwin 2005).

Table 2.3 – Challenges of DES for supporting decision-making in production system design adapted from Fowler and Rose (2004), Wang and Chatwin (2005), and Mönch et al. (2011).

DES model phase

Challenge

Design - Decision support restricted by question-specific model formulation. What is the problem and how is it addressed? - Representation of production system dynamics and complexity - Validity of model detail level - Simplification of production system complexity and factor interdependence - Non-uniform abstraction level for model simplification - Modeling combinatorial explosion of options in a production process - Incomplete and conflicting production system knowledge - Development of simulation and production system knowledge - Software diversity and lack of standardization

Development - Model verification and validation - Model development time - Input data collection and analysis - Input data availability and quality

Deployment - Model interoperability and information sharing between models - Industry acceptance of DES - Communication of results for effective decision making - Simulation model maintenance - Consideration of trade-offs and non-intuitive decisions - High cost and low reusability of models

Relevant literature based on specific manufacturing contexts provides different perspectives on the challenges of using DES for supporting decision-making in the design of production systems. Fowler and Rose (2004) addressed the challenges of using DES for supporting decision-making in current and future production systems. Heilala et al. (2010)

22

difficulty of DES modeling arises from the need to build trustworthy DES models for factory management, in order to commit to decisions during production system design (Fischbein and Yellig 2011). This situation underscores the criticality of issues arising in determining which elements of a production system to represent in order to successfully implement the DES model for supporting decision-making (Wang and Chatwin 2005).

Table 2.3 – Challenges of DES for supporting decision-making in production system design adapted from Fowler and Rose (2004), Wang and Chatwin (2005), and Mönch et al. (2011).

DES model phase

Challenge

Design - Decision support restricted by question-specific model formulation. What is the problem and how is it addressed? - Representation of production system dynamics and complexity - Validity of model detail level - Simplification of production system complexity and factor interdependence - Non-uniform abstraction level for model simplification - Modeling combinatorial explosion of options in a production process - Incomplete and conflicting production system knowledge - Development of simulation and production system knowledge - Software diversity and lack of standardization

Development - Model verification and validation - Model development time - Input data collection and analysis - Input data availability and quality

Deployment - Model interoperability and information sharing between models - Industry acceptance of DES - Communication of results for effective decision making - Simulation model maintenance - Consideration of trade-offs and non-intuitive decisions - High cost and low reusability of models

Relevant literature based on specific manufacturing contexts provides different perspectives on the challenges of using DES for supporting decision-making in the design of production systems. Fowler and Rose (2004) addressed the challenges of using DES for supporting decision-making in current and future production systems. Heilala et al. (2010)

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analyzed the support of a production system design and operations decisions based on DES use. Wang and Chatwin (2005) and Fischbein and Yellig (2011) described the key issues in successful DES model implementation and the difficulties in supporting decision-making for production systems evaluation, respectively. Finally, Mönch et al. (2011) analyzed a production system grounded in logistics.

2.3 SUMMARY OF FRAME OF REFERENCE The frame of reference outlined the current understanding about supporting decision-making in production system design through DES and recommends a deeper understanding. In particular, a deeper understanding is required in situations involving equivocality and uncertainty associated with process innovations. Two streams of literature inform this understanding—publications focused on production system design and studies involving the design, development, and deployment of DES models in production system design.

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analyzed the support of a production system design and operations decisions based on DES use. Wang and Chatwin (2005) and Fischbein and Yellig (2011) described the key issues in successful DES model implementation and the difficulties in supporting decision-making for production systems evaluation, respectively. Finally, Mönch et al. (2011) analyzed a production system grounded in logistics.

2.3 SUMMARY OF FRAME OF REFERENCE The frame of reference outlined the current understanding about supporting decision-making in production system design through DES and recommends a deeper understanding. In particular, a deeper understanding is required in situations involving equivocality and uncertainty associated with process innovations. Two streams of literature inform this understanding—publications focused on production system design and studies involving the design, development, and deployment of DES models in production system design.

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3 RESEARCH METHOD This chapter describes the research approach, process, design, data collection and analysis, and concludes with a discussion on the research quality.

3.1 RESEARCH APPROACH The purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. Accordingly, the research questions of this thesis address the need for supporting decision-making essential for renewing the production processes of manufacturing companies. Amid this, I considered important selecting a research approach that facilitated what Coughlan et al. (2016) and Sandberg et al. (2011) refer to as an outcome relevant to practice and of scholarly rigor. This led to adopting a collaborative research approach.

The literature describes a collaborative research approach as that which originates from a non-readily solvable problem with significant implications to an organization (Shani et al., 2007). During my PhD study, I enacted a collaborative research approach by interacting with a manufacturing company in the heavy vehicle industry. This allowed me to collaborate with their European Operations Manufacturing Research Department.

The purpose of the collaboration was to increase organizational knowledge, and thus support decision-making through DES. The collaboration was limited to the design of production systems involving process innovations (e.g. multi-product production systems). In this collaboration, the manufacturing company faced new challenges for which there existed no managerial solutions or scholarly answers, and undertook three responsibilities. First, the commitment of staff and managers responsible for the design of production systems for participating in a research study. Second, allocating time for periods of reflection and critique associated with the research outcomes of my thesis. Third, facilitating the dissemination of the findings at the manufacturing company. This situation provided me with the opportunity to contribute to the solution of a challenging practical task at the manufacturing company, and generate useful knowledge to support decision-making during the design of production systems involving process innovations.

In collaborative research, practitioners and researchers interact, with the purpose of improving the understanding of a problem, yet follow a clear division of responsibilities (Ellström 2008). My responsibilities were to act as a bridge between academia and the manufacturing company, translating information between the worlds of research and practice (Coghlan and Brannick 2014). In this role, I interacted with the staff responsible for designing production systems on a daily basis between 2014 and 2016.

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3 RESEARCH METHOD This chapter describes the research approach, process, design, data collection and analysis, and concludes with a discussion on the research quality.

3.1 RESEARCH APPROACH The purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. Accordingly, the research questions of this thesis address the need for supporting decision-making essential for renewing the production processes of manufacturing companies. Amid this, I considered important selecting a research approach that facilitated what Coughlan et al. (2016) and Sandberg et al. (2011) refer to as an outcome relevant to practice and of scholarly rigor. This led to adopting a collaborative research approach.

The literature describes a collaborative research approach as that which originates from a non-readily solvable problem with significant implications to an organization (Shani et al., 2007). During my PhD study, I enacted a collaborative research approach by interacting with a manufacturing company in the heavy vehicle industry. This allowed me to collaborate with their European Operations Manufacturing Research Department.

The purpose of the collaboration was to increase organizational knowledge, and thus support decision-making through DES. The collaboration was limited to the design of production systems involving process innovations (e.g. multi-product production systems). In this collaboration, the manufacturing company faced new challenges for which there existed no managerial solutions or scholarly answers, and undertook three responsibilities. First, the commitment of staff and managers responsible for the design of production systems for participating in a research study. Second, allocating time for periods of reflection and critique associated with the research outcomes of my thesis. Third, facilitating the dissemination of the findings at the manufacturing company. This situation provided me with the opportunity to contribute to the solution of a challenging practical task at the manufacturing company, and generate useful knowledge to support decision-making during the design of production systems involving process innovations.

In collaborative research, practitioners and researchers interact, with the purpose of improving the understanding of a problem, yet follow a clear division of responsibilities (Ellström 2008). My responsibilities were to act as a bridge between academia and the manufacturing company, translating information between the worlds of research and practice (Coghlan and Brannick 2014). In this role, I interacted with the staff responsible for designing production systems on a daily basis between 2014 and 2016.

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These activities were of two types—the first type includes initiating meetings, discussions, and workshops involving staff of different levels of seniority (operators, production engineers, logistic developers, consultants, and production managers, and site managers). This type focused on creating awareness about DES; and, the second type involved designing, developing, and presenting the results of the DES models during the design of production systems. These activities provided a venue for joint discussions with staff from the manufacturing company, to understand the phenomenon of inquiry (Cirella et al. 2012).

3.2 RESEARCH PROCESS Literature in the field of Operations Management describes the research process as a systematic gathering of evidence supported by clear steps, which may help interpret the evidence to draw conclusions of scientific value (Gill and Johnson 2002). The research process includes interaction between the existing academic literature and manufacturing practice, where opportunities may arise and dissolve at any time (Karlsson 2010). Consequently, there is a need to follow a structured series of steps, which may help avoid disorganization and failed research results.

This thesis adopts in its research process the steps suggested by Bryman (2013), which include identifying areas of study, selecting a research topic, deciding on a research approach, formulating a research design, collecting and analyzing data, and presenting the findings. These steps were, however, not followed linearly. Instead, the process of research was highly iterative, and was characterized by reflection and questioning emerging from the empirical findings. In addition, a concurrent review of academic literature was used to support the research process. Reviewing academic literature was fundamental to identifying the area of study, selecting a research topic, and supporting the overall development of this thesis. The review of academic literature pursued three objectives, following the suggestions of Karlsson (2010)—establishing the authority and legitimacy of papers appended in this thesis; clarifying the possible contributions of the thesis; and, constraining the objective of the thesis to a feasible scope.

To meet the purpose of this thesis and answer the research questions, this thesis is based on a collection of six papers. Each of these papers contains an independent research question and purpose. The decision to collect these papers was motivated by the intention of bringing together distinct findings in a coherent manner. Therefore, the research questions presented herein address intermediate concepts. The relation between each paper, its research questions, and purpose to this thesis is outlined in Table 3.1.

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These activities were of two types—the first type includes initiating meetings, discussions, and workshops involving staff of different levels of seniority (operators, production engineers, logistic developers, consultants, and production managers, and site managers). This type focused on creating awareness about DES; and, the second type involved designing, developing, and presenting the results of the DES models during the design of production systems. These activities provided a venue for joint discussions with staff from the manufacturing company, to understand the phenomenon of inquiry (Cirella et al. 2012).

3.2 RESEARCH PROCESS Literature in the field of Operations Management describes the research process as a systematic gathering of evidence supported by clear steps, which may help interpret the evidence to draw conclusions of scientific value (Gill and Johnson 2002). The research process includes interaction between the existing academic literature and manufacturing practice, where opportunities may arise and dissolve at any time (Karlsson 2010). Consequently, there is a need to follow a structured series of steps, which may help avoid disorganization and failed research results.

This thesis adopts in its research process the steps suggested by Bryman (2013), which include identifying areas of study, selecting a research topic, deciding on a research approach, formulating a research design, collecting and analyzing data, and presenting the findings. These steps were, however, not followed linearly. Instead, the process of research was highly iterative, and was characterized by reflection and questioning emerging from the empirical findings. In addition, a concurrent review of academic literature was used to support the research process. Reviewing academic literature was fundamental to identifying the area of study, selecting a research topic, and supporting the overall development of this thesis. The review of academic literature pursued three objectives, following the suggestions of Karlsson (2010)—establishing the authority and legitimacy of papers appended in this thesis; clarifying the possible contributions of the thesis; and, constraining the objective of the thesis to a feasible scope.

To meet the purpose of this thesis and answer the research questions, this thesis is based on a collection of six papers. Each of these papers contains an independent research question and purpose. The decision to collect these papers was motivated by the intention of bringing together distinct findings in a coherent manner. Therefore, the research questions presented herein address intermediate concepts. The relation between each paper, its research questions, and purpose to this thesis is outlined in Table 3.1.

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Table 3.1 – Relation between thesis research questions, collected papers, titles, and their objectives.

Thesis research question Title of paper Paper Objective Research Question 1 – How do manufacturing companies specify the conditions of use when utilizing DES to support decision-making during the design of production systems involving process innovations?

Paper 1 – Decision-Making Approaches in Process Innovations: An Explorative Case Study

To explore the selection of decision-making approaches at manufacturing companies when implementing process innovations

Research Question 2 – What challenges faced during the design of production systems involving process innovations undermine the use of DES for supporting decision-making?

Paper 2 – Approaching the reduction of uncertainty in production system design through DES Paper 3 – Challenges of DES in the early stages of production system design

To examine the uncertainties reduced through use of DES during the design of a production system, when significant changes are introduced at the manufacturing company To analyze the challenges of applying DES in the early stages of production system design

Research Question 3 – What requirements must be considered in the design of production systems involving process innovations when using DES to support decision-making for reducing uncertainty and equivocality?

Paper 4 - What guides information consensus? Approaching the reduction of equivocality in process innovations

To analyze information consensus and the reduction of equivocality in process innovations

Research Question 4 – How should additional activities be included in the design of production systems involving process innovations, to facilitate the use of DES for supporting decision-making?

Paper 5 – Simulation-based optimization for facility layout design in conditions of high uncertainty Paper 6 - Simulation in the Production System Design Process of Assembly Systems

To understand the conceptual modeling activities of simulation-based optimization for facility layout design under high uncertainty To explore how DES can be used in the production system design process

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Table 3.1 – Relation between thesis research questions, collected papers, titles, and their objectives.

Thesis research question Title of paper Paper Objective Research Question 1 – How do manufacturing companies specify the conditions of use when utilizing DES to support decision-making during the design of production systems involving process innovations?

Paper 1 – Decision-Making Approaches in Process Innovations: An Explorative Case Study

To explore the selection of decision-making approaches at manufacturing companies when implementing process innovations

Research Question 2 – What challenges faced during the design of production systems involving process innovations undermine the use of DES for supporting decision-making?

Paper 2 – Approaching the reduction of uncertainty in production system design through DES Paper 3 – Challenges of DES in the early stages of production system design

To examine the uncertainties reduced through use of DES during the design of a production system, when significant changes are introduced at the manufacturing company To analyze the challenges of applying DES in the early stages of production system design

Research Question 3 – What requirements must be considered in the design of production systems involving process innovations when using DES to support decision-making for reducing uncertainty and equivocality?

Paper 4 - What guides information consensus? Approaching the reduction of equivocality in process innovations

To analyze information consensus and the reduction of equivocality in process innovations

Research Question 4 – How should additional activities be included in the design of production systems involving process innovations, to facilitate the use of DES for supporting decision-making?

Paper 5 – Simulation-based optimization for facility layout design in conditions of high uncertainty Paper 6 - Simulation in the Production System Design Process of Assembly Systems

To understand the conceptual modeling activities of simulation-based optimization for facility layout design under high uncertainty To explore how DES can be used in the production system design process

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3.3 RESEARCH DESIGN This thesis uses a case study method to meet its purpose. An important consideration for undertaking a case study method is to clearly articulate the rationale for the research (Barratt et al. 2011). Four reasons justify this choice.

First, the use of case study method is justified when applying an existing general theory to a context that is not known well enough to obtain sufficiently detailed findings (Ketokivi and Choi 2014). Undeniably, there are other studies in the literature focusing on production system design (Kurkkio et al. 2011; Attri and Grover 2012; Rösiö and Bruch 2018). These studies prescribe the phases, activities, and responsibilities of staff designing a production system. However, to the best of the author’s knowledge, they do not differentiate what needs to be done under high uncertainty and equivocality that are inherent to process innovation. This is problematic, as empirical evidence shows that manufacturing companies struggle to support decision-making when designing production systems in this context (Kurkkio et al. 2011). Amid this, the case study method operates as a disciplined iteration between existing literature and empirical data (Eisenhardt and Graebner 2007). Specifically, this thesis uses a case study method to analyze the conditions of use, requirements, challenges, and activities for using DES to support decision-making in the design of production systems.

Second, supporting decision-making in the design of production systems requires placing events in chronological order and determining the causal links over time (Mintzberg et al. 1976). Yin (2013) argues that the case study method can be effectively employed to conduct time-series analyses and understand events as they unfold. This provided the opportunity to develop a rich explanation for the complex patterns and compare that explanation to the actual outcomes.

Third, strengthening the relevance of findings of this thesis to practice. Fisher (2007) posits that the nature of a case study method, which so naturally integrates conversations with individuals in the context of a study, is extremely useful to bridge academic relevance with practical scenarios. This aspect is important to address the current need for rigor and relevance in the field of Operations Management (Boer et al. 2015). In addition, this aspect is also necessary to comply with the collaborative research approach of this thesis (Coughlan et al. 2016), and an increased need for findings that lead to improved support of decision-making in production system design.

Fourth, the choice of case study method rests on its appropriateness to conduct research in a novel context, in which there is a need to identify themes and patterns, rather than confirm them (Meredith et al. 1989). To this extent, this thesis sheds light on the conditions of use, requirements,

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3.3 RESEARCH DESIGN This thesis uses a case study method to meet its purpose. An important consideration for undertaking a case study method is to clearly articulate the rationale for the research (Barratt et al. 2011). Four reasons justify this choice.

First, the use of case study method is justified when applying an existing general theory to a context that is not known well enough to obtain sufficiently detailed findings (Ketokivi and Choi 2014). Undeniably, there are other studies in the literature focusing on production system design (Kurkkio et al. 2011; Attri and Grover 2012; Rösiö and Bruch 2018). These studies prescribe the phases, activities, and responsibilities of staff designing a production system. However, to the best of the author’s knowledge, they do not differentiate what needs to be done under high uncertainty and equivocality that are inherent to process innovation. This is problematic, as empirical evidence shows that manufacturing companies struggle to support decision-making when designing production systems in this context (Kurkkio et al. 2011). Amid this, the case study method operates as a disciplined iteration between existing literature and empirical data (Eisenhardt and Graebner 2007). Specifically, this thesis uses a case study method to analyze the conditions of use, requirements, challenges, and activities for using DES to support decision-making in the design of production systems.

Second, supporting decision-making in the design of production systems requires placing events in chronological order and determining the causal links over time (Mintzberg et al. 1976). Yin (2013) argues that the case study method can be effectively employed to conduct time-series analyses and understand events as they unfold. This provided the opportunity to develop a rich explanation for the complex patterns and compare that explanation to the actual outcomes.

Third, strengthening the relevance of findings of this thesis to practice. Fisher (2007) posits that the nature of a case study method, which so naturally integrates conversations with individuals in the context of a study, is extremely useful to bridge academic relevance with practical scenarios. This aspect is important to address the current need for rigor and relevance in the field of Operations Management (Boer et al. 2015). In addition, this aspect is also necessary to comply with the collaborative research approach of this thesis (Coughlan et al. 2016), and an increased need for findings that lead to improved support of decision-making in production system design.

Fourth, the choice of case study method rests on its appropriateness to conduct research in a novel context, in which there is a need to identify themes and patterns, rather than confirm them (Meredith et al. 1989). To this extent, this thesis sheds light on the conditions of use, requirements,

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challenges, and activities to use DES for supporting decision-making in the design of production systems. This required continuous comparison between empirical data and literature. As a result, research questions and their answers emerged during the research (Eisenhardt 1989).

The case study method requires specifying how existing literature and empirical data contribute to a sense of generality and situational groundedness of the findings (Mantere and Ketokivi 2013; Ketokivi and Choi 2014). This thesis uses an existing theory to formulate its initial concepts that help interpret the empirical findings (Voss et al. 2002). In particular, this thesis is underpinned by the phases and activities described in the studies on production system design. Based on previous studies, the conditions of use, challenges, activities, and requirements stated in the research questions and in the frame of reference are described. However, empirical data provides evidence for the idiosyncrasies experienced during the design of a production system, which may either support or run counter to current understanding.

The purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. Defining the purpose is important as it guides in the selection of the unit of analysis, which is critical to understanding how empirical data relates to a broader body of knowledge (Dubé and Paré 2003). In addition, a clearly stated unit of analysis can help identify the applicable literature and clarify the phenomenon under investigation (Barratt et al. 2011). In this thesis, the unit of analysis is a production system design project. It is an appropriate unit of analysis because changes to a production system take place during its design, and this activity is commonly carried out in projects, while minor changes to a production system form part of everyday activities (Bellgran and Säfsten 2010).

Bounding decision support to a temporal context, such as that of a project, is necessary. Doing so will provide guidance in identifying where critical decisions are made. However, it is equally important to understand how those decisions are made. To this end, this thesis adopts two embedded units of analysis. The first unit involves decisions within the production system design project. In this context, decisions are understood as a specific commitment to actions and resources (Mintzberg et al. 1976; Frishammar 2003). The second unit comprehends the use of DES in the design of production systems (Melão and Pidd 2003). Selecting these embedded units of analysis is important because it helps understand the conditions of use, requirements, challenges, and activities leading to commitments during production system design through DES.

This study draws empirical data from one global manufacturing company. While the case study method at a single manufacturing company offers limited generalizability (Ahlskog et al. 2017), it allows for an in-depth

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challenges, and activities to use DES for supporting decision-making in the design of production systems. This required continuous comparison between empirical data and literature. As a result, research questions and their answers emerged during the research (Eisenhardt 1989).

The case study method requires specifying how existing literature and empirical data contribute to a sense of generality and situational groundedness of the findings (Mantere and Ketokivi 2013; Ketokivi and Choi 2014). This thesis uses an existing theory to formulate its initial concepts that help interpret the empirical findings (Voss et al. 2002). In particular, this thesis is underpinned by the phases and activities described in the studies on production system design. Based on previous studies, the conditions of use, challenges, activities, and requirements stated in the research questions and in the frame of reference are described. However, empirical data provides evidence for the idiosyncrasies experienced during the design of a production system, which may either support or run counter to current understanding.

The purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. Defining the purpose is important as it guides in the selection of the unit of analysis, which is critical to understanding how empirical data relates to a broader body of knowledge (Dubé and Paré 2003). In addition, a clearly stated unit of analysis can help identify the applicable literature and clarify the phenomenon under investigation (Barratt et al. 2011). In this thesis, the unit of analysis is a production system design project. It is an appropriate unit of analysis because changes to a production system take place during its design, and this activity is commonly carried out in projects, while minor changes to a production system form part of everyday activities (Bellgran and Säfsten 2010).

Bounding decision support to a temporal context, such as that of a project, is necessary. Doing so will provide guidance in identifying where critical decisions are made. However, it is equally important to understand how those decisions are made. To this end, this thesis adopts two embedded units of analysis. The first unit involves decisions within the production system design project. In this context, decisions are understood as a specific commitment to actions and resources (Mintzberg et al. 1976; Frishammar 2003). The second unit comprehends the use of DES in the design of production systems (Melão and Pidd 2003). Selecting these embedded units of analysis is important because it helps understand the conditions of use, requirements, challenges, and activities leading to commitments during production system design through DES.

This study draws empirical data from one global manufacturing company. While the case study method at a single manufacturing company offers limited generalizability (Ahlskog et al. 2017), it allows for an in-depth

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exploration of decision-making at the manufacturing companies (Kihlander and Ritzén 2012). The choice of manufacturing company was based on theoretical sampling, with the aim of exploiting opportunities to explore a significant phenomenon under rare or extreme circumstances relevant to the purpose of this thesis (Eisenhardt and Graebner 2007; Yin 2013). For the selection of a manufacturing company, this thesis focused on four factors associated with the competent design of production systems involving process innovations: large-sized manufacturing companies of high capital intensity, established processes for developing production systems, continual design of new products, and an emphasis on the increasing flexibility of production systems (Pisano 1997; Martinez-Ros 1999; Cabagnols and Le Bas 2002).

The selection of projects gave precedence to information-rich projects illuminating the issues under study (Patton, 1990). First, I screened projects by questioning staff knowledgeable about the details of each potential project (Yin, 2013). Then, I selected projects including experienced global production system designers, to improve opportunities to interact with professionally trained managers, reducing language barriers, and simplifying interaction during qualitative interviewing. Finally, I gave precedence to projects that explicitly required DES during the design of a production system. These projects could either allow me to contribute with the development of DES models, or follow staff assigned to this activity.

3.4 EMPIRICAL BACKGROUND AND DESCRIPTION OF PROJECTS

The empirical data is gathered from three projects (A, B, and C) from the heavy vehicle industry. The heavy vehicle industry is characterized by a high degree of product customization and specialized product families targeting specific markets. Manufacturers in this segment consider a wide offering of products to be a key competitive advantage. Traditionally, this classification of product families serves not only to distinguish a manufacturing company’s product offering but also to segment the assembly of products. Therefore, production systems in this industry are distinguished by assembly lines that specialize in a single product family and have little commonality other than being from the same manufacturing facility.

At the start of this thesis, the manufacturing company deployed a series of process innovations across the world, including projects A, B, and C. These projects were aimed at introducing a new production technology and organizational processes that would enable the assembly of more than one product family in an assembly line, namely multi-product production systems. These projects were considered process innovations because of the broad scope of changes they bring that affect the production system, the novelty of its approach when compared to traditional production, the lack of

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exploration of decision-making at the manufacturing companies (Kihlander and Ritzén 2012). The choice of manufacturing company was based on theoretical sampling, with the aim of exploiting opportunities to explore a significant phenomenon under rare or extreme circumstances relevant to the purpose of this thesis (Eisenhardt and Graebner 2007; Yin 2013). For the selection of a manufacturing company, this thesis focused on four factors associated with the competent design of production systems involving process innovations: large-sized manufacturing companies of high capital intensity, established processes for developing production systems, continual design of new products, and an emphasis on the increasing flexibility of production systems (Pisano 1997; Martinez-Ros 1999; Cabagnols and Le Bas 2002).

The selection of projects gave precedence to information-rich projects illuminating the issues under study (Patton, 1990). First, I screened projects by questioning staff knowledgeable about the details of each potential project (Yin, 2013). Then, I selected projects including experienced global production system designers, to improve opportunities to interact with professionally trained managers, reducing language barriers, and simplifying interaction during qualitative interviewing. Finally, I gave precedence to projects that explicitly required DES during the design of a production system. These projects could either allow me to contribute with the development of DES models, or follow staff assigned to this activity.

3.4 EMPIRICAL BACKGROUND AND DESCRIPTION OF PROJECTS

The empirical data is gathered from three projects (A, B, and C) from the heavy vehicle industry. The heavy vehicle industry is characterized by a high degree of product customization and specialized product families targeting specific markets. Manufacturers in this segment consider a wide offering of products to be a key competitive advantage. Traditionally, this classification of product families serves not only to distinguish a manufacturing company’s product offering but also to segment the assembly of products. Therefore, production systems in this industry are distinguished by assembly lines that specialize in a single product family and have little commonality other than being from the same manufacturing facility.

At the start of this thesis, the manufacturing company deployed a series of process innovations across the world, including projects A, B, and C. These projects were aimed at introducing a new production technology and organizational processes that would enable the assembly of more than one product family in an assembly line, namely multi-product production systems. These projects were considered process innovations because of the broad scope of changes they bring that affect the production system, the novelty of its approach when compared to traditional production, the lack of

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experience necessary for its conception, and the large number of unknown factors in its development. These process innovations included interchangeable production processes and technologies for the production of different product families. The manufacturing company predicted that introducing these process innovations would lead to shortening lead time to customers, reducing its manufacturing footprint, and providing a common product architecture.

Project A enabled the assembly of five heavy vehicle product families and their 500 variants in one assembly line. Data collection was initiated between January 2014 and January 2015. Project B involved the assembly of five product families and 190 variants of heavy vehicle powertrains in one assembly line. Data collection for this project was initiated between December 2014 and January 2016. Project C involved the assembly of three product families of heavy vehicle cabins and their 600 variants in one assembly line. The data collection for this project took place between January 2016 and June 2016. Figure 3.1 presents a timeline of the papers appended in this thesis, and the three projects providing the empirical data for these papers. Table 3.2 describes projects A, B, and C and outlines the relation between these projects and the papers appended to this thesis.

November 20192015 20172016 2018

Start of PhD

October 2014

Licentiatepresentation

PhD Disputation

Project BProject A

Paper 1 Paper 2 Paper 3

Paper 4

Paper 5

Paper 6Project C

Figure 3.1 – Timeline of my PhD studies, including papers appended in this thesis and the projects that provided empirical data to these papers.

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experience necessary for its conception, and the large number of unknown factors in its development. These process innovations included interchangeable production processes and technologies for the production of different product families. The manufacturing company predicted that introducing these process innovations would lead to shortening lead time to customers, reducing its manufacturing footprint, and providing a common product architecture.

Project A enabled the assembly of five heavy vehicle product families and their 500 variants in one assembly line. Data collection was initiated between January 2014 and January 2015. Project B involved the assembly of five product families and 190 variants of heavy vehicle powertrains in one assembly line. Data collection for this project was initiated between December 2014 and January 2016. Project C involved the assembly of three product families of heavy vehicle cabins and their 600 variants in one assembly line. The data collection for this project took place between January 2016 and June 2016. Figure 3.1 presents a timeline of the papers appended in this thesis, and the three projects providing the empirical data for these papers. Table 3.2 describes projects A, B, and C and outlines the relation between these projects and the papers appended to this thesis.

November 20192015 20172016 2018

Start of PhD

October 2014

Licentiatepresentation

PhD Disputation

Project BProject A

Paper 1 Paper 2 Paper 3

Paper 4

Paper 5

Paper 6Project C

Figure 3.1 – Timeline of my PhD studies, including papers appended in this thesis and the projects that provided empirical data to these papers.

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Table 3.2 – Description of projects A, B, and C and their relation to the appended papers.

Project A Project B Project C Location North America Latin America Sweden Products Heavy vehicles Powertrains Cabins Variants 500 190 600 Organization size 900 780 350 Staff responsible for process innovation

Project manager Project manager Project manager Manufacturing research manager

Production managers

Manufacturing research manager

Site manager Site manager Site manager Production engineers from six sites

Production engineers from five sites

Production engineers from one site

Product design managers

Product design engineers

Production engineer manager

Logistics developers



R&D staff R&D staff R&D staff Consultants Consultants Consultants


Presented in papers

1, 3, 5, 6 1, 3 2, 3, 4, 5

3.5 DATA COLLECTION The underlying principle in the data collection in the case study method is the use of a combination of different sources to study a phenomenon (Karlsson 2010). Therefore, to increase the reliability of findings, data collection included various sources (Eisenhardt 1989). Data in this thesis was collected from four sources: field notes, semi-structured interviews, company documents, and DES models.

The primary sources of data included field notes taken during my participation in projects A, B, and C (Yin et al. 2017). Field notes comprehended written and typed notes of events in the natural context of occurrence among actors who would normally participate in the interaction (Adler and Adler 1994). Field notes focused on recording observations, impression, hunches, and questions raised during weekly project meetings, workshops, prototype demonstrations, floor shop visits, DES related meetings, and informal conversations with staff responsible for the production system design. In this thesis, field notes were developed while becoming part of the observed process of study with the intent of uncovering

32

Table 3.2 – Description of projects A, B, and C and their relation to the appended papers.

Project A Project B Project C Location North America Latin America Sweden Products Heavy vehicles Powertrains Cabins Variants 500 190 600 Organization size 900 780 350 Staff responsible for process innovation

Project manager Project manager Project manager Manufacturing research manager

Production managers

Manufacturing research manager

Site manager Site manager Site manager Production engineers from six sites

Production engineers from five sites

Production engineers from one site

Product design managers


Production engineer manager




R&D staff R&D staff R&D staff Consultants Consultants Consultants


Presented in papers

1, 3, 5, 6 1, 3 2, 3, 4, 5

3.5 DATA COLLECTION The underlying principle in the data collection in the case study method is the use of a combination of different sources to study a phenomenon (Karlsson 2010). Therefore, to increase the reliability of findings, data collection included various sources (Eisenhardt 1989). Data in this thesis was collected from four sources: field notes, semi-structured interviews, company documents, and DES models.

The primary sources of data included field notes taken during my participation in projects A, B, and C (Yin et al. 2017). Field notes comprehended written and typed notes of events in the natural context of occurrence among actors who would normally participate in the interaction (Adler and Adler 1994). Field notes focused on recording observations, impression, hunches, and questions raised during weekly project meetings, workshops, prototype demonstrations, floor shop visits, DES related meetings, and informal conversations with staff responsible for the production system design. In this thesis, field notes were developed while becoming part of the observed process of study with the intent of uncovering

50

33

the sequence and nature of events accurately and increasing the richness of data of the case study (Flynn et al. 1990; Leonard-Barton 1990).

At the same time, data collection in a collaborative research approach aims to meet both organizational and academic criteria (Canterino et al. 2016). Data collection occurred collaboratively. In this thesis, the staff collected data for making decisions during the design a production system. I collected data related to how staff supported decision-making during production system design, and built DES models. The staff members and I exchanged data regularly. For me, the purpose of this exchange was to gain better understanding of each project, and, for the manufacturing company, to have periods of reflection related to the requirements, challenges, and activities while supporting decision-making in the design of a production system.

Data—from company documents including presentations and executive summaries detailing the goals, activities, time plans, best practices, findings, risk analysis, and the design, development, and deployment of DES models—was collected with consent of the respective managers of each project. In addition, I drafted and exchanged systematic and detailed field notes with project participants. For the purpose of exchanging collected data with the manufacturing company, field notes were summarized in the form of Powerpoint presentations. These were then drafted and sent to the project participants as soon as possible, after meetings, interviews, and workshops.

To facilitate collaborative data collection, I was allowed to present an update on my practical contribution to each project, including: DES models or a summary of theoretical findings that gave an academic perspective. The subject of this presentation was mutually agreed upon by the managers and me, to not only acquire data for practical purposes but also this thesis. This was an important source for validating the preliminary findings and gaining additional input from the staff participating in the projects necessary in a collaborative inquiry process (Börjesson et al. 2006).

Semi-structured interview focused on supporting decision-making during production system design. These interviews touched upon the problems and solutions related to uncertainty and equivocality, and the use of DES in production system design. The interviews began with a description of the interviewee’s background and their role. Later, the interviewees were asked to describe the design of a production system, and narrate the major decisions from the start of each project until its conclusion. Then, the interviewees were asked to explain how they supported decision-making under uncertainty and equivocality, and its challenges, enablers, and implications with respect to the outcome of each project. They were directed to narrate the use of DES during production system design, followed by a closure and a period of open-ended questions for clarifying any issues arising during the interview. All interviews were recorded, transcribed, and

33

the sequence and nature of events accurately and increasing the richness of data of the case study (Flynn et al. 1990; Leonard-Barton 1990).

At the same time, data collection in a collaborative research approach aims to meet both organizational and academic criteria (Canterino et al. 2016). Data collection occurred collaboratively. In this thesis, the staff collected data for making decisions during the design a production system. I collected data related to how staff supported decision-making during production system design, and built DES models. The staff members and I exchanged data regularly. For me, the purpose of this exchange was to gain better understanding of each project, and, for the manufacturing company, to have periods of reflection related to the requirements, challenges, and activities while supporting decision-making in the design of a production system.

Data—from company documents including presentations and executive summaries detailing the goals, activities, time plans, best practices, findings, risk analysis, and the design, development, and deployment of DES models—was collected with consent of the respective managers of each project. In addition, I drafted and exchanged systematic and detailed field notes with project participants. For the purpose of exchanging collected data with the manufacturing company, field notes were summarized in the form of Powerpoint presentations. These were then drafted and sent to the project participants as soon as possible, after meetings, interviews, and workshops.

To facilitate collaborative data collection, I was allowed to present an update on my practical contribution to each project, including: DES models or a summary of theoretical findings that gave an academic perspective. The subject of this presentation was mutually agreed upon by the managers and me, to not only acquire data for practical purposes but also this thesis. This was an important source for validating the preliminary findings and gaining additional input from the staff participating in the projects necessary in a collaborative inquiry process (Börjesson et al. 2006).

Semi-structured interview focused on supporting decision-making during production system design. These interviews touched upon the problems and solutions related to uncertainty and equivocality, and the use of DES in production system design. The interviews began with a description of the interviewee’s background and their role. Later, the interviewees were asked to describe the design of a production system, and narrate the major decisions from the start of each project until its conclusion. Then, the interviewees were asked to explain how they supported decision-making under uncertainty and equivocality, and its challenges, enablers, and implications with respect to the outcome of each project. They were directed to narrate the use of DES during production system design, followed by a closure and a period of open-ended questions for clarifying any issues arising during the interview. All interviews were recorded, transcribed, and

51

34

sent to the interviewees for verification. After a period of reflection, and during the analysis of data, I contacted the interviewees for further clarifications. A summary of data collected for projects A, B and C is presented in Table 3.3.

Table 3.3 – Summary of data collection for projects A, B, and C.

Project A Project B Project C Source Description No. Duration No. Duration No. Duration Field notes Full day

workshops 12 96 hours 10 80 hours 3 24 hours

Meetings 60 360

hours 40 240

hours 10 80 hours

Informal conversations with staff

Daily

Daily

Daily

Semi-structured interviews

Site management

1 50 minutes

1 40 minutes

1 53 minutes

Project management

1 73 minutes

1 80 minutes

1 55 minutes

Production engineering

1 60 minutes

1 60 minutes

1 53 minutes

Logistics development

1 45 minutes

1 60 minutes

1 50 minutes

Production development consultant

1 38 minutes

1 40 minutes

1 64 minutes

Company documents

Presentations and executive summaries detailing the goals, activities, time plans, best practices, findings, risk analysis, and the design, development, and deployment of the DES models

DES models

5 0 2

Finally, data collection included five DES models for project A, and two DES models for project C. I was responsible for the design, development, and deployment of models in project A. Models in project C were developed by personnel external to the manufacturing company and proficient in the use of DES. The development of the DES model followed the steps suggested by Banks et al. (2000). The development included the design, development and deployment phases, according to Fowler and Rose (2004). Documentation of the models was performed concurrently in these phases, in accordance to Robinson (2008c). All the DES models were initiated by a conceptual model that specified a problem situation and modeling objectives, identified the input and output data, determined the model scope,

34

sent to the interviewees for verification. After a period of reflection, and during the analysis of data, I contacted the interviewees for further clarifications. A summary of data collected for projects A, B and C is presented in Table 3.3.

Table 3.3 – Summary of data collection for projects A, B, and C.

Project A Project B Project C Source Description No. Duration No. Duration No. Duration Field notes Full day

workshops 12 96 hours 10 80 hours 3 24 hours

Meetings 60 360

hours 40 240

hours 10 80 hours

Informal conversations with staff

Daily

Daily

Daily

Semi-structured interviews

Site management

1 50 minutes

1 40 minutes

1 53 minutes

Project management

1 73 minutes

1 80 minutes

1 55 minutes

Production engineering

1 60 minutes

1 60 minutes

1 53 minutes

Logistics development

1 45 minutes

1 60 minutes

1 50 minutes

Production development consultant

1 38 minutes

1 40 minutes

1 64 minutes

Company documents

Presentations and executive summaries detailing the goals, activities, time plans, best practices, findings, risk analysis, and the design, development, and deployment of the DES models

DES models

5 0 2

Finally, data collection included five DES models for project A, and two DES models for project C. I was responsible for the design, development, and deployment of models in project A. Models in project C were developed by personnel external to the manufacturing company and proficient in the use of DES. The development of the DES model followed the steps suggested by Banks et al. (2000). The development included the design, development and deployment phases, according to Fowler and Rose (2004). Documentation of the models was performed concurrently in these phases, in accordance to Robinson (2008c). All the DES models were initiated by a conceptual model that specified a problem situation and modeling objectives, identified the input and output data, determined the model scope,

52

35

established the level of detail, and formulated assumptions and simplifications (Robinson 2008b, a).

The DES models and all elements included in the conceptual models were verified and validated concurrently during the design, development, and deployment phases (Robinson 2008c; Sargent 2013). The verification and validation was performed in four stages: a) during the abstraction of the real problem and its formulation. b) as part of the verification of model coding between conceptual model and computer model. c) during the experimentation and validation of the DES models. c) when validating the solution of DES models.

Staff responsible for projects A and C participated in the verification and validation of the DES models. In particular, production and product experts were asked to judge the reliability of the DES models in representing the system behavior. In addition, animations were used to display the operational behavior of the models. Staff from projects A and C submitted the models to extreme condition testing, looking for the plausibility of outputs. Operational graphics illustrated the performance of the model’s operation through time, and sensitivity analyses (changing model inputs to determine outputs) were performed. Finally, the models were submitted to white- and black-box validation. The white-box validation addressed the question of whether each part of the DES model represented the production systems with sufficient accuracy (Robinson 2008c). The black-box validation focused on the question of whether the model provided sufficiently accurate results according to the staff responsible for projects A and C (Kleijnen 1995). Project B failed to successfully design, develop, and deploy a DES model. Namely, a timeline was proposed that exceeded the time in which the evaluation results were expected, and thus DES could not be used in the production system design project. All models in this thesis were developed with ExtendSim 9 software. A summary of the DES models of project A is presented in Table 3.4. Table 3.5 presents a summary of the DES models of project C.

35

established the level of detail, and formulated assumptions and simplifications (Robinson 2008b, a).

The DES models and all elements included in the conceptual models were verified and validated concurrently during the design, development, and deployment phases (Robinson 2008c; Sargent 2013). The verification and validation was performed in four stages: a) during the abstraction of the real problem and its formulation. b) as part of the verification of model coding between conceptual model and computer model. c) during the experimentation and validation of the DES models. c) when validating the solution of DES models.

Staff responsible for projects A and C participated in the verification and validation of the DES models. In particular, production and product experts were asked to judge the reliability of the DES models in representing the system behavior. In addition, animations were used to display the operational behavior of the models. Staff from projects A and C submitted the models to extreme condition testing, looking for the plausibility of outputs. Operational graphics illustrated the performance of the model’s operation through time, and sensitivity analyses (changing model inputs to determine outputs) were performed. Finally, the models were submitted to white- and black-box validation. The white-box validation addressed the question of whether each part of the DES model represented the production systems with sufficient accuracy (Robinson 2008c). The black-box validation focused on the question of whether the model provided sufficiently accurate results according to the staff responsible for projects A and C (Kleijnen 1995). Project B failed to successfully design, develop, and deploy a DES model. Namely, a timeline was proposed that exceeded the time in which the evaluation results were expected, and thus DES could not be used in the production system design project. All models in this thesis were developed with ExtendSim 9 software. A summary of the DES models of project A is presented in Table 3.4. Table 3.5 presents a summary of the DES models of project C.

53

Tabl

e 3.

4 –

Des

crip

tion

of D

ES m

odel

s in

pro

ject

A.

Mod

el

Mod

el o

bjec

tive

Inpu

t dat

a Ev

alua

tion

para

met

ers

Mod

el o

utco

me

A Vi

sual

repr

esen

tatio

n of

tw

o al

tern

ativ

e pr

oduc

tion

proc

esse

s

Takt

tim

e, a

ssem

bly

stat

ions

, pro

duct

dem

and

Qua

litat

ive

disc

ussio

n w

ithin

des

ign

team

Se

lect

ing

a pr

oduc

tion

proc

ess

and

deve

lopi

ng a

layo

ut

B Ev

alua

tion

of d

esig

ned

prod

uctio

n sy

stem

Ye

arly

pro

duct

dem

and,

pr

oduc

tion

proc

ess,

cyc

le

times

Prod

uctio

n th

roug

hput

, le

ad ti

me,

and

util

izatio

n of

ass

embl

y st

atio

ns

Iden

tifyi

ng a

nd p

ropo

sing

solu

tions

to p

rodu

ctio

n-pr

oces

s bo

ttle

neck

s C

Eval

uatio

n of

des

igne

d pr

oduc

tion

syst

em

base

d on

dat

a fr

om

sele

cted

site

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es

Prod

uctio

n th

roug

hput

, le

ad ti

me,

and

util

izatio

n of

ass

embl

y st

atio

ns

Iden

tifyi

ng a

nd p

ropo

sing

solu

tions

to p

rodu

ctio

n pr

oces

s bo

ttle

neck

s

D Ev

alua

tion

of a

ssem

bly

line

staf

fing

alte

rnat

ives

Ye

arly

pro

duct

dem

and,

pr

oduc

tion

proc

ess,

cyc

le

times

, dist

urba

nces

, as

sem

bly

staf

f ass

ignm

ent

rule

s

Prod

uctio

n th

roug

hput

, le

ad ti

me,

util

izatio

n of

as

sem

bly

stat

ions

and

as

sem

bly

staf

f

Esta

blish

ing

rule

s for

as

signm

ent o

f ass

embl

y st

aff

and

need

for a

dditi

onal

im

prov

emen

ts to

ach

ieve

op

erat

iona

l obj

ectiv

es

E Ev

alua

ting

effe

cts o

f fo

ur p

rodu

ctio

n pr

oces

s en

able

rs

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es,

asse

mbl

y st

aff a

ssig

nmen

t ru

les,

logi

stic

s, c

omm

on

asse

mbl

y to

ols,

inst

ruct

ions

, au

tono

mou

s veh

icle

s in

asse

mbl

y

Prod

uctio

n th

roug

hput

, le

ad ti

me,

util

izatio

n of

as

sem

bly

stat

ions

and

as

sem

bly

staf

f

Early

des

ign,

fina

lized

and

read

y fo

r con

tinue

d on

-site

test

ing

Tabl

e 3.

4 –

Des

crip

tion

of D

ES m

odel

s in

pro

ject

A.

Mod

el

Mod

el o

bjec

tive

Inpu

t dat

a Ev

alua

tion

para

met

ers

Mod

el o

utco

me

A Vi

sual

repr

esen

tatio

n of

tw

o al

tern

ativ

e pr

oduc

tion

proc

esse

s

Takt

tim

e, a

ssem

bly

stat

ions

, pro

duct

dem

and

Qua

litat

ive

disc

ussio

n w

ithin

des

ign

team

Se

lect

ing

a pr

oduc

tion

proc

ess

and

deve

lopi

ng a

layo

ut

B Ev

alua

tion

of d

esig

ned

prod

uctio

n sy

stem

Ye

arly

pro

duct

dem

and,

pr

oduc

tion

proc

ess,

cyc

le

times

Prod

uctio

n th

roug

hput

, le

ad ti

me,

and

util

izatio

n of

ass

embl

y st

atio

ns

Iden

tifyi

ng a

nd p

ropo

sing

solu

tions

to p

rodu

ctio

n-pr

oces

s bo

ttle

neck

s C

Eval

uatio

n of

des

igne

d pr

oduc

tion

syst

em

base

d on

dat

a fr

om

sele

cted

site

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es

Prod

uctio

n th

roug

hput

, le

ad ti

me,

and

util

izatio

n of

ass

embl

y st

atio

ns

Iden

tifyi

ng a

nd p

ropo

sing

solu

tions

to p

rodu

ctio

n pr

oces

s bo

ttle

neck

s

D Ev

alua

tion

of a

ssem

bly

line

staf

fing

alte

rnat

ives

Ye

arly

pro

duct

dem

and,

pr

oduc

tion

proc

ess,

cyc

le

times

, dist

urba

nces

, as

sem

bly

staf

f ass

ignm

ent

rule

s

Prod

uctio

n th

roug

hput

, le

ad ti

me,

util

izatio

n of

as

sem

bly

stat

ions

and

as

sem

bly

staf

f

Esta

blish

ing

rule

s for

as

signm

ent o

f ass

embl

y st

aff

and

need

for a

dditi

onal

im

prov

emen

ts to

ach

ieve

op

erat

iona

l obj

ectiv

es

E Ev

alua

ting

effe

cts o

f fo

ur p

rodu

ctio

n pr

oces

s en

able

rs

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es,

asse

mbl

y st

aff a

ssig

nmen

t ru

les,

logi

stic

s, c

omm

on

asse

mbl

y to

ols,

inst

ruct

ions

, au

tono

mou

s veh

icle

s in

asse

mbl

y

Prod

uctio

n th

roug

hput

, le

ad ti

me,

util

izatio

n of

as

sem

bly

stat

ions

and

as

sem

bly

staf

f

Early

des

ign,

fina

lized

and

read

y fo

r con

tinue

d on

-site

test

ing

54

Tabl

e 3.

5 –

Des

crip

tion

of D

ES m

odel

s pr

ojec

t C.

Mod

el

Mod

el o

bjec

tive

Inpu

t dat

a Ev

alua

tion

para

met

ers

Mod

el o

utco

me

F An

alyz

e cu

rren

t veh

icle

ca

bin

prod

uctio

n sy

stem

in

thre

e in

depe

nden

t as

sem

bly

lines

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es

Prod

uctio

n th

roug

hput

, lea

d tim

e, u

tiliza

tion

of a

ssem

bly

staf

f

Qua

ntita

tive

com

paris

on

betw

een

curr

ent a

nd

futu

re p

rodu

ctio

n sy

stem

s. S

elec

tion

of a

m

ulti-

prod

uct p

rodu

ctio

n sy

stem

s G

Anal

yze

futu

re v

ehic

le c

abin

pr

oduc

tion

syst

em in

a

mul

ti-pr

oduc

t pro

duct

ion

syst

em

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es, a

nd

one

addi

tiona

l pro

duct

Prod

uctio

n th

roug

hput

, lea

d tim

e, u

tiliza

tion

of a

ssem

bly

staf

f

Tabl

e 3.

5 –

Des

crip

tion

of D

ES m

odel

s pr

ojec

t C.

Mod

el

Mod

el o

bjec

tive

Inpu

t dat

a Ev

alua

tion

para

met

ers

Mod

el o

utco

me

F An

alyz

e cu

rren

t veh

icle

ca

bin

prod

uctio

n sy

stem

in

thre

e in

depe

nden

t as

sem

bly

lines

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es

Prod

uctio

n th

roug

hput

, lea

d tim

e, u

tiliza

tion

of a

ssem

bly

staf

f

Qua

ntita

tive

com

paris

on

betw

een

curr

ent a

nd

futu

re p

rodu

ctio

n sy

stem

s. S

elec

tion

of a

m

ulti-

prod

uct p

rodu

ctio

n sy

stem

s G

Anal

yze

futu

re v

ehic

le c

abin

pr

oduc

tion

syst

em in

a

mul

ti-pr

oduc

t pro

duct

ion

syst

em

Year

ly p

rodu

ct d

eman

d,

prod

uctio

n pr

oces

s, c

ycle

tim

es, d

istur

banc

es, a

nd

one

addi

tiona

l pro

duct

Prod

uctio

n th

roug

hput

, lea

d tim

e, u

tiliza

tion

of a

ssem

bly

staf

f

55

38

3.6 DATA ANALYSIS Data analysis consists of examining, categorizing, tabulating, testing, or otherwise recombining evidence to produce empirically based findings (Yin 2013). In this thesis, data analysis was conducted simultaneously and incrementally with data collection (McCutcheon and Meredith 1993), and it included three concurrent activities: data condensation, data display, and conclusion drawing (Miles et al. 2013). Data condensation refers to the abstraction of raw data, data display is the compression of data in visual form to draw conclusions from, and conclusions drawing is concerned with a search for the meaning of happenings of each project. These activities were divided into six steps—review of literature, coding, within-case analysis, cross-case analysis, comparison of findings to literature, and drawing conclusions—as shown in Figure 3.2. Figure 3.2 presents data analysis as a linear process. In its execution, data analysis was iterative, and questions arising during the analysis often required reflection on the choice of literature or interpretation of collected data.

Conclusion Drawing

Data Display

Data Condensation

5. Literature comparison

6. Conclusions

3. Within case analysis

4. Cross case analysis

1. Reviewing literature

2. Coding empirical data

Literature Collected Data from Projects A, B, and C

Figure 3.2 – Data analysis according to the activities of data condensation, display, and conclusion drawing, and their six corresponding steps.

The first step for analyzing the collected data included reviewing relevant literature. This helped understanding the key concepts necessary for analyzing the collected data (Voss et al. 2002). Reviewing literature occurred concurrently throughout the research process. Eventually, I settled for three streams of literature that shaped data analysis. These included organization theory, literature on production system design, and DES. Organization theory informed about the salient challenges of equivocality and uncertainty, which are relevant to process innovations. The literature on production system design underpinned the basic logic behind supporting decision-making. Literature about DES provided a contextual understanding

38

3.6 DATA ANALYSIS Data analysis consists of examining, categorizing, tabulating, testing, or otherwise recombining evidence to produce empirically based findings (Yin 2013). In this thesis, data analysis was conducted simultaneously and incrementally with data collection (McCutcheon and Meredith 1993), and it included three concurrent activities: data condensation, data display, and conclusion drawing (Miles et al. 2013). Data condensation refers to the abstraction of raw data, data display is the compression of data in visual form to draw conclusions from, and conclusions drawing is concerned with a search for the meaning of happenings of each project. These activities were divided into six steps—review of literature, coding, within-case analysis, cross-case analysis, comparison of findings to literature, and drawing conclusions—as shown in Figure 3.2. Figure 3.2 presents data analysis as a linear process. In its execution, data analysis was iterative, and questions arising during the analysis often required reflection on the choice of literature or interpretation of collected data.

Conclusion Drawing

Data Display

Data Condensation

5. Literature comparison

6. Conclusions

3. Within case analysis

4. Cross case analysis

1. Reviewing literature

2. Coding empirical data

Literature Collected Data from Projects A, B, and C

Figure 3.2 – Data analysis according to the activities of data condensation, display, and conclusion drawing, and their six corresponding steps.

The first step for analyzing the collected data included reviewing relevant literature. This helped understanding the key concepts necessary for analyzing the collected data (Voss et al. 2002). Reviewing literature occurred concurrently throughout the research process. Eventually, I settled for three streams of literature that shaped data analysis. These included organization theory, literature on production system design, and DES. Organization theory informed about the salient challenges of equivocality and uncertainty, which are relevant to process innovations. The literature on production system design underpinned the basic logic behind supporting decision-making. Literature about DES provided a contextual understanding

56

39

of the process of designing, developing, and deploying models during the design of production systems. These streams of literature were necessary to comprehend the extant knowledge about conditions of use, requirements, challenges, and activities in the design of production systems facilitating the use of DES for supporting decision-making.

A second step involved coding and compiling the collected data in a case study database. Coding is especially useful for reducing the collected data (Karlsson 2010). In addition, a coding scheme facilitates the replication or extension of a study and establishes a logical link between literature and codes (Dubé and Paré 2003). I began by identifying the conditions of use, requirements, challenges hindering the use, and activities facilitating the use of DES for supporting decision-making. In addition, I referred to literature prescribing the support of decision-making during the design of production systems, and literature describing decision support in practice. I then compared and categorized those studies, and generated a code that included the conditions of use, requirements, challenges, and activities. I was careful to ensure that these codes were not based on instances of non-representative informants or events, and therefore generated codes prior to analyzing the collected data. I coded data immediately after collecting them, and included the field notes (workshops, meetings, site visits, and informal conversation with staff) and the semi-structured interviews. I revised the coded data a number of times during each project, to comprehend the meaning of events in relation to supporting decision-making in the design of production systems.

A third step was to analyze each project separately, namely by performing a within-case analysis including the description of a project and its emerging findings (Barratt et al. 2011). This required developing four detailed project descriptions, which included data collated from various sources. The project descriptions focused on the conditions of use, requirements, challenges, and activities for the use of DES for supporting decision-making in the design of production systems. At this point, I ordered chronologically each of the codes and drafted a report detailing the findings of each project. Seeking confirmation of the findings from the key stakeholders is important for validating results (Eisenhardt and Graebner 2007). Therefore, I presented the findings to the corresponding managers of projects A, B, and C. The presentation included a description of the project, the objectives pursued by the manufacturing company for designing a production system, and a chronology of decisions. This was followed by a list of conditions of use, requirements, challenges, and activities in the design of production systems facilitating the use of DES for supporting decision-making. The managers provided feedback and clarified the chronology of events. A chronology of events was adopted to understand the basic sequence of cause and effect during the design of a production system, and establish the conditions surrounding a decision and its outcome, as suggested by Yin

39

of the process of designing, developing, and deploying models during the design of production systems. These streams of literature were necessary to comprehend the extant knowledge about conditions of use, requirements, challenges, and activities in the design of production systems facilitating the use of DES for supporting decision-making.

A second step involved coding and compiling the collected data in a case study database. Coding is especially useful for reducing the collected data (Karlsson 2010). In addition, a coding scheme facilitates the replication or extension of a study and establishes a logical link between literature and codes (Dubé and Paré 2003). I began by identifying the conditions of use, requirements, challenges hindering the use, and activities facilitating the use of DES for supporting decision-making. In addition, I referred to literature prescribing the support of decision-making during the design of production systems, and literature describing decision support in practice. I then compared and categorized those studies, and generated a code that included the conditions of use, requirements, challenges, and activities. I was careful to ensure that these codes were not based on instances of non-representative informants or events, and therefore generated codes prior to analyzing the collected data. I coded data immediately after collecting them, and included the field notes (workshops, meetings, site visits, and informal conversation with staff) and the semi-structured interviews. I revised the coded data a number of times during each project, to comprehend the meaning of events in relation to supporting decision-making in the design of production systems.

A third step was to analyze each project separately, namely by performing a within-case analysis including the description of a project and its emerging findings (Barratt et al. 2011). This required developing four detailed project descriptions, which included data collated from various sources. The project descriptions focused on the conditions of use, requirements, challenges, and activities for the use of DES for supporting decision-making in the design of production systems. At this point, I ordered chronologically each of the codes and drafted a report detailing the findings of each project. Seeking confirmation of the findings from the key stakeholders is important for validating results (Eisenhardt and Graebner 2007). Therefore, I presented the findings to the corresponding managers of projects A, B, and C. The presentation included a description of the project, the objectives pursued by the manufacturing company for designing a production system, and a chronology of decisions. This was followed by a list of conditions of use, requirements, challenges, and activities in the design of production systems facilitating the use of DES for supporting decision-making. The managers provided feedback and clarified the chronology of events. A chronology of events was adopted to understand the basic sequence of cause and effect during the design of a production system, and establish the conditions surrounding a decision and its outcome, as suggested by Yin

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40

(2013). For example, during the presentation of findings, the manager of project B described the order in which the requirements were formulated (by first defining products and then a production process), and how doing so helped achieve consensus on the process innovation during the design of the production system.

The fourth step was to develop a cross-case analysis. Cross-case analysis is the act of comparing and contrasting the patterns emerging from a detailed within-case analysis, which guards against arriving at conclusions based on limited data (Eisenhardt 1989). The cross-case analysis included pattern matching, clustering, and chronological sequences (Yin 2013), and was built on the findings from the within-case analysis. An initial step involved grouping the codes for the conditions of use, requirements, challenges, and activities from each project into the general sets of codes that applied to the three projects. The four codes were further broken down into 14 subcategories. Breaking down the codes into sub-categories is a logical step, because each project provided a higher detail, which either agreed with or ran counter to reviewed literature. The descriptions of the codes and their subcategories utilized in the cross-case analysis are presented in Figure 3.3. The coded data from each project were placed in relation to the process of designing a production system described in the literature, and were compared. The final step consisted of comparing the codes and subcategories of a project against the same codes and subcategories from another project, to maintain interdependence in the analysis of practice, requirements, challenges, and activities.

A fifth step involved comparing the findings from the cross-case analysis to the literature, and identifying the commonalities and differences, and questioning why those differences existed in the first place. The use of organization theory and the literature on production system design were essential. On the one hand, organization theory provided the logic required to understand the concepts of uncertainty and equivocality, and the nature of activities for their reduction. On the other hand, literature on production system design specifies the rationale for the design of a production system. This was fundamental to establish a “theoretical” sequence of events, and an expected consequence from requirements, challenges, and activities when designing a production system. Based on the theoretical insight, it was possible to identify empirical data that ran counter to or substantiated an empirical finding. This was necessary to understand how the use of DES for supporting decisions differed when introducing process innovations from traditional projects for designing production systems. Lastly, the conclusions were drawn.

40

(2013). For example, during the presentation of findings, the manager of project B described the order in which the requirements were formulated (by first defining products and then a production process), and how doing so helped achieve consensus on the process innovation during the design of the production system.

The fourth step was to develop a cross-case analysis. Cross-case analysis is the act of comparing and contrasting the patterns emerging from a detailed within-case analysis, which guards against arriving at conclusions based on limited data (Eisenhardt 1989). The cross-case analysis included pattern matching, clustering, and chronological sequences (Yin 2013), and was built on the findings from the within-case analysis. An initial step involved grouping the codes for the conditions of use, requirements, challenges, and activities from each project into the general sets of codes that applied to the three projects. The four codes were further broken down into 14 subcategories. Breaking down the codes into sub-categories is a logical step, because each project provided a higher detail, which either agreed with or ran counter to reviewed literature. The descriptions of the codes and their subcategories utilized in the cross-case analysis are presented in Figure 3.3. The coded data from each project were placed in relation to the process of designing a production system described in the literature, and were compared. The final step consisted of comparing the codes and subcategories of a project against the same codes and subcategories from another project, to maintain interdependence in the analysis of practice, requirements, challenges, and activities.

A fifth step involved comparing the findings from the cross-case analysis to the literature, and identifying the commonalities and differences, and questioning why those differences existed in the first place. The use of organization theory and the literature on production system design were essential. On the one hand, organization theory provided the logic required to understand the concepts of uncertainty and equivocality, and the nature of activities for their reduction. On the other hand, literature on production system design specifies the rationale for the design of a production system. This was fundamental to establish a “theoretical” sequence of events, and an expected consequence from requirements, challenges, and activities when designing a production system. Based on the theoretical insight, it was possible to identify empirical data that ran counter to or substantiated an empirical finding. This was necessary to understand how the use of DES for supporting decisions differed when introducing process innovations from traditional projects for designing production systems. Lastly, the conclusions were drawn.

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Analyzing Conditions of Use RQ 1 - How do manufacturing companies specify the conditions of use when utilizing DES to support decision-making during the design of production systems involving process innovations?

• Background for designing production systems involving process innovation• Description, scope and motivation for production system design• Choice of decision-making approach in the design of production systems • Decisions made during the design of production systems

Understanding RequirementsRQ 3 - What requirements must be considered in the design of production systems involving process innovations when using DES to support decision-making for reducing uncertainty and equivocality?

• Process followed for supporting decisions during production system design• Major decisions in the design of production system• Functions or departments involved in the design of the production system• Activities focused on reducing equivocality and uncertainty during the design

of the production system

Identifying ChallengesRQ 2 - What challenges faced during the design of production systems involving process innovations undermine the use of DES for supporting decision-making?

• Incidence of equivocality and uncertainty in the design of production systems affecting use of DES

• Resources associated to the use of DES in the design of production systems• Process standardization for the use of DES in the design of production systems

Specifying ActivitiesRQ 3 - How should additional activities be included in the design of production systems involving process innovations, to facilitate the use of DES for supporting decision-making?

• Stage in the design of a production system involving process innovations for using DES to supporting decisions-making

• Criteria characterizing production system included in DES models for supporting decision-making

• Objective of DES use during production system design for supporting decision-making under equivocality or uncertainty

Figure 3.3 – Description of codes and their sub-categories utilized in the analysis of collected data

3.7 QUALITY OF RESEARCH The general criterion for research quality must be trustworthiness (Karlsson 2010). Adopting a collaborative research approach required accounting for a number of challenges that have been perceived as a threat to the quality of research. For example, a classical criticism to collaborative research concerns the potential threat to validity posed by the partnership between researchers and practitioners (Svensson et al. 2007; Ellström 2008). This may mean that the researcher becomes “one of the tribe”, and that practical contribution precedes academic ones. At the same time, there exists a dilemma of being close to practitioners to earn trust, and distancing oneself from practitioners to allow critical thinking.

To overcome this threat, I alternated between periods favoring practice-oriented and research-oriented activities. In the former, I participated or initiated activities focusing on interaction with practitioners, including defining problems, developing a common understanding, facilitating critical reflections, and disseminating results at the manufacturing company. In addition, after the conclusion of each project, I submitted the findings to the scrutiny of managers responsible for each project. This helped verify the

41

Analyzing Conditions of Use RQ 1 - How do manufacturing companies specify the conditions of use when utilizing DES to support decision-making during the design of production systems involving process innovations?

• Background for designing production systems involving process innovation• Description, scope and motivation for production system design• Choice of decision-making approach in the design of production systems • Decisions made during the design of production systems

Understanding RequirementsRQ 3 - What requirements must be considered in the design of production systems involving process innovations when using DES to support decision-making for reducing uncertainty and equivocality?

• Process followed for supporting decisions during production system design• Major decisions in the design of production system• Functions or departments involved in the design of the production system• Activities focused on reducing equivocality and uncertainty during the design

of the production system

Identifying ChallengesRQ 2 - What challenges faced during the design of production systems involving process innovations undermine the use of DES for supporting decision-making?

• Incidence of equivocality and uncertainty in the design of production systems affecting use of DES

• Resources associated to the use of DES in the design of production systems• Process standardization for the use of DES in the design of production systems

Specifying ActivitiesRQ 3 - How should additional activities be included in the design of production systems involving process innovations, to facilitate the use of DES for supporting decision-making?

• Stage in the design of a production system involving process innovations for using DES to supporting decisions-making

• Criteria characterizing production system included in DES models for supporting decision-making

• Objective of DES use during production system design for supporting decision-making under equivocality or uncertainty

Figure 3.3 – Description of codes and their sub-categories utilized in the analysis of collected data

3.7 QUALITY OF RESEARCH The general criterion for research quality must be trustworthiness (Karlsson 2010). Adopting a collaborative research approach required accounting for a number of challenges that have been perceived as a threat to the quality of research. For example, a classical criticism to collaborative research concerns the potential threat to validity posed by the partnership between researchers and practitioners (Svensson et al. 2007; Ellström 2008). This may mean that the researcher becomes “one of the tribe”, and that practical contribution precedes academic ones. At the same time, there exists a dilemma of being close to practitioners to earn trust, and distancing oneself from practitioners to allow critical thinking.

To overcome this threat, I alternated between periods favoring practice-oriented and research-oriented activities. In the former, I participated or initiated activities focusing on interaction with practitioners, including defining problems, developing a common understanding, facilitating critical reflections, and disseminating results at the manufacturing company. In addition, after the conclusion of each project, I submitted the findings to the scrutiny of managers responsible for each project. This helped verify the

59

42

findings and interact with the key staff who provided insights for developing actionable knowledge on supporting decision-making in the design of production systems. In the latter, I withdrew from the manufacturing company and gave precedence to critical reflections, analyses, and academic writing. Reviewing and identifying a stream of literature to position my findings, and underpinning the conditions of use, requirements, challenges, and activities on extant literature were essential to distance myself from the interaction. In addition, this thesis undertook activities to meet the criteria of construct validity, internal validity, external validity, and reliability, which accounts for research quality in the case study method (Gibbert and Ruigrok 2010).

Construct validity is the extent to which the correct operational measures can be established for the concepts being studied (Voss et al. 2002). According to Yin (2013), construct validity is supported by the use of multiple source of evidence. This was accounted by collecting and comparing data from semi-structured interviews, field notes (e.g. workshops, meetings, and informal conversations), and company documents. Construct validity was further strengthened in two ways. Firstly, through the examination of preliminary findings presented to the staff participating in the different projects, and external researchers who provided feedback for each study at conferences or during journal submissions, namely discussions with my supervisors. Secondly, the projects were followed as they were conducted and not retrospectively, a situation that facilitates establishing a chain of evidence (Gibbert et al. 2008b).

In addition, a preliminary version of the findings were presented on-site to the Global Technology Manager, the Global Director of Product Architecture, the Director of Manufacturing Technology Strategy and Research, and members of the European Operations Management team from the manufacturing company that provided additional feedback during an open discussion workshop. In this event, the feedback was focused on the conditions of use, requirements, challenges, and activities in the design of production systems facilitating the use of DES for supporting decision-making. Finally, construct validity was reinforced by establishing a chain of evidence leading to the answers of the research questions, as advised by Karlsson (2010). This began by clarifying what and how data were collected, describing why interviews and projects were selected, and providing an explanation of how literature was employed to analysis data in relation to the purpose and research questions of this thesis.

Internal validity establishes a causal relationship whereby certain conditions are shown to lead to other results (Yin 2013). To meet the criteria of internal validity, I developed a frame of reference against which the empirical findings are compared, as exemplified by the process for designing a production system and the information constructs utilized for supporting decisions described in Chapter 2. In addition, extant literature was utilized

42

findings and interact with the key staff who provided insights for developing actionable knowledge on supporting decision-making in the design of production systems. In the latter, I withdrew from the manufacturing company and gave precedence to critical reflections, analyses, and academic writing. Reviewing and identifying a stream of literature to position my findings, and underpinning the conditions of use, requirements, challenges, and activities on extant literature were essential to distance myself from the interaction. In addition, this thesis undertook activities to meet the criteria of construct validity, internal validity, external validity, and reliability, which accounts for research quality in the case study method (Gibbert and Ruigrok 2010).

Construct validity is the extent to which the correct operational measures can be established for the concepts being studied (Voss et al. 2002). According to Yin (2013), construct validity is supported by the use of multiple source of evidence. This was accounted by collecting and comparing data from semi-structured interviews, field notes (e.g. workshops, meetings, and informal conversations), and company documents. Construct validity was further strengthened in two ways. Firstly, through the examination of preliminary findings presented to the staff participating in the different projects, and external researchers who provided feedback for each study at conferences or during journal submissions, namely discussions with my supervisors. Secondly, the projects were followed as they were conducted and not retrospectively, a situation that facilitates establishing a chain of evidence (Gibbert et al. 2008b).

In addition, a preliminary version of the findings were presented on-site to the Global Technology Manager, the Global Director of Product Architecture, the Director of Manufacturing Technology Strategy and Research, and members of the European Operations Management team from the manufacturing company that provided additional feedback during an open discussion workshop. In this event, the feedback was focused on the conditions of use, requirements, challenges, and activities in the design of production systems facilitating the use of DES for supporting decision-making. Finally, construct validity was reinforced by establishing a chain of evidence leading to the answers of the research questions, as advised by Karlsson (2010). This began by clarifying what and how data were collected, describing why interviews and projects were selected, and providing an explanation of how literature was employed to analysis data in relation to the purpose and research questions of this thesis.

Internal validity establishes a causal relationship whereby certain conditions are shown to lead to other results (Yin 2013). To meet the criteria of internal validity, I developed a frame of reference against which the empirical findings are compared, as exemplified by the process for designing a production system and the information constructs utilized for supporting decisions described in Chapter 2. In addition, extant literature was utilized

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43

to develop codes for interpreting empirical data and match them with patterns established in previous studies, which either confirmed or ran counter to prior publications, as described in the data analysis section. Furthermore, I compiled chronologically the list of events related to supporting decisions in the design of production systems during the introduction of process innovations. Besides arraying events descriptively, this procedure helped investigate the presumed causal events, and understand whether empirical data complied or differed from prior publications focused on decision support during production system design.

External validity refers to the generalizability of research findings and the extent to which the results can account for the settings beyond those of the subject of the study (Williamson and Johanson 2013). This thesis adopted an analytical generalization, to comply with the requirements of external validity (Maxwell 2012; Ketokivi and Choi 2014). Contributions to the current understanding in this field were developed from empirical observations, rather than by the statistical generalization of a population (Gibbert et al. 2008b). To account for project-specific findings, I provided a rationale for sampling and selection derived from its relevance to extant literature, as suggested by McCutcheon and Meredith (1993). Thus, this thesis extrapolated its findings in relation to similar results present in prior studies that refer to the design of production systems and the use of DES for supporting decision-making. Two characteristics of the three projects presented in this thesis justify the relevance of findings beyond a local industrial segment: a) the three projects are universal, being undertaken in two different continents and three countries; and, b) the staff responsible for designing each production system were experienced international experts.

Reliability involves the certainty that the inquiry process and results of the research are repeated (Yin 2013). Reliability was strengthened by documenting research procedures and using case study protocols to report the manner in which each project was conducted. Following the suggestions of Gibbert et al. (2008a), transparency was pursued for reliability. To achieve transparency of results, the description of how each study was documented in a case study database as proposed by Karlsson (2010). The case study database included a condensation of all empirical data and its origins, as well as a synthesis of literature relevant to each study. Four activities supported the reliability of data analysis: First, the analysis of data by multiple researchers, which was particularly useful in the beginning of my PhD. Second, comparing and identifying discrepancies between empirical data and theoretical understanding, and corroborating empirical data with the staff participating in each project, to ensure trustworthiness of the findings (Barratt et al. 2011). Third, a description of the analysis process and the use of preliminary techniques (e.g. field notes, coding of data, data display) (Yin 2013). Fourth, maintaining a chain of evidence connecting the purpose of the thesis, its research questions, and the collection of data and

43

to develop codes for interpreting empirical data and match them with patterns established in previous studies, which either confirmed or ran counter to prior publications, as described in the data analysis section. Furthermore, I compiled chronologically the list of events related to supporting decisions in the design of production systems during the introduction of process innovations. Besides arraying events descriptively, this procedure helped investigate the presumed causal events, and understand whether empirical data complied or differed from prior publications focused on decision support during production system design.

External validity refers to the generalizability of research findings and the extent to which the results can account for the settings beyond those of the subject of the study (Williamson and Johanson 2013). This thesis adopted an analytical generalization, to comply with the requirements of external validity (Maxwell 2012; Ketokivi and Choi 2014). Contributions to the current understanding in this field were developed from empirical observations, rather than by the statistical generalization of a population (Gibbert et al. 2008b). To account for project-specific findings, I provided a rationale for sampling and selection derived from its relevance to extant literature, as suggested by McCutcheon and Meredith (1993). Thus, this thesis extrapolated its findings in relation to similar results present in prior studies that refer to the design of production systems and the use of DES for supporting decision-making. Two characteristics of the three projects presented in this thesis justify the relevance of findings beyond a local industrial segment: a) the three projects are universal, being undertaken in two different continents and three countries; and, b) the staff responsible for designing each production system were experienced international experts.

Reliability involves the certainty that the inquiry process and results of the research are repeated (Yin 2013). Reliability was strengthened by documenting research procedures and using case study protocols to report the manner in which each project was conducted. Following the suggestions of Gibbert et al. (2008a), transparency was pursued for reliability. To achieve transparency of results, the description of how each study was documented in a case study database as proposed by Karlsson (2010). The case study database included a condensation of all empirical data and its origins, as well as a synthesis of literature relevant to each study. Four activities supported the reliability of data analysis: First, the analysis of data by multiple researchers, which was particularly useful in the beginning of my PhD. Second, comparing and identifying discrepancies between empirical data and theoretical understanding, and corroborating empirical data with the staff participating in each project, to ensure trustworthiness of the findings (Barratt et al. 2011). Third, a description of the analysis process and the use of preliminary techniques (e.g. field notes, coding of data, data display) (Yin 2013). Fourth, maintaining a chain of evidence connecting the purpose of the thesis, its research questions, and the collection of data and

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its analysis (Dubé and Paré 2003). Table 3.6 presents the activities supporting the requirements for research quality of the papers appended in this thesis.

Table 3.6 – Activities supporting the requirements for research quality of the papers appended in this thesis.

Research quality Activities supporting research quality Papers requirement

1 2 3 4 5 6

Construct validity Use of multiple sources of evidence ● ● ● ● ● ● Presenting preliminary findings for feedback and review ● ● ● ● ● ○

Matching data patterns and establishing a chain of evidence ● ● ● ● ● ●

Internal validity Building preliminary explanations based on the analysis of empirical data and literature

● ○ ● ● ● ○

Eliminating rival explanations that were insufficiently supported ● ○ ● ● ● ○

Building a time series analysis of empirical data ● ● ● ● ● ●

External validity Developing a theoretical understanding of key concepts ● ● ● ● ● ○

Providing a rationale for project selection and sampling ● ○ ● ● ● ○

Reliability Using case study protocols ● ● ● ● ● ● Developing a case study database ● ● ● ● ● ●

● and ○ indicate high and low incidence of activities supporting research quality, respectively

44

its analysis (Dubé and Paré 2003). Table 3.6 presents the activities supporting the requirements for research quality of the papers appended in this thesis.

Table 3.6 – Activities supporting the requirements for research quality of the papers appended in this thesis.

Research quality Activities supporting research quality Papers requirement

1 2 3 4 5 6

Construct validity Use of multiple sources of evidence ● ● ● ● ● ● Presenting preliminary findings for feedback and review ● ● ● ● ● ○

Matching data patterns and establishing a chain of evidence ● ● ● ● ● ●

Internal validity Building preliminary explanations based on the analysis of empirical data and literature

● ○ ● ● ● ○

Eliminating rival explanations that were insufficiently supported ● ○ ● ● ● ○

Building a time series analysis of empirical data ● ● ● ● ● ●

External validity Developing a theoretical understanding of key concepts ● ● ● ● ● ○

Providing a rationale for project selection and sampling ● ○ ● ● ● ○

Reliability Using case study protocols ● ● ● ● ● ● Developing a case study database ● ● ● ● ● ●

● and ○ indicate high and low incidence of activities supporting research quality, respectively

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4 SUMMARY OF APPENDED PAPERS This chapter summarizes the six papers appended in this thesis, and highlights the findings relevant to the answers to the research questions.

4.1 PAPER 1 Paper 1 primarily contributes to addressing research questions 1 and 3.

Title: Decision-Making Approaches in Process Innovations: An Explorative Case Study

Process innovations do not always lead to desirable results (Frishammar et al. 2011; Sjödin et al. 2016). Literature shows that staff frequently encounter difficulties when identifying decision-making approaches (Eriksson et al. 2016; Terjesen and Patel 2017). Different decision-making approaches are used to solve problems when implementing process innovations (Bellgran and Säfsten 2010; Gershwin 2018). However, it remains unclear when to select a particular decision-making approach (Dane et al. 2012; Matzler et al. 2014; Luoma 2016; Calabretta et al. 2017).

The purpose of this study was to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. In particular, this study was focused on decision-making in the production system design. Furthermore, this paper analyzed recent findings suggesting that the degree of equivocality and analyzability of a decision, the structuredness of a decision, may constitute the main criteria for determining a decision-making approach (Julmi 2019).

This paper utilized a case study method to analyze the empirical data from two production system design projects (projects A and B). The findings revealed that distinct decision-making approaches occur at different degrees of equivocality and analyzability. Figure 4.1 illustrates the correspondence and frequency of decision-making approaches along with the degrees of equivocality and analyzability in projects A and B.

Findings showed that decision-making approaches were most frequently utilized in conditions of low equivocality and high analyzability. Data revealed that the staff adopted an intuitive or a combination of intuitive and normative decision-making approaches under low equivocality and low analyzability. In the context of equivocality and high analyzability, the results showed that the staff resorted to intuitive decision-making for agreeing on the type of information necessary to complete a task.

In addition, staff of projects A and B made decisions against a backdrop of equivocality and low analyzability involving the lack of rules or processes, and partial agreement about information necessary to complete a task. Data showed that the staff made exclusive use of intuitive decision-making in

45

4 SUMMARY OF APPENDED PAPERS This chapter summarizes the six papers appended in this thesis, and highlights the findings relevant to the answers to the research questions.


Title: Decision-Making Approaches in Process Innovations: An Explorative Case Study

Process innovations do not always lead to desirable results (Frishammar et al. 2011; Sjödin et al. 2016). Literature shows that staff frequently encounter difficulties when identifying decision-making approaches (Eriksson et al. 2016; Terjesen and Patel 2017). Different decision-making approaches are used to solve problems when implementing process innovations (Bellgran and Säfsten 2010; Gershwin 2018). However, it remains unclear when to select a particular decision-making approach (Dane et al. 2012; Matzler et al. 2014; Luoma 2016; Calabretta et al. 2017).

The purpose of this study was to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. In particular, this study was focused on decision-making in the production system design. Furthermore, this paper analyzed recent findings suggesting that the degree of equivocality and analyzability of a decision, the structuredness of a decision, may constitute the main criteria for determining a decision-making approach (Julmi 2019).

This paper utilized a case study method to analyze the empirical data from two production system design projects (projects A and B). The findings revealed that distinct decision-making approaches occur at different degrees of equivocality and analyzability. Figure 4.1 illustrates the correspondence and frequency of decision-making approaches along with the degrees of equivocality and analyzability in projects A and B.

Findings showed that decision-making approaches were most frequently utilized in conditions of low equivocality and high analyzability. Data revealed that the staff adopted an intuitive or a combination of intuitive and normative decision-making approaches under low equivocality and low analyzability. In the context of equivocality and high analyzability, the results showed that the staff resorted to intuitive decision-making for agreeing on the type of information necessary to complete a task.

In addition, staff of projects A and B made decisions against a backdrop of equivocality and low analyzability involving the lack of rules or processes, and partial agreement about information necessary to complete a task. Data showed that the staff made exclusive use of intuitive decision-making in

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decisions involving high equivocality and low analyzability. Findings showed that no decisions coincided with high equivocality and high analyzability, namely multiple and conflicting interpretation, ambiguous information, and clear rules and processes. Figure 4.2 presents a classification of the choice of decision-making approaches in relation to the degree of equivocality and analyzability of projects A and B.

High Equivocality

Low Equivocality

Low Analyzability High Analyzability

Equivocality

Intuitive decision-making

Intuitive and normative decision-making

Normative decision-making

Decisions Project A

Decisions Project B

Figure 4.1 - Correspondence of decision-making approaches with degree of equivocality and analyzability in projects A and B.

46

decisions involving high equivocality and low analyzability. Findings showed that no decisions coincided with high equivocality and high analyzability, namely multiple and conflicting interpretation, ambiguous information, and clear rules and processes. Figure 4.2 presents a classification of the choice of decision-making approaches in relation to the degree of equivocality and analyzability of projects A and B.

High Equivocality

Low Equivocality

Low Analyzability High Analyzability

Equivocality

Intuitive decision-making

Intuitive and normative decision-making

Normative decision-making

Decisions Project A

Decisions Project B

Figure 4.1 - Correspondence of decision-making approaches with degree of equivocality and analyzability in projects A and B.

64

Low

Ana

lyza

bilit

y

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bjec

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rule

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-bas

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roce

dure

s

High

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ne

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, com

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• No

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nal

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this

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icate

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inad

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type

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• Dec

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d th

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of c

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dec

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n-m

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g ap

proa

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in re

latio

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quiv

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ity a

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of p

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A an

d B

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• Dec

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tions

, com

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nova

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and

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cuse

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ach

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. Ad

ditio

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rmat

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trim

enta

l (in

tuiti

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• No

decis

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for

reex

amin

atio

n,

deci

sion

in

this

area

may

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icate

an

inad

equa

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inte

rpre

tatio

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les

or

proc

esse

s

High

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mat

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• Dec

ision

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out

type

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qu

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ysis

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ch

oice

(c

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d no

rmat

ive

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• Non

rea

dily

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re in

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quan

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ive

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(com

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ial a

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• Det

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re

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ng

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l ana

lysis

of w

ell d

efin

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form

atio

n

(nor

mat

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• Hig

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ake

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prev

ious

ly

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coun

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the

aggr

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ctiv

ities

an

d m

anag

eria

l in

volv

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t

(com

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orm

ativ

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• Rap

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rienc

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in th

e pa

st a

nd b

ased

on

a ho

listic

ass

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of

info

rmat

ion

(intu

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dec

ision

-mak

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ivoc

ality

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cal i

nter

pret

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ck o

f inf

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n

• Dec

ision

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quiri

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and

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res,

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sight

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d th

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l de

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posit

ion

of a

pro

blem

(com

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tuiti

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and

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• Dec

ision

s de

term

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g ne

w p

roce

sses

or

rule

s ne

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ary

for

expl

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ow t

o ar

rive

to a

so

lutio

n (in

tuiti

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on-m

akin

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Fi

gure

4.2

- C

lass

ifica

tion

of c

hoic

e of

dec

isio

n-m

akin

g ap

proa

ches

in re

latio

n to

deg

rees

of e

quiv

ocal

ity a

nd

anal

yzab

ility

of p

roje

cts

A an

d B

65

48


Title: Approaching the Reduction of Uncertainty in Production System Design through Discrete-Event Simulation

Manufacturing companies require introducing process innovation to gain a competitive edge. Literature proposes the use of DES and a structured design in production systems to facilitate the implementation of process innovations (Bellgran and Säfsten 2010). However, implementing process innovations require reducing uncertainties that challenge the characterization of a production system during its design (O'Connor and Rice 2013).

A key limitation of current DES publications is that these have approached the reduction of uncertainty by focusing on the selection of values of design parameters and not on issues that arise when such parameters must first be identified (Wynn et al. 2011). The purpose of this paper was to examine the uncertainties reduced through DES during the design of a production system when implementing process innovations. This paper was based on a single project study (project C).

The paper identified 17 types of uncertainties during the design of a production system, which are presented in Table 4.1. The results showed that DES may reduce eight of those uncertainties, including state, technical, environmental, system, temporal, structural, epistemic, and of definition.

Based on these findings, the paper proposed a model for understanding how DES may help reduce the uncertainties of process innovation implementation. This study argued that information provided by the DES models to reduce uncertainties during production system design could be classified into two categories: the output of the models in the form of key performance indicators (KPIs); and, the simulation modeling process itself. The KPIs provided information about the behavior of the system, and thus reduced the technical and epistemic uncertainties to a high degree.

Empirical evidence suggested that the modeling process also brought a decrease in the uncertainty level. Discrepancies between management and operational levels among capacity and disturbance behaviors were solved by involving backgrounds of a cross-functional team members in the project, with steps such as problem formulation or data gathering. The visual aspects of DES were made easier to present information and results from different backgrounds, ensuring a common vision towards the uncertainties affecting the production system in the project team. In regard to the building of a conceptual model, the simplification of the system and subsystems made the overall introduction of significant changes more understandable

48


Title: Approaching the Reduction of Uncertainty in Production System Design through Discrete-Event Simulation

Manufacturing companies require introducing process innovation to gain a competitive edge. Literature proposes the use of DES and a structured design in production systems to facilitate the implementation of process innovations (Bellgran and Säfsten 2010). However, implementing process innovations require reducing uncertainties that challenge the characterization of a production system during its design (O'Connor and Rice 2013).

A key limitation of current DES publications is that these have approached the reduction of uncertainty by focusing on the selection of values of design parameters and not on issues that arise when such parameters must first be identified (Wynn et al. 2011). The purpose of this paper was to examine the uncertainties reduced through DES during the design of a production system when implementing process innovations. This paper was based on a single project study (project C).

The paper identified 17 types of uncertainties during the design of a production system, which are presented in Table 4.1. The results showed that DES may reduce eight of those uncertainties, including state, technical, environmental, system, temporal, structural, epistemic, and of definition.

Based on these findings, the paper proposed a model for understanding how DES may help reduce the uncertainties of process innovation implementation. This study argued that information provided by the DES models to reduce uncertainties during production system design could be classified into two categories: the output of the models in the form of key performance indicators (KPIs); and, the simulation modeling process itself. The KPIs provided information about the behavior of the system, and thus reduced the technical and epistemic uncertainties to a high degree.

Empirical evidence suggested that the modeling process also brought a decrease in the uncertainty level. Discrepancies between management and operational levels among capacity and disturbance behaviors were solved by involving backgrounds of a cross-functional team members in the project, with steps such as problem formulation or data gathering. The visual aspects of DES were made easier to present information and results from different backgrounds, ensuring a common vision towards the uncertainties affecting the production system in the project team. In regard to the building of a conceptual model, the simplification of the system and subsystems made the overall introduction of significant changes more understandable

66

49

and accessible to different levels of the organization. Figure 4.3 presents a model showing a reduction in uncertainties through DES.

4.1 – Types of uncertainty

Author Type Description Milliken (1987)

State Limited understanding of changes in environment Effect Inability to predict the consequences of changes Response Lack of knowledge of available response options

Gerwin (1988)

Technical Unknown precision and reliability of processes Financial Inability to determine the return on investment Social Questions concerning the required support system

Rowe (1994) Temporal Absence of information about future or past states Structural Uncertainty due to system complexity Metrical Doubtful and variable measurement process Translational Difficulties in explaining results across the company

McManus and Hastings (2006)

Epistemic Facts unknown or imprecisely known Definition System has not been decided/specified

Lane and Maxfield (2005a)

Truth Inability to prove if an alternative is true or not Semantic Doubts on the meaning of an alternative Ontological Actors’ ontology-based uncertainty

Clarkson and Eckert (2005)

Known Variability in past cases seen as a distribution Unknown Unexpected internal and/or external uncertainties

Figure 4.3 – Model showing the reduction of uncertainties through DES.

49

and accessible to different levels of the organization. Figure 4.3 presents a model showing a reduction in uncertainties through DES.

4.1 – Types of uncertainty

Author Type Description Milliken (1987)

State Limited understanding of changes in environment Effect Inability to predict the consequences of changes Response Lack of knowledge of available response options

Gerwin (1988)

Technical Unknown precision and reliability of processes Financial Inability to determine the return on investment Social Questions concerning the required support system

Rowe (1994) Temporal Absence of information about future or past states Structural Uncertainty due to system complexity Metrical Doubtful and variable measurement process Translational Difficulties in explaining results across the company

McManus and Hastings (2006)

Epistemic Facts unknown or imprecisely known Definition System has not been decided/specified

Lane and Maxfield (2005a)

Truth Inability to prove if an alternative is true or not Semantic Doubts on the meaning of an alternative Ontological Actors’ ontology-based uncertainty

Clarkson and Eckert (2005)

Known Variability in past cases seen as a distribution Unknown Unexpected internal and/or external uncertainties

Figure 4.3 – Model showing the reduction of uncertainties through DES.

67

50


Title: Challenges of Discrete Event Simulation in the early stages of production system design

Researchers have devoted considerable efforts to identify the challenges of DES in production system design (Taylor et al. 2015; McGinnis and Rose 2017). Few studies to date investigate the challenges of DES in the early stages of production system design (Medyna et al. 2012). Addressing this issue is critical, as there exist an increased need for supporting decisions in the early phases of production system design based on simulation tools such as DES. The knowledge of such challenges is essential for implementing process innovations that facilitate increased competitiveness among manufacturing companies (Javahernia and Sunmola 2017). The purpose of this paper was to analyze the challenges of applying DES in the early stages of production system design.

This paper adopted case study research to analyze three production system design projects (projects A, B, and C). This paper identified the challenges of using DES for supporting decision-making in production system design. These challenges are related to the design, development, and deployment of DES models. Table 4.2 presents the relation of cases and challenges of using DES for supporting decision-making. This paper made four new contributions to the current understanding of DES applications.

First, this study revealed that equivocality limited the overall comprehension of the intricacies of the production system during design. As a consequence, the design teams faced difficulties in identifying problems during the production system design and translating problems into DES models. Second, findings suggested that uncertainty is a challenge that is never truly resolved in the early stages of production system design. Consequently, uncertainty may not only affect the output of DES models, but can also hinder the use of DES altogether. Third, specifying the interrelation of subsystems in a production process may be just as important as specifying accurate parameters for input values in DES models. Data suggested that proper coordination would bring production system design teams a step closer to avoiding many known challenges of DES application. Fourth, findings showed that one-time effort to define the assumptions and simplifications may be insufficient. Manufacturing companies must develop a strategy to continuously acquire data to inform the reduction of assumptions and simplifications over time.

50


Title: Challenges of Discrete Event Simulation in the early stages of production system design

Researchers have devoted considerable efforts to identify the challenges of DES in production system design (Taylor et al. 2015; McGinnis and Rose 2017). Few studies to date investigate the challenges of DES in the early stages of production system design (Medyna et al. 2012). Addressing this issue is critical, as there exist an increased need for supporting decisions in the early phases of production system design based on simulation tools such as DES. The knowledge of such challenges is essential for implementing process innovations that facilitate increased competitiveness among manufacturing companies (Javahernia and Sunmola 2017). The purpose of this paper was to analyze the challenges of applying DES in the early stages of production system design.

This paper adopted case study research to analyze three production system design projects (projects A, B, and C). This paper identified the challenges of using DES for supporting decision-making in production system design. These challenges are related to the design, development, and deployment of DES models. Table 4.2 presents the relation of cases and challenges of using DES for supporting decision-making. This paper made four new contributions to the current understanding of DES applications.

First, this study revealed that equivocality limited the overall comprehension of the intricacies of the production system during design. As a consequence, the design teams faced difficulties in identifying problems during the production system design and translating problems into DES models. Second, findings suggested that uncertainty is a challenge that is never truly resolved in the early stages of production system design. Consequently, uncertainty may not only affect the output of DES models, but can also hinder the use of DES altogether. Third, specifying the interrelation of subsystems in a production process may be just as important as specifying accurate parameters for input values in DES models. Data suggested that proper coordination would bring production system design teams a step closer to avoiding many known challenges of DES application. Fourth, findings showed that one-time effort to define the assumptions and simplifications may be insufficient. Manufacturing companies must develop a strategy to continuously acquire data to inform the reduction of assumptions and simplifications over time.

68

51

Table 4.2 – Challenges of using DES for supporting decision-making in the design of production systems during the design, development, and deployment of DES models.

DES Phase Challenge Project A

Project B

Project C

Design - Decision support restricted by question-specific model formulation

√ √ √

- Representation of production system dynamics and complexity

- Validity of model detail level √ - Simplification of production system complexity and factor interdependence

√

- Non-uniform abstraction level for model simplification √ √

- Modeling combinatorial explosion of options in a production process

- Incomplete and conflicting production system knowledge √ √

- Development of simulation and production system knowledge √ √

- Software diversity and lack of standardization

Development - Model verification and validation √ √ √ - Model development time √ √ - Input data collection and analysis √ √ √ - Input data availability and quality √ √ √

Deployment - Model interoperability and information sharing between models

√

- Industry acceptance of DES √ √ √ - Communication of results for effective decision making √ √

- Simulation model maintenance - Consideration of trade-offs and non-intuitive decisions

√

- High cost and low reusability of models √ √

51

Table 4.2 – Challenges of using DES for supporting decision-making in the design of production systems during the design, development, and deployment of DES models.

DES Phase Challenge Project A

Project B

Project C

Design - Decision support restricted by question-specific model formulation

√ √ √

- Representation of production system dynamics and complexity

- Validity of model detail level √ - Simplification of production system complexity and factor interdependence

√

- Non-uniform abstraction level for model simplification √ √

- Modeling combinatorial explosion of options in a production process

- Incomplete and conflicting production system knowledge √ √

- Development of simulation and production system knowledge √ √

- Software diversity and lack of standardization

Development - Model verification and validation √ √ √ - Model development time √ √ - Input data collection and analysis √ √ √ - Input data availability and quality √ √ √

Deployment - Model interoperability and information sharing between models

√

- Industry acceptance of DES √ √ √ - Communication of results for effective decision making √ √

- Simulation model maintenance - Consideration of trade-offs and non-intuitive decisions

√

- High cost and low reusability of models √ √

69

52

4.4 PAPER 4 Paper 4 primarily contributes to addressing research questions 3, and subsequently 1 and 2.

Title: What Guides Information Consensus? Approaching the Reduction of Equivocality in Process Innovations

Prior studies on equivocality have focused on its consequences or the strategies that may lead to its reduction (Stevens 2014; Eriksson et al. 2016; Sjödin et al. 2016). However, there is no clarity on what guides information consensus. This is a critical issue because equivocality originates from a lack of consensus and understanding (Eriksson et al. 2016). The purpose of this study was to analyze information consensus and the reduction of equivocality in process innovations. This paper presented findings based on a single case study (project C), and investigated the factors considered important by managers when guiding information consensus in process innovations. The data analysis was performed based on four classical concepts of manufacturing strategy: strategic objectives, decision areas, external fit, and internal fit. Organization theory was used to inform the data analysis and provide a logic for comprehending the uncertainty and equivocality inherent to process innovations.

This paper proposed a novel framework (Table 4.3) to facilitate the reduction of equivocality and guide information consensus when implementing process innovations during the design of production systems. This paper recommends the concepts of strategic objective, decision areas, external fit, and internal fit to guide information consensus through the exchange of subjective views that define the purpose, characteristics, and operation of process innovation. Furthermore, findings highlighted the importance of the interdependence of these concepts. In addition, the findings reveal not only the importance of the relationship among the four concepts, but that an order of these concepts is indispensable to achieve information consensus.

The first element in this framework involved the strategic decision concept. This concept was used to understand what the process innovation would achieve. A second element in this framework involved the decision area concept, which the development team relied on to define what the process innovation would look like. The third element in this framework included the concepts of external and internal fits, which helped solve the disagreements and determine the most critical issues affecting the operation of the process innovations. The results of this study highlighted the existence of different perspectives about information over time. This implied that information consensus is not achieved by a single event, and active work to reduce the equivocality is necessary.

52

4.4 PAPER 4 Paper 4 primarily contributes to addressing research questions 3, and subsequently 1 and 2.

Title: What Guides Information Consensus? Approaching the Reduction of Equivocality in Process Innovations

Prior studies on equivocality have focused on its consequences or the strategies that may lead to its reduction (Stevens 2014; Eriksson et al. 2016; Sjödin et al. 2016). However, there is no clarity on what guides information consensus. This is a critical issue because equivocality originates from a lack of consensus and understanding (Eriksson et al. 2016). The purpose of this study was to analyze information consensus and the reduction of equivocality in process innovations. This paper presented findings based on a single case study (project C), and investigated the factors considered important by managers when guiding information consensus in process innovations. The data analysis was performed based on four classical concepts of manufacturing strategy: strategic objectives, decision areas, external fit, and internal fit. Organization theory was used to inform the data analysis and provide a logic for comprehending the uncertainty and equivocality inherent to process innovations.

This paper proposed a novel framework (Table 4.3) to facilitate the reduction of equivocality and guide information consensus when implementing process innovations during the design of production systems. This paper recommends the concepts of strategic objective, decision areas, external fit, and internal fit to guide information consensus through the exchange of subjective views that define the purpose, characteristics, and operation of process innovation. Furthermore, findings highlighted the importance of the interdependence of these concepts. In addition, the findings reveal not only the importance of the relationship among the four concepts, but that an order of these concepts is indispensable to achieve information consensus.

The first element in this framework involved the strategic decision concept. This concept was used to understand what the process innovation would achieve. A second element in this framework involved the decision area concept, which the development team relied on to define what the process innovation would look like. The third element in this framework included the concepts of external and internal fits, which helped solve the disagreements and determine the most critical issues affecting the operation of the process innovations. The results of this study highlighted the existence of different perspectives about information over time. This implied that information consensus is not achieved by a single event, and active work to reduce the equivocality is necessary.

70

53

Table 4.3. Framework guiding information consensus for the reduction of equivocality in process innovations.

Concept Description Information consensus Strategic objective Inherent strengths

pursued by a manufacturing company

Cost, quality, flexibility, delivery

Decision areas Decisions that determine the internal composition of a manufacturing company

Structural decisions Process technology Capacity Facilities Vertical integration

Infrastructural decisions Human resources Organization Quality Production planning New product development Performance measurement system

External fit Congruence between decisions made in a production system with its external settings

Product range, customer order size, level of schedule changes

Internal fit Internal cohesiveness of all elements inside a production system

Production processes, production volumes, and key manufacturing tasks

53

Table 4.3. Framework guiding information consensus for the reduction of equivocality in process innovations.

Concept Description Information consensus Strategic objective Inherent strengths

pursued by a manufacturing company

Cost, quality, flexibility, delivery

Decision areas Decisions that determine the internal composition of a manufacturing company

Structural decisions Process technology Capacity Facilities Vertical integration

Infrastructural decisions Human resources Organization Quality Production planning New product development Performance measurement system

External fit Congruence between decisions made in a production system with its external settings

Product range, customer order size, level of schedule changes

Internal fit Internal cohesiveness of all elements inside a production system

Production processes, production volumes, and key manufacturing tasks

71

54

4.5 PAPER 5 Paper 5 primarily contributes to addressing research question 4.

Title: Simulation-Based Optimization for Facility Layout Design in Conditions of High Uncertainty

Despite the increased use of simulation-based optimization (SBO), the design of facility layout is challenged by high levels of uncertainty and equivocality associated with implementing process innovations (Drira et al. 2007; O'Connor and Rice 2013). Adding to this, there is limited comprehension about the agreement and acquisition of information necessary to manage high levels of uncertainty prior to the assignment of probabilities that are critical for increased manufacturing competitiveness (Frishammar et al. 2011). Addressing this issue, this paper offered a different approach from prior publications and thus aimed to understand the conceptual modeling activities of SBO for facility layout design in conditions of high uncertainty and equivocality.

This paper was based on a case study method that included three production system design projects, including projects A and B. Data was collected between 2014 and 2016, and included company documents and conceptual models. Data analysis included categorization of criteria in the design of a production system. Additionally, the analysis focused on the level of uncertainty and equivocality in the conceptual models supporting the facility layout design, based on a theory-driven classification (Courtney 2003).

This paper presented two main findings. Firstly, the criteria of strategic objectives, products, market, production processes, and decision interrelation are essential for specifying the conceptual model activities during facility layout design. These criteria are critical to achieving an agreement on the information and increase the information amid high uncertainty and equivocality. Table 4.4 presents the conceptual modeling activities and the criteria of strategic objectives, products, market, production processes, and decision interrelation for each project in this paper.

Secondly, the development of the conceptual model of SBO for facility layout design requires transferring from equivocality to uncertainty before the start of the conceptual model. This paper showed that the criteria of strategic objectives, products, markets, and decision interrelation in the facility layout changes included in the activities of the conceptual models are essential to reduce the equivocality and uncertainty.

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4.5 PAPER 5 Paper 5 primarily contributes to addressing research question 4.

Title: Simulation-Based Optimization for Facility Layout Design in Conditions of High Uncertainty

Despite the increased use of simulation-based optimization (SBO), the design of facility layout is challenged by high levels of uncertainty and equivocality associated with implementing process innovations (Drira et al. 2007; O'Connor and Rice 2013). Adding to this, there is limited comprehension about the agreement and acquisition of information necessary to manage high levels of uncertainty prior to the assignment of probabilities that are critical for increased manufacturing competitiveness (Frishammar et al. 2011). Addressing this issue, this paper offered a different approach from prior publications and thus aimed to understand the conceptual modeling activities of SBO for facility layout design in conditions of high uncertainty and equivocality.

This paper was based on a case study method that included three production system design projects, including projects A and B. Data was collected between 2014 and 2016, and included company documents and conceptual models. Data analysis included categorization of criteria in the design of a production system. Additionally, the analysis focused on the level of uncertainty and equivocality in the conceptual models supporting the facility layout design, based on a theory-driven classification (Courtney 2003).

This paper presented two main findings. Firstly, the criteria of strategic objectives, products, market, production processes, and decision interrelation are essential for specifying the conceptual model activities during facility layout design. These criteria are critical to achieving an agreement on the information and increase the information amid high uncertainty and equivocality. Table 4.4 presents the conceptual modeling activities and the criteria of strategic objectives, products, market, production processes, and decision interrelation for each project in this paper.

Secondly, the development of the conceptual model of SBO for facility layout design requires transferring from equivocality to uncertainty before the start of the conceptual model. This paper showed that the criteria of strategic objectives, products, markets, and decision interrelation in the facility layout changes included in the activities of the conceptual models are essential to reduce the equivocality and uncertainty.

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Table 4.4 – Conceptual modeling activities and characterization criteria for facility layout design

Conceptual model activity

Characterization Criteria for Facility Layout Design Project A Project B Project C

Objective Layout selection, detailing, refinement, improvement, preparation

Layout assessment, layout refinement

Layout assessment, layout comparison

Model output

Strategic objectives Strategic objectives

Strategic objectives

1. Units produced 1. Units produced 1. Units produced 2. Lead time 2. Lead time 2. Lead time 3. Utilization 3. Operator

quantity 3. Operator quantity

4. Visualization

4. Visualization Model input Product, production

process, market Product, production process, market

Product, production process, market

1. Assembly times 1. Assembly times 1. Assembly times 2. Products 2. Products 2. Products 3. Demand 3. Demand 3. Demand 4. Operation sequence

4. Operation sequence

4. Initial layout

5. Disturbances 5. Disturbances

6. Operators

Model scope Decision interrelation

Decision interrelation


Detail level Information amount of characterization criteria

Information amount of characterization criteria


Assumption Information amount of characterization criteria



Simplification Agreement on information type

Agreement on information type


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Table 4.4 – Conceptual modeling activities and characterization criteria for facility layout design

Conceptual model activity

Characterization Criteria for Facility Layout Design Project A Project B Project C

Objective Layout selection, detailing, refinement, improvement, preparation

Layout assessment, layout refinement

Layout assessment, layout comparison

Model output

Strategic objectives Strategic objectives

Strategic objectives

1. Units produced 1. Units produced 1. Units produced 2. Lead time 2. Lead time 2. Lead time 3. Utilization 3. Operator

quantity 3. Operator quantity

4. Visualization

4. Visualization Model input Product, production

process, market Product, production process, market

Product, production process, market

1. Assembly times 1. Assembly times 1. Assembly times 2. Products 2. Products 2. Products 3. Demand 3. Demand 3. Demand 4. Operation sequence

4. Operation sequence

4. Initial layout

5. Disturbances 5. Disturbances

6. Operators

Model scope Decision interrelation



Detail level Information amount of characterization criteria



Assumption Information amount of characterization criteria



Simplification Agreement on information type



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4.6 PAPER 6 Paper 6 primarily contributes to addressing research questions 4, and questions 1 and 3.

Title: Simulation in the Production System Design Process of Assembly Systems

A well-designed production system is necessary for a company to achieve competitiveness (Heilala et al. 2010; Rösiö and Bruch 2014). The use of DES as a tool for the analysis of change in production is well-documented and extends to numerous applications (Jahangirian et al. 2010; Negahban and Smith 2014). Despite the relevance of DES and its benefits, there are few empirical studies detailing the use of DES throughout the production system design process.

There are several challenges facing the implementation of DES in the design of a production system, including data availability, design processes, and personnel proficient in the use of DES. These limitations question the use of DES throughout the design process of a production system. Therefore, the purpose of this paper was to explore how DES can be used in the production system design process. This paper was based on a single case study (project A).

This paper reported the development of five different DES models during the design of a production system. The results showed the phases of production system design, and the objectives pursued by DES specialists when developing these models in each phase. Figure 4.4 presents the relation of DES models to the phases of production system design, according to empirical data.

DES was selected because of its ease of use in modeling discrete systems, dynamic flows, and what-if scenarios. The findings highlighted that the use of DES helped the design team to learn more about a novel assembly system amid limited experience. The findings stressed the need for numerous DES models during the process of designing a production system as a countermeasure to the design team’s concerns regarding the implementation of a new assembly system.

Furthermore, the findings showed that the DES models supported decision-making during the design of the production system. The findings presented evidence of a link between decisions and the objectives of DES models. From an input data perspective, the volume of data increased consistently from Model 1 to Model 5. This was a consequence of the refinement of the type of objectives each model was built for.

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4.6 PAPER 6 Paper 6 primarily contributes to addressing research questions 4, and questions 1 and 3.

Title: Simulation in the Production System Design Process of Assembly Systems

A well-designed production system is necessary for a company to achieve competitiveness (Heilala et al. 2010; Rösiö and Bruch 2014). The use of DES as a tool for the analysis of change in production is well-documented and extends to numerous applications (Jahangirian et al. 2010; Negahban and Smith 2014). Despite the relevance of DES and its benefits, there are few empirical studies detailing the use of DES throughout the production system design process.

There are several challenges facing the implementation of DES in the design of a production system, including data availability, design processes, and personnel proficient in the use of DES. These limitations question the use of DES throughout the design process of a production system. Therefore, the purpose of this paper was to explore how DES can be used in the production system design process. This paper was based on a single case study (project A).

This paper reported the development of five different DES models during the design of a production system. The results showed the phases of production system design, and the objectives pursued by DES specialists when developing these models in each phase. Figure 4.4 presents the relation of DES models to the phases of production system design, according to empirical data.

DES was selected because of its ease of use in modeling discrete systems, dynamic flows, and what-if scenarios. The findings highlighted that the use of DES helped the design team to learn more about a novel assembly system amid limited experience. The findings stressed the need for numerous DES models during the process of designing a production system as a countermeasure to the design team’s concerns regarding the implementation of a new assembly system.

Furthermore, the findings showed that the DES models supported decision-making during the design of the production system. The findings presented evidence of a link between decisions and the objectives of DES models. From an input data perspective, the volume of data increased consistently from Model 1 to Model 5. This was a consequence of the refinement of the type of objectives each model was built for.

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5 ANALYSIS This chapter brings together findings from the six papers appended to this thesis, and answers its four research questions.

5.1 DETERMINING THE CONDITIONS OF USE OF DES IN PRODUCTION SYSTEM DESIGN

The existing literature advocates an increased use of simulation-based techniques for supporting decision-making in the design of production systems including DES (Trigueiro de Sousa Junior et al. 2019). This knowledge about supporting decision-making in the design of production systems is particularly valuable under conditions of equivocality and uncertainty originating from process innovations (Sjödin 2019).

The literature describes the conditions of use of DES for supporting decision-making as normative (Karlsson 2010). Prior studies argue that DES supports decision-making based on quantitative data analysis including causal relationships between control and performance variables (Law 2015). The quantitative analysis of DES models determine bottlenecks, queues, resources utilization, lead times and throughput leading to enhanced system performances (Ali and Seifoddini 2006). This knowledge is essential for anticipating failed decisions and avoiding costly mistakes during the design of production systems (Longo and Mirabelli 2009; Negahban and Smith 2014). In agreement with prior studies, the findings of papers 1, 4, and 6 show the saliency of quantitative based analyses when using DES to support decision-making in the design of production systems. For example, evaluating effects of four production process enablers or staffing policies in the assembly process. However, the findings of this thesis suggest that the conditions of use of DES for supporting decision-making extend beyond quantitative analyses.

Recent studies suggest that the conditions of use of a decision-making approach, and the techniques supporting decision-making, rest on the structuredness of decisions (Julmi 2019). This argument rests on the claim that a decision may range from well- to ill-structured depending on whether rules and processes can be unequivocally applied. Decisions with low levels of equivocality and high levels of analyzability are described as well structured. These types of decisions are assigned to normative decision-making, in which DES has been commonly applied. Decisions including high levels of equivocality and low levels of analyzability are described as ill structured. Ill-structured decisions are assigned to intuitive decision-making. They commonly lack the use of DES.

This thesis provides novel findings about the conditions of use of DES for supporting decision-making to situations for which the decisions were

59

5 ANALYSIS This chapter brings together findings from the six papers appended to this thesis, and answers its four research questions.

5.1 DETERMINING THE CONDITIONS OF USE OF DES IN PRODUCTION SYSTEM DESIGN

The existing literature advocates an increased use of simulation-based techniques for supporting decision-making in the design of production systems including DES (Trigueiro de Sousa Junior et al. 2019). This knowledge about supporting decision-making in the design of production systems is particularly valuable under conditions of equivocality and uncertainty originating from process innovations (Sjödin 2019).

The literature describes the conditions of use of DES for supporting decision-making as normative (Karlsson 2010). Prior studies argue that DES supports decision-making based on quantitative data analysis including causal relationships between control and performance variables (Law 2015). The quantitative analysis of DES models determine bottlenecks, queues, resources utilization, lead times and throughput leading to enhanced system performances (Ali and Seifoddini 2006). This knowledge is essential for anticipating failed decisions and avoiding costly mistakes during the design of production systems (Longo and Mirabelli 2009; Negahban and Smith 2014). In agreement with prior studies, the findings of papers 1, 4, and 6 show the saliency of quantitative based analyses when using DES to support decision-making in the design of production systems. For example, evaluating effects of four production process enablers or staffing policies in the assembly process. However, the findings of this thesis suggest that the conditions of use of DES for supporting decision-making extend beyond quantitative analyses.

Recent studies suggest that the conditions of use of a decision-making approach, and the techniques supporting decision-making, rest on the structuredness of decisions (Julmi 2019). This argument rests on the claim that a decision may range from well- to ill-structured depending on whether rules and processes can be unequivocally applied. Decisions with low levels of equivocality and high levels of analyzability are described as well structured. These types of decisions are assigned to normative decision-making, in which DES has been commonly applied. Decisions including high levels of equivocality and low levels of analyzability are described as ill structured. Ill-structured decisions are assigned to intuitive decision-making. They commonly lack the use of DES.

This thesis provides novel findings about the conditions of use of DES for supporting decision-making to situations for which the decisions were

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derived conceptually in the past (Julmi 2019). This thesis shows that in the design of production systems involving process innovations, the conditions of use of DES are not limited to well-structured decisions. Instead, it is also of value for ill-structured decisions to support decision-making during the design of production systems involving process innovations. Thus, the conditions of use of DES extend over a spectrum of equivocality and analyzability ranges.

The data presented in paper 1 show that staff utilized DES models in three distinct degrees of decision structuredness. First, staff adopted DES in equivocality and low analyzability for determining advantages and trade-offs of multi-product production system in the conceptual phase of production system design. This included non-readily solvable decisions where intuition can enact shared understanding, but normative-decision making is needed for quantitative insight. Second, staff used DES in low equivocality and high analyzability for comparing the performance of an existing production system to a multi-product production system. In these two instances, DES models provided valuable quantitative analysis essential for comparing layout alternatives or quantifying the operational performance of line staffing alternatives for DES models of projects A and C. Third, staff used DES in high equivocality and low analyzability for understanding the consequence of selecting a layout including a multi-product production system in the pre-study phase. In the past, literature suggested that staff facing ill-structured decisions adopt intuitive decision-making because intuition relies on integrating information holistically into coherent patterns (Dane and Pratt 2007). The data from paper 1 show that the use of the DES model for supporting ill-structured decisions (high equivocality and low analyzability) were of qualitative nature and depended on the intuitive input from staff. For example, the qualitative-based DES models in project A are providing a visual representation of the production process.

Recent publications underscore the importance of visual DES models and qualitative analyses (Gogi et al. 2016). Studies suggest that technological advances and increasing computational capabilities may facilitate the use of visual DES models for supporting decision-making during production system design (Turner et al. 2016; Frank et al. 2019). The results of this thesis indicate that qualitative-based DES models that give precedence to visual representations are resource consuming, but essential for the design of production systems involving process innovations. The findings of paper 4 show that for manufacturing companies the choice of visual DES models focused on qualitative analysis may come as a trade-off. This trade-off is related to the cost and effort required for designing, developing, and deploying visual DES models and the increased understanding of staff who face unfamiliar circumstances inherent to process innovations.

The findings of paper 1, 4, and 6 suggest that the conditions of use of DES for supporting decision-making cannot be limited to situations involving the

60

derived conceptually in the past (Julmi 2019). This thesis shows that in the design of production systems involving process innovations, the conditions of use of DES are not limited to well-structured decisions. Instead, it is also of value for ill-structured decisions to support decision-making during the design of production systems involving process innovations. Thus, the conditions of use of DES extend over a spectrum of equivocality and analyzability ranges.

The data presented in paper 1 show that staff utilized DES models in three distinct degrees of decision structuredness. First, staff adopted DES in equivocality and low analyzability for determining advantages and trade-offs of multi-product production system in the conceptual phase of production system design. This included non-readily solvable decisions where intuition can enact shared understanding, but normative-decision making is needed for quantitative insight. Second, staff used DES in low equivocality and high analyzability for comparing the performance of an existing production system to a multi-product production system. In these two instances, DES models provided valuable quantitative analysis essential for comparing layout alternatives or quantifying the operational performance of line staffing alternatives for DES models of projects A and C. Third, staff used DES in high equivocality and low analyzability for understanding the consequence of selecting a layout including a multi-product production system in the pre-study phase. In the past, literature suggested that staff facing ill-structured decisions adopt intuitive decision-making because intuition relies on integrating information holistically into coherent patterns (Dane and Pratt 2007). The data from paper 1 show that the use of the DES model for supporting ill-structured decisions (high equivocality and low analyzability) were of qualitative nature and depended on the intuitive input from staff. For example, the qualitative-based DES models in project A are providing a visual representation of the production process.

Recent publications underscore the importance of visual DES models and qualitative analyses (Gogi et al. 2016). Studies suggest that technological advances and increasing computational capabilities may facilitate the use of visual DES models for supporting decision-making during production system design (Turner et al. 2016; Frank et al. 2019). The results of this thesis indicate that qualitative-based DES models that give precedence to visual representations are resource consuming, but essential for the design of production systems involving process innovations. The findings of paper 4 show that for manufacturing companies the choice of visual DES models focused on qualitative analysis may come as a trade-off. This trade-off is related to the cost and effort required for designing, developing, and deploying visual DES models and the increased understanding of staff who face unfamiliar circumstances inherent to process innovations.

The findings of paper 1, 4, and 6 suggest that the conditions of use of DES for supporting decision-making cannot be limited to situations involving the

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detailed specification of production systems. Instead, this thesis argues that determining the conditions of use of DES for supporting decision-making, involving process innovations, rests on two aspects. These are the structuredness of a decision, including its degree of equivocality or analyzability, and the quantitative or qualitative nature of the DES models. Importantly, this thesis suggests that the conditions of use of DES for supporting decision-making comprehending well-structured decisions correspond to quantitative analyses. This is exemplified by the quantitative-based models B through G described in this thesis. Conversely, the conditions of use of DES comprehending ill-structured decisions correspond to qualitative based models, for instance model A of project A presented in this thesis.

5.2 CHALLENGES UNDERMINING DES USE IN PRODUCTION SYSTEM DESIGN

The use of DES in the design of production systems is subject to numerous challenges undermining the support of decision-making (Balci 2012; Taylor et al. 2015; Bärring et al. 2018; Robinson 2019). Studies describing the challenges of DES use in production system design are presented in Table 2.3. This thesis provides four novel findings about the challenges undermining the use of DES for supporting decision-making in the design of production systems involving process innovations. These challenges are related to the lack of understanding (equivocality), the absence of information (uncertainty), the lack of a structured approach, and the absence of resources for DES use.

A first finding constitutes empirical data suggesting that equivocality is at the root-cause of failing to use DES for supporting decision-making, or consuming additional resources for the design, development, and deployment of models. This thesis suggests when and how equivocality leads to forsaking the test of what-if scenarios, or comprehending the consequences of choices, by means of DES in the design of production systems. Papers 3 and 4 show that equivocality faced in the use of DES for supporting decision-making related to the existence of multiple and conflicting interpretations, which limited the overall comprehension of the production system during its design (Fowler and Rose 2004). An important finding of this study constitutes identifying the period during the design of production systems when equivocality constitutes a challenge undermining the use of DES. Prior studies identify the start of production system design as a domain where equivocality is encountered (Zack 2007; Eriksson et al. 2016; Sjödin et al. 2016). The results of papers 2, 3, and 4 identify equivocality in the pre-study phase as the salient challenge undermining the use of DES for supporting decision-making based on empirical data from projects A, B, and C. Consequently, the staff responsible for the design of production systems faced difficulties identifying and translating problems

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detailed specification of production systems. Instead, this thesis argues that determining the conditions of use of DES for supporting decision-making, involving process innovations, rests on two aspects. These are the structuredness of a decision, including its degree of equivocality or analyzability, and the quantitative or qualitative nature of the DES models. Importantly, this thesis suggests that the conditions of use of DES for supporting decision-making comprehending well-structured decisions correspond to quantitative analyses. This is exemplified by the quantitative-based models B through G described in this thesis. Conversely, the conditions of use of DES comprehending ill-structured decisions correspond to qualitative based models, for instance model A of project A presented in this thesis.

5.2 CHALLENGES UNDERMINING DES USE IN PRODUCTION SYSTEM DESIGN

The use of DES in the design of production systems is subject to numerous challenges undermining the support of decision-making (Balci 2012; Taylor et al. 2015; Bärring et al. 2018; Robinson 2019). Studies describing the challenges of DES use in production system design are presented in Table 2.3. This thesis provides four novel findings about the challenges undermining the use of DES for supporting decision-making in the design of production systems involving process innovations. These challenges are related to the lack of understanding (equivocality), the absence of information (uncertainty), the lack of a structured approach, and the absence of resources for DES use.

A first finding constitutes empirical data suggesting that equivocality is at the root-cause of failing to use DES for supporting decision-making, or consuming additional resources for the design, development, and deployment of models. This thesis suggests when and how equivocality leads to forsaking the test of what-if scenarios, or comprehending the consequences of choices, by means of DES in the design of production systems. Papers 3 and 4 show that equivocality faced in the use of DES for supporting decision-making related to the existence of multiple and conflicting interpretations, which limited the overall comprehension of the production system during its design (Fowler and Rose 2004). An important finding of this study constitutes identifying the period during the design of production systems when equivocality constitutes a challenge undermining the use of DES. Prior studies identify the start of production system design as a domain where equivocality is encountered (Zack 2007; Eriksson et al. 2016; Sjödin et al. 2016). The results of papers 2, 3, and 4 identify equivocality in the pre-study phase as the salient challenge undermining the use of DES for supporting decision-making based on empirical data from projects A, B, and C. Consequently, the staff responsible for the design of production systems faced difficulties identifying and translating problems

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into DES models during the production system design. Furthermore, the design teams experienced problems in specifying an appropriate level of model abstraction when incomplete and conflicting production system knowledge existed (Mönch et al. 2011). This situation is best exemplified in paper 4 and in the DES models of projects A and C.

A second finding constitutes additional insight about the lack of information, e.g., uncertainty, in the use of DES for supporting decision-making in production system design. Prior studies propose that the lack of information constitute a challenge troubling the development of DES models (Barlas and Heavey 2016; Bokrantz et al. 2018; Gürdür et al. 2019). The findings suggest that uncertainty is a challenge that is never truly resolved in the design of production systems. Agreeing with prior studies, this thesis shows that uncertainty constitutes a critical challenge for the application of the DES models for the design of production systems. The literature emphasizes the absence of input data necessary to verify and validate the DES models as one of the most salient challenges (Skoogh et al. 2012). Yet, empirical results from papers 2, 3, and 4 reveal that the absence of input data is not the only challenge associated with uncertainty. In addition, the findings of paper 2, 3, and 4 suggest that specifying the interrelation of subsystems in a production process may be just as important as specifying accurate parameters for input values in the DES models. The severity of this issue was increased by the uncertainty related to the probability that assumptions made during design were incorrect or to the presence of entirely unknown facts that have a bearing on the designed production system (Frishammar et al. 2011). Consequently, uncertainty may not only affect the output of DES models, but also hinder the use of DES. These findings add further empirical evidence to the reports suggesting the importance of uncertainty in the use of DES during the design of production systems (Oberkampf et al. 2002; Bokrantz et al. 2018). The literature indicates that the negative consequences of uncertainty in the design of production systems increase when involving process innovations (Säfsten et al. 2014; Stevens 2014; Sjödin et al. 2016; Sjödin 2019). The findings of papers 2, 3, and 4 confirm this, and suggest the prominence of uncertainty in the conceptual and detailed design phases of production system design. This thesis identifies uncertainty in the conceptual and detailed design phases as the predominant challenge undermining the use of DES for supporting decision-making.

A third finding involves a structured approach for the use of DES in the design of production systems. The literature argues for a structured approach to the design of production systems (Rösiö and Bruch 2018). This includes clear procedures for identifying needs, specifying requirements, and developing solutions (Bellgran and Säfsten 2010). Similarly, DES advocates the use of a structured approach for designing, developing, and deploying DES models during the design of production systems (Martin et

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into DES models during the production system design. Furthermore, the design teams experienced problems in specifying an appropriate level of model abstraction when incomplete and conflicting production system knowledge existed (Mönch et al. 2011). This situation is best exemplified in paper 4 and in the DES models of projects A and C.

A second finding constitutes additional insight about the lack of information, e.g., uncertainty, in the use of DES for supporting decision-making in production system design. Prior studies propose that the lack of information constitute a challenge troubling the development of DES models (Barlas and Heavey 2016; Bokrantz et al. 2018; Gürdür et al. 2019). The findings suggest that uncertainty is a challenge that is never truly resolved in the design of production systems. Agreeing with prior studies, this thesis shows that uncertainty constitutes a critical challenge for the application of the DES models for the design of production systems. The literature emphasizes the absence of input data necessary to verify and validate the DES models as one of the most salient challenges (Skoogh et al. 2012). Yet, empirical results from papers 2, 3, and 4 reveal that the absence of input data is not the only challenge associated with uncertainty. In addition, the findings of paper 2, 3, and 4 suggest that specifying the interrelation of subsystems in a production process may be just as important as specifying accurate parameters for input values in the DES models. The severity of this issue was increased by the uncertainty related to the probability that assumptions made during design were incorrect or to the presence of entirely unknown facts that have a bearing on the designed production system (Frishammar et al. 2011). Consequently, uncertainty may not only affect the output of DES models, but also hinder the use of DES. These findings add further empirical evidence to the reports suggesting the importance of uncertainty in the use of DES during the design of production systems (Oberkampf et al. 2002; Bokrantz et al. 2018). The literature indicates that the negative consequences of uncertainty in the design of production systems increase when involving process innovations (Säfsten et al. 2014; Stevens 2014; Sjödin et al. 2016; Sjödin 2019). The findings of papers 2, 3, and 4 confirm this, and suggest the prominence of uncertainty in the conceptual and detailed design phases of production system design. This thesis identifies uncertainty in the conceptual and detailed design phases as the predominant challenge undermining the use of DES for supporting decision-making.

A third finding involves a structured approach for the use of DES in the design of production systems. The literature argues for a structured approach to the design of production systems (Rösiö and Bruch 2018). This includes clear procedures for identifying needs, specifying requirements, and developing solutions (Bellgran and Säfsten 2010). Similarly, DES advocates the use of a structured approach for designing, developing, and deploying DES models during the design of production systems (Martin et

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al. 2018). However, recent studies prescribing the activities of a structured production design process do not specify activities related to the use of DES beyond the evaluation of a production system (Andersen et al. 2017).

Findings from this thesis show that the state of practice does not present an improvement over current theoretical understanding. Instead, empirical data from papers 2, 3, and 4 outline the absence of clear activities showing what, how, and when DES should be used during the design of a production system. For example, findings from paper 3 underscore the lack of clear DES-related responsibilities for staff involved in the design of production systems in projects A, B, and C. In addition, the results of paper 3 show the dependency on the continuous enforcement of managers with previous simulation experience for implementing DES in the design of production systems.

Recent publications emphasize the importance of simulation as an enabler of future manufacturing capabilities critical for the design of production systems (Francalanza et al. 2017). However, the findings of paper 3 show the reactive nature of DES use and the absence of theoretical insight at manufacturing companies when supporting decision-making. Prior studies argue that adopting ad hoc practices under scant theoretical insight have led to failed attempts at supporting decisions in the design of production systems (Chryssolouris et al. 2008). Consequently, the findings of this thesis underscore the vulnerability of manufacturing companies when using simulation-based tools for supporting decision-making. In line with earlier research, these findings suggest that manufacturing companies cannot rely exclusively on simulation specialists following best practices advocated by literature (Balci 2012), nor on the increased use of simulation based tools at manufacturing companies (Mourtzis 2019). Instead, the results highlight the need for increased knowledge in production system design including the specification of DES-related activities. For example, joint activities between decision-maker and DES specialists in the formulation of problems, setting of objectives, or scope of models (Robinson 2019).

A fourth finding includes the allocation of resources associated to the use of DES for supporting decision-making in the design of production systems. The use of simulation in the design of production systems is essential for the future of manufacturing and so are the resources facilitating its use (McGinnis and Rose 2017). Yet, studies report a low level of simulation knowledge in organizations, and a scarcity of simulation specialists in the manufacturing sector (Jahangirian et al. 2017). The empirical data presented in paper 3 confirms this and reveals that DES knowledge is confined to experts responsible for designing, developing, and deploying DES models. These experts must frequently balance DES-related activities with tasks that report no direct benefit from a simulation process, but which are nonetheless essential for the use of DES in the design of production systems. For example, data show the reliance on DES specialists for

63

al. 2018). However, recent studies prescribing the activities of a structured production design process do not specify activities related to the use of DES beyond the evaluation of a production system (Andersen et al. 2017).

Findings from this thesis show that the state of practice does not present an improvement over current theoretical understanding. Instead, empirical data from papers 2, 3, and 4 outline the absence of clear activities showing what, how, and when DES should be used during the design of a production system. For example, findings from paper 3 underscore the lack of clear DES-related responsibilities for staff involved in the design of production systems in projects A, B, and C. In addition, the results of paper 3 show the dependency on the continuous enforcement of managers with previous simulation experience for implementing DES in the design of production systems.

Recent publications emphasize the importance of simulation as an enabler of future manufacturing capabilities critical for the design of production systems (Francalanza et al. 2017). However, the findings of paper 3 show the reactive nature of DES use and the absence of theoretical insight at manufacturing companies when supporting decision-making. Prior studies argue that adopting ad hoc practices under scant theoretical insight have led to failed attempts at supporting decisions in the design of production systems (Chryssolouris et al. 2008). Consequently, the findings of this thesis underscore the vulnerability of manufacturing companies when using simulation-based tools for supporting decision-making. In line with earlier research, these findings suggest that manufacturing companies cannot rely exclusively on simulation specialists following best practices advocated by literature (Balci 2012), nor on the increased use of simulation based tools at manufacturing companies (Mourtzis 2019). Instead, the results highlight the need for increased knowledge in production system design including the specification of DES-related activities. For example, joint activities between decision-maker and DES specialists in the formulation of problems, setting of objectives, or scope of models (Robinson 2019).

A fourth finding includes the allocation of resources associated to the use of DES for supporting decision-making in the design of production systems. The use of simulation in the design of production systems is essential for the future of manufacturing and so are the resources facilitating its use (McGinnis and Rose 2017). Yet, studies report a low level of simulation knowledge in organizations, and a scarcity of simulation specialists in the manufacturing sector (Jahangirian et al. 2017). The empirical data presented in paper 3 confirms this and reveals that DES knowledge is confined to experts responsible for designing, developing, and deploying DES models. These experts must frequently balance DES-related activities with tasks that report no direct benefit from a simulation process, but which are nonetheless essential for the use of DES in the design of production systems. For example, data show the reliance on DES specialists for

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promoting the use and interpreting the findings of models as a counter measure to the absence of simulation knowledge in staff responsible for the design of production systems.

The finding above is critical because it suggests the limited improvement in the allocation of resources associated with DES expertise in the design of production systems over a 15 year span (Fowler and Rose 2004). This situation stands in contrast to improvements of technical resources associated with the use of DES in the same period; for example data acquisition (Skoogh et al. 2012; Ivers et al. 2016; Bokrantz et al. 2018). In agreement with prior studies, this thesis contends that failure to specify and manage tasks for staff proficient in the use of DES participating in the design production systems jeopardizes technical improvements towards supporting decision-making (Murphy and Perera 2002). Examples of these tasks include, but are not limited to, promoting knowledge about DES at manufacturing company or across staff responsible for the design of production systems, communicating the results of models to management, and making small scale change to models (e.g., the magnitude of input values).

5.3 REQUIREMENTS IN PRODUCTION SYSTEM DESIGN FOR DES USE

The literature highlights the importance of requirements for reducing equivocality and uncertainty in the design of production systems involving process innovations (Kurkkio et al. 2011). Establishing the requirements for the use of simulation-based technologies, including DES, is essential for their continuous use during the design of production systems (Schluse et al. 2018). The literature underscores the importance of requirements for the use of DES during the design of production systems including the steps for designing, developing, and deploying DES models (Balci 2012; Martin et al. 2018). The results of this thesis provide valuable insight including three requirements that must be included in the design of production systems: analyzing information consensus, specifying the activities of conceptual models, and coordinating DES models with the information needs during production system design. These findings are essential when using DES to reduce the uncertainty and equivocality inherent to process innovations.

The first requirement involves analyzing information in the design of production systems when using DES. Drawing on the Operations Management literature (Hayes et al. 2004) and organization theory (Galbraith 1973), paper 4 identifies the importance of information consensus for reducing equivocality. Paper 4 shows that information consensus is not achieved by a single event, but that active work towards this goal is necessary. Paper 4 proposes that strategic objectives, decision areas, and external and internal fits are essential for achieving information consensus

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promoting the use and interpreting the findings of models as a counter measure to the absence of simulation knowledge in staff responsible for the design of production systems.

The finding above is critical because it suggests the limited improvement in the allocation of resources associated with DES expertise in the design of production systems over a 15 year span (Fowler and Rose 2004). This situation stands in contrast to improvements of technical resources associated with the use of DES in the same period; for example data acquisition (Skoogh et al. 2012; Ivers et al. 2016; Bokrantz et al. 2018). In agreement with prior studies, this thesis contends that failure to specify and manage tasks for staff proficient in the use of DES participating in the design production systems jeopardizes technical improvements towards supporting decision-making (Murphy and Perera 2002). Examples of these tasks include, but are not limited to, promoting knowledge about DES at manufacturing company or across staff responsible for the design of production systems, communicating the results of models to management, and making small scale change to models (e.g., the magnitude of input values).

5.3 REQUIREMENTS IN PRODUCTION SYSTEM DESIGN FOR DES USE

The literature highlights the importance of requirements for reducing equivocality and uncertainty in the design of production systems involving process innovations (Kurkkio et al. 2011). Establishing the requirements for the use of simulation-based technologies, including DES, is essential for their continuous use during the design of production systems (Schluse et al. 2018). The literature underscores the importance of requirements for the use of DES during the design of production systems including the steps for designing, developing, and deploying DES models (Balci 2012; Martin et al. 2018). The results of this thesis provide valuable insight including three requirements that must be included in the design of production systems: analyzing information consensus, specifying the activities of conceptual models, and coordinating DES models with the information needs during production system design. These findings are essential when using DES to reduce the uncertainty and equivocality inherent to process innovations.

The first requirement involves analyzing information in the design of production systems when using DES. Drawing on the Operations Management literature (Hayes et al. 2004) and organization theory (Galbraith 1973), paper 4 identifies the importance of information consensus for reducing equivocality. Paper 4 shows that information consensus is not achieved by a single event, but that active work towards this goal is necessary. Paper 4 proposes that strategic objectives, decision areas, and external and internal fits are essential for achieving information consensus

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during the design of production systems involving process innovations. Strategic objectives (Skinner 1969; Machuca et al. 2011) were used to understand what the process innovation would achieve by specifying the company’s expectations regarding the process innovation. The staff responsible for the design of production systems relied on defining decision areas (Choudhari et al. 2013) to determine what the process innovation would look like. Finally, staff relied on external and internal fits (Nair and Boulton 2008; Slack et al. 2010) to solve these disagreements and determine the most critical issues affecting the operation of the process innovation.

The second requirement constitutes specifying the activities of conceptual models during the design of production systems involving process innovations. The literature shows that conceptual models are crucial for translating a real life problem into a simulation domain when supporting decision-making (Chwif et al. 2013). It is argued that the activities involved in conceptual models are indispensable for identifying the simulation parameters leading to reduction of equivocality and uncertainty (Oberkampf et al. 2002; Haveman and Bonnema 2015). The results of paper 5 suggest that information consensus is essential for reducing equivocality but is insufficient for DES to reduce both equivocality and uncertainty. Contributing to the existing understanding, the results of paper 5 reveal that including strategic objectives, decision areas, and external and internal fits is essential for specifying the activities of a conceptual model. In addition, paper 5 proposes that including strategic objectives, decision areas, and external and internal fits in the activities of conceptual models leads to DES reducing equivocality and uncertainty during production system design. These findings are essential because they help specify activities of conceptual models during the design of production systems involving process innovations. Confirming prior studies (Robinson 2008a), the findings of paper 5 identify the development of conceptual models as critical for enacting dialogue and achieving understanding between staff responsible for the design of production systems and DES specialists.

The third requirement involves coordinating DES models with the information needs of the phases of the production system design. The literature warns against mistaking equivocality and uncertainty, and the information processing activities leading to their reduction (Frishammar et al. 2011). In the past, academics have argued that the use of DES in the design of production systems must align with the information processing needs of manufacturing companies (Eldabi et al. 2002). The results of papers 4, 5, and 6 suggest a clear differentiation between the information process needs of the phases of production system design and DES models. Accordingly, the results suggest that the pre-study phase of production system design presents an opportunity for DES to reduce equivocality. DES models utilized in this phase were characterized by their qualitative nature

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during the design of production systems involving process innovations. Strategic objectives (Skinner 1969; Machuca et al. 2011) were used to understand what the process innovation would achieve by specifying the company’s expectations regarding the process innovation. The staff responsible for the design of production systems relied on defining decision areas (Choudhari et al. 2013) to determine what the process innovation would look like. Finally, staff relied on external and internal fits (Nair and Boulton 2008; Slack et al. 2010) to solve these disagreements and determine the most critical issues affecting the operation of the process innovation.

The second requirement constitutes specifying the activities of conceptual models during the design of production systems involving process innovations. The literature shows that conceptual models are crucial for translating a real life problem into a simulation domain when supporting decision-making (Chwif et al. 2013). It is argued that the activities involved in conceptual models are indispensable for identifying the simulation parameters leading to reduction of equivocality and uncertainty (Oberkampf et al. 2002; Haveman and Bonnema 2015). The results of paper 5 suggest that information consensus is essential for reducing equivocality but is insufficient for DES to reduce both equivocality and uncertainty. Contributing to the existing understanding, the results of paper 5 reveal that including strategic objectives, decision areas, and external and internal fits is essential for specifying the activities of a conceptual model. In addition, paper 5 proposes that including strategic objectives, decision areas, and external and internal fits in the activities of conceptual models leads to DES reducing equivocality and uncertainty during production system design. These findings are essential because they help specify activities of conceptual models during the design of production systems involving process innovations. Confirming prior studies (Robinson 2008a), the findings of paper 5 identify the development of conceptual models as critical for enacting dialogue and achieving understanding between staff responsible for the design of production systems and DES specialists.

The third requirement involves coordinating DES models with the information needs of the phases of the production system design. The literature warns against mistaking equivocality and uncertainty, and the information processing activities leading to their reduction (Frishammar et al. 2011). In the past, academics have argued that the use of DES in the design of production systems must align with the information processing needs of manufacturing companies (Eldabi et al. 2002). The results of papers 4, 5, and 6 suggest a clear differentiation between the information process needs of the phases of production system design and DES models. Accordingly, the results suggest that the pre-study phase of production system design presents an opportunity for DES to reduce equivocality. DES models utilized in this phase were characterized by their qualitative nature

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and focused on the exchange of subjective interpretations, consensus formation, and the enactment of shared understanding. The findings from paper 6 indicate that the conceptual and detailed design phases of the production system design offer an opportunity for DES models to reduce uncertainty. The DES models adopted in these phases included quantitative analyses focused on acquiring information, evaluating the performance of production systems, or the consequences of production process enablers.

5.4 ACTIVITIES IN PRODUCTION SYSTEM DESIGN FACILITATING THE USE OF DES

The literature on the design of production systems identifies the importance of simulation-based tools for supporting decision-making (Ferro et al. 2017; Wiktorsson et al. 2018; Bendul and Blunck 2019). Current understanding of activities related to the use of simulation during the design of production systems is limited to evaluating and testing alternatives (Andersen et al. 2017). This thesis provides three valuable findings describing additional activities in the design of production systems, which may facilitate the use of DES. These findings include activities defining the objectives of DES models, facilitating a structured approach, and the management of resources for the use of DES. These activities include countermeasures to the challenges of DES use for supporting decision-making in the design of production systems, identified in section 5.1.

The first finding includes activities focused on defining the objectives of DES models according to the incidence of equivocality or uncertainty in the design of production systems. Equivocality and uncertainty are critical challenges in the design of production systems involving process innovations (Sjödin et al. 2016). Developing clear activities including the use of DES is essential for supporting decision-making in this context. The literature argues that a first step for adopting DES in the design of production systems involves defining the objectives of the DES models (Law 2015). The empirical results of paper 6 suggest that linking the objectives of a DES model to the information needs of staff is critical for reducing equivocality and uncertainty. The data of papers 3 and 6 indicate that the information processing needs of the pre-study phase correspond to a need for reducing equivocality by generating consensus and that this purpose is best achieved through qualitative based DES models. In addition, the data of papers 2 and 6 suggest that the information processing needs of the conceptual and detailed design phases coincide with a need for reducing uncertainty by increasing information and that quantitative based DES models facilitate this task. This thesis argues for the clear specification of objectives of DES models in the design of production systems in agreement with prior studies (Negahban and Smith 2014).

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and focused on the exchange of subjective interpretations, consensus formation, and the enactment of shared understanding. The findings from paper 6 indicate that the conceptual and detailed design phases of the production system design offer an opportunity for DES models to reduce uncertainty. The DES models adopted in these phases included quantitative analyses focused on acquiring information, evaluating the performance of production systems, or the consequences of production process enablers.

5.4 ACTIVITIES IN PRODUCTION SYSTEM DESIGN FACILITATING THE USE OF DES

The literature on the design of production systems identifies the importance of simulation-based tools for supporting decision-making (Ferro et al. 2017; Wiktorsson et al. 2018; Bendul and Blunck 2019). Current understanding of activities related to the use of simulation during the design of production systems is limited to evaluating and testing alternatives (Andersen et al. 2017). This thesis provides three valuable findings describing additional activities in the design of production systems, which may facilitate the use of DES. These findings include activities defining the objectives of DES models, facilitating a structured approach, and the management of resources for the use of DES. These activities include countermeasures to the challenges of DES use for supporting decision-making in the design of production systems, identified in section 5.1.

The first finding includes activities focused on defining the objectives of DES models according to the incidence of equivocality or uncertainty in the design of production systems. Equivocality and uncertainty are critical challenges in the design of production systems involving process innovations (Sjödin et al. 2016). Developing clear activities including the use of DES is essential for supporting decision-making in this context. The literature argues that a first step for adopting DES in the design of production systems involves defining the objectives of the DES models (Law 2015). The empirical results of paper 6 suggest that linking the objectives of a DES model to the information needs of staff is critical for reducing equivocality and uncertainty. The data of papers 3 and 6 indicate that the information processing needs of the pre-study phase correspond to a need for reducing equivocality by generating consensus and that this purpose is best achieved through qualitative based DES models. In addition, the data of papers 2 and 6 suggest that the information processing needs of the conceptual and detailed design phases coincide with a need for reducing uncertainty by increasing information and that quantitative based DES models facilitate this task. This thesis argues for the clear specification of objectives of DES models in the design of production systems in agreement with prior studies (Negahban and Smith 2014).

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The second finding constitutes including activities facilitating a structured approach to the use of DES during the design of production systems. The DES literature emphasizes the importance of a process for designing, developing, and deploying DES models (Martin et al. 2018). The results of paper 3 project B show that understanding the process leading to a trustworthy model does not secure the use of DES in the design of a production system. Specifically, paper 3 shows that while DES specialists may know how to design, develop, and deploy models, the lack of knowledge about DES from managers and staff may hinder the use of DES. Agreeing with prior studies (Chwif et al. 2013; Robinson 2019), the findings of paper 3 show the importance of conceptual models for achieving new cross-functional insight between DES specialist and managers and staff. Therefore, this thesis proposes the development of conceptual models in each phase of the production system design to facilitate the use of DES for supporting decision-making under uncertainty and equivocality.

Prior research has explored and suggested the use of conceptual models as the most important activity of DES (Chwif et al. 2013). Conceptual models are described as the point of origin for the abstraction of production systems, and the place of return for the continuous iterations of a model to reach an increased level of understanding (Robinson 2008a). The participation of managers and staff responsible for the design of production systems has been encouraged when using DES that includes conceptual modeling (Balci 2012). Yet, reports show that the design, development, and deployment of DES models is largely the responsibility of simulation specialists, which includes conceptual modeling activities (Jahangirian et al. 2017; McGinnis and Rose 2017). The findings in this thesis complement prior studies and suggest that conceptual models should become a recurring point of encounter between DES specialists and staff. Empirical data from this thesis suggest that doing so may help specify both responsibilities and information processing needs during the design of production systems involving process innovations.

The adoption of a conceptual model for the effective use of DES in the design of production systems is essential (Robinson 2019). However, specifying the development of conceptual models may not suffice for reducing equivocality and uncertainty. The findings of paper 5 show that conceptual models are necessary for DES to support decision-making in the design of production systems. Yet, activities of a conceptual model must be informed by strategic objectives, decision areas, and internal and external fits. The findings suggest that doing so provides input to the activities of conceptual models, i.e., understanding a problem, determining modeling objectives, identifying model inputs and outputs, determining the scope of a model, establishing a level of detail, and formulating assumptions and simplifications.

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The second finding constitutes including activities facilitating a structured approach to the use of DES during the design of production systems. The DES literature emphasizes the importance of a process for designing, developing, and deploying DES models (Martin et al. 2018). The results of paper 3 project B show that understanding the process leading to a trustworthy model does not secure the use of DES in the design of a production system. Specifically, paper 3 shows that while DES specialists may know how to design, develop, and deploy models, the lack of knowledge about DES from managers and staff may hinder the use of DES. Agreeing with prior studies (Chwif et al. 2013; Robinson 2019), the findings of paper 3 show the importance of conceptual models for achieving new cross-functional insight between DES specialist and managers and staff. Therefore, this thesis proposes the development of conceptual models in each phase of the production system design to facilitate the use of DES for supporting decision-making under uncertainty and equivocality.

Prior research has explored and suggested the use of conceptual models as the most important activity of DES (Chwif et al. 2013). Conceptual models are described as the point of origin for the abstraction of production systems, and the place of return for the continuous iterations of a model to reach an increased level of understanding (Robinson 2008a). The participation of managers and staff responsible for the design of production systems has been encouraged when using DES that includes conceptual modeling (Balci 2012). Yet, reports show that the design, development, and deployment of DES models is largely the responsibility of simulation specialists, which includes conceptual modeling activities (Jahangirian et al. 2017; McGinnis and Rose 2017). The findings in this thesis complement prior studies and suggest that conceptual models should become a recurring point of encounter between DES specialists and staff. Empirical data from this thesis suggest that doing so may help specify both responsibilities and information processing needs during the design of production systems involving process innovations.

The adoption of a conceptual model for the effective use of DES in the design of production systems is essential (Robinson 2019). However, specifying the development of conceptual models may not suffice for reducing equivocality and uncertainty. The findings of paper 5 show that conceptual models are necessary for DES to support decision-making in the design of production systems. Yet, activities of a conceptual model must be informed by strategic objectives, decision areas, and internal and external fits. The findings suggest that doing so provides input to the activities of conceptual models, i.e., understanding a problem, determining modeling objectives, identifying model inputs and outputs, determining the scope of a model, establishing a level of detail, and formulating assumptions and simplifications.

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The third finding constitutes activities facilitating the management of resources for the use of DES during the design of production systems. Research efforts towards addressing the challenge of resources associated to the use of DES for supporting decision-making focus on increased simulation training (Murphy and Perera 2002). The findings of papers 2, 3, and 6 provide complementary knowledge and identify the phases in the design of production systems in which staff with DES expertise engaged in activities for promoting, communicating, or interpreting the findings of models. In doing so, this thesis offers a direct response towards identifying the location and nature of training needs of the staff during the design of production systems (Zee and Sloot 2018).

Manufacturing companies may not be able to deal with the lack of staff proficient in the use of DES during the design of production systems. However, this thesis argues that activities facilitating the use of DES in the design of production systems could focus on improving the allocation of resources and their responsibilities. The literature highlights the importance of a close interaction between simulation experts and knowledgeable staff when utilizing DES in the design of production systems (Robinson 2017). In the past, the interaction between simulation experts and staff is described as discontinuous (Balci and Ormsby 2007). Papers 3 and 6 underscore the need for a different interaction between simulation experts and staff designing production systems involving process innovations. The data indicate that DES specialists and staff relied on a continuous collaboration necessary for comprehending the implications of process innovations involving the design of multi-product production systems. This thesis proposes a definition of responsibilities for simulation experts, managers responsible for and staff participating in the design of a production system. The empirical data suggest that this definition of responsibilities is dynamic and must adapt to the conditions of the informal start, pre-study, and conceptual and detailed phases of production system design. In relation to resource responsibilities, this thesis proposes that activities facilitating the use of DES include securing DES resources, building trust of DES capabilities, enacting a simulation process (e.g., designing, developing, and deploying DES models), collecting data, validating models, and increasing information sharing.

The discussion above complements current understanding informing the design, development, and deployment of DES models. The results of this thesis shown in papers 2, 3 and 6 underscore the saliency of trustworthy models that are equally valid and verified. In doing so, this thesis confirms the need for formulating a problem, collecting data, documenting assumptions, programing and verifying, making pilot runs, validating results, experimenting, analyzing outputs, and implementing results (Law 2015).

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The third finding constitutes activities facilitating the management of resources for the use of DES during the design of production systems. Research efforts towards addressing the challenge of resources associated to the use of DES for supporting decision-making focus on increased simulation training (Murphy and Perera 2002). The findings of papers 2, 3, and 6 provide complementary knowledge and identify the phases in the design of production systems in which staff with DES expertise engaged in activities for promoting, communicating, or interpreting the findings of models. In doing so, this thesis offers a direct response towards identifying the location and nature of training needs of the staff during the design of production systems (Zee and Sloot 2018).

Manufacturing companies may not be able to deal with the lack of staff proficient in the use of DES during the design of production systems. However, this thesis argues that activities facilitating the use of DES in the design of production systems could focus on improving the allocation of resources and their responsibilities. The literature highlights the importance of a close interaction between simulation experts and knowledgeable staff when utilizing DES in the design of production systems (Robinson 2017). In the past, the interaction between simulation experts and staff is described as discontinuous (Balci and Ormsby 2007). Papers 3 and 6 underscore the need for a different interaction between simulation experts and staff designing production systems involving process innovations. The data indicate that DES specialists and staff relied on a continuous collaboration necessary for comprehending the implications of process innovations involving the design of multi-product production systems. This thesis proposes a definition of responsibilities for simulation experts, managers responsible for and staff participating in the design of a production system. The empirical data suggest that this definition of responsibilities is dynamic and must adapt to the conditions of the informal start, pre-study, and conceptual and detailed phases of production system design. In relation to resource responsibilities, this thesis proposes that activities facilitating the use of DES include securing DES resources, building trust of DES capabilities, enacting a simulation process (e.g., designing, developing, and deploying DES models), collecting data, validating models, and increasing information sharing.

The discussion above complements current understanding informing the design, development, and deployment of DES models. The results of this thesis shown in papers 2, 3 and 6 underscore the saliency of trustworthy models that are equally valid and verified. In doing so, this thesis confirms the need for formulating a problem, collecting data, documenting assumptions, programing and verifying, making pilot runs, validating results, experimenting, analyzing outputs, and implementing results (Law 2015).

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6 PROPOSING A FRAMEWORK This chapter presents a framework for supporting decision making by means of DES in the design of production systems involving process innovations. This framework is based on the analysis presented in Chapter 5.

The purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. Empirical findings indicate that the use of DES for supporting decision-making is neither intuitive nor simple. Above all, the framework clarifies the challenges, conditions of use, requirements and activities for using DES to support decision-making. This framework provides direct implications to managers, staff, and DES specialists responsible for production system design projects including process innovations. Based on this framework, managers can supervise formal activities involving the use of DES in the design of production systems. In addition, staff and DES specialists can anticipate and respond to the presence of equivocality and uncertainty inherent to process innovations.

The findings of this research reveal that many of the challenges hindering the use of DES for supporting decision-making, such as a lack of consensus and understanding (equivocality), the absence of information (uncertainty), and resources facilitating the use of DES. These challenges could arguably be decreased by specifying the use of DES during the design of production systems. Accordingly, the framework elaborates on current understanding of production system design, namely its multiple phases and procedural nature. Specifically, the framework encourages the adoption of the informal start, pre-study, conceptual and detailed design for the use of DES.

The Informal Start Phase

This research showed that a challenge undermining the use of DES for supporting decision-making includes the limited specialist resources. Therefore, manufacturing companies may benefit from including DES specialists as members of cross functional teams responsible for the design of production systems, and securing their participating in all phases of production system design. In addition, the results of this research suggest the need for promoting understanding of DES use. Manufacturing companies may benefit from assigning the responsibility of promoting understating of DES in the informal start phase. Literature indicates that this phase involves discussions focused on reaching an overall objective, identifying an objective and determining a long-term view of the implemented production system.

The findings of this research identified three activities promoting the use of DES. First, exemplifying DES models and their capacity to analyze entities

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6 PROPOSING A FRAMEWORK This chapter presents a framework for supporting decision making by means of DES in the design of production systems involving process innovations. This framework is based on the analysis presented in Chapter 5.

The purpose of this thesis is to support decision-making by means of DES in the design of production systems involving process innovations. Empirical findings indicate that the use of DES for supporting decision-making is neither intuitive nor simple. Above all, the framework clarifies the challenges, conditions of use, requirements and activities for using DES to support decision-making. This framework provides direct implications to managers, staff, and DES specialists responsible for production system design projects including process innovations. Based on this framework, managers can supervise formal activities involving the use of DES in the design of production systems. In addition, staff and DES specialists can anticipate and respond to the presence of equivocality and uncertainty inherent to process innovations.

The findings of this research reveal that many of the challenges hindering the use of DES for supporting decision-making, such as a lack of consensus and understanding (equivocality), the absence of information (uncertainty), and resources facilitating the use of DES. These challenges could arguably be decreased by specifying the use of DES during the design of production systems. Accordingly, the framework elaborates on current understanding of production system design, namely its multiple phases and procedural nature. Specifically, the framework encourages the adoption of the informal start, pre-study, conceptual and detailed design for the use of DES.

The Informal Start Phase

This research showed that a challenge undermining the use of DES for supporting decision-making includes the limited specialist resources. Therefore, manufacturing companies may benefit from including DES specialists as members of cross functional teams responsible for the design of production systems, and securing their participating in all phases of production system design. In addition, the results of this research suggest the need for promoting understanding of DES use. Manufacturing companies may benefit from assigning the responsibility of promoting understating of DES in the informal start phase. Literature indicates that this phase involves discussions focused on reaching an overall objective, identifying an objective and determining a long-term view of the implemented production system.

The findings of this research identified three activities promoting the use of DES. First, exemplifying DES models and their capacity to analyze entities

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and their processing through a series of stages in a production system. Second, scoping the use of DES for supporting decisions including capacity, resource utilization, or throughput. Third, specifying the qualitative and quantitative outputs produced by DES models, and the process of designing, developing, and deploying DES models.

Taken together, these activities may facilitate comprehending the capabilities and limitations of DES for supporting decision-making. Clearly, these activities are not meant to produce DES specialists during the design of a production system. However, these activities focus on providing basic knowledge for staff to identify potential decisions requiring DES support. Additionally, the framework encourages a proactive use of DES for supporting decision-making in the design of production systems involving process innovations. Therefore, the framework encourages managers responsible for the design of production systems and DES specialists to plan jointly DES related activities such as the identifying needs and formulating problems including all members of a production system design project.

The Pre-Study Phase

This research identified equivocality as the salient challenge undermining the use of DES for supporting decision-making in the pre-study phase. The results of this research indicated that equivocality in the pre-study phase is crucial when the design of production systems involve process innovations. Equivocality refers to the existence of multiple or conflicting interpretations across project participants. The findings of this research suggested that the use of DES facilitate the reduction of equivocality inherent to process innovations. For example, production system design teams, including managers and staff, benefit from the use of DES as models facilitate the exchange of subjective views, consensus, and agreement necessary to reduce equivocality. Accordingly, the framework proposes that all DES related activities of the pre-study phase be aligned to the reduction of equivocality.

The framework proposes that a first step involves identifying the conditions of use of DES for supporting decision-making in the pre-study phase. This research showed that the conditions of use of DES under equivocality correspond to ill-structured decisions. Staff encountering ill-structured decisions are subject to multiple and conflicting interpretations and the absence of objective or rule-based procedures. The framework recommends that staff facing ill-structured decisions use DES for qualitative-based models focused on generating consensus. Qualitative-based DES models may give precedence to the visual representation of the production system involving process innovations over quantitative based analyses. Clearly, DES may not respond to all decisions needed to design production systems involving process innovations. Therefore, DES

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and their processing through a series of stages in a production system. Second, scoping the use of DES for supporting decisions including capacity, resource utilization, or throughput. Third, specifying the qualitative and quantitative outputs produced by DES models, and the process of designing, developing, and deploying DES models.

Taken together, these activities may facilitate comprehending the capabilities and limitations of DES for supporting decision-making. Clearly, these activities are not meant to produce DES specialists during the design of a production system. However, these activities focus on providing basic knowledge for staff to identify potential decisions requiring DES support. Additionally, the framework encourages a proactive use of DES for supporting decision-making in the design of production systems involving process innovations. Therefore, the framework encourages managers responsible for the design of production systems and DES specialists to plan jointly DES related activities such as the identifying needs and formulating problems including all members of a production system design project.

The Pre-Study Phase

This research identified equivocality as the salient challenge undermining the use of DES for supporting decision-making in the pre-study phase. The results of this research indicated that equivocality in the pre-study phase is crucial when the design of production systems involve process innovations. Equivocality refers to the existence of multiple or conflicting interpretations across project participants. The findings of this research suggested that the use of DES facilitate the reduction of equivocality inherent to process innovations. For example, production system design teams, including managers and staff, benefit from the use of DES as models facilitate the exchange of subjective views, consensus, and agreement necessary to reduce equivocality. Accordingly, the framework proposes that all DES related activities of the pre-study phase be aligned to the reduction of equivocality.

The framework proposes that a first step involves identifying the conditions of use of DES for supporting decision-making in the pre-study phase. This research showed that the conditions of use of DES under equivocality correspond to ill-structured decisions. Staff encountering ill-structured decisions are subject to multiple and conflicting interpretations and the absence of objective or rule-based procedures. The framework recommends that staff facing ill-structured decisions use DES for qualitative-based models focused on generating consensus. Qualitative-based DES models may give precedence to the visual representation of the production system involving process innovations over quantitative based analyses. Clearly, DES may not respond to all decisions needed to design production systems involving process innovations. Therefore, DES

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specialists and production system design teams are likely to benefit from scoping the use of DES to decisions which may be modeled as queuing systems. For example, this research discussed the implications of a qualitative-based model, which gave precedence to visually presenting alternative layouts for a multi-product production system.

The results of this research showed that including requirements to the use of DES help support decision-making during the design of production systems involving process innovations. This research identified that adopting strategic objectives, decision areas, internal and external fit is an essential requirement for the use of DES to reduce equivocality in the pre-study phase. Identifying strategic decisions helped understand what the process innovation would achieve by specifying the company’s expectations regarding the process innovation. Determining decision areas facilitated defining what the process innovation would look like. Internal fit addressed concerns about how the process innovation would operate. External fit targeted concerns related to external settings (i.e., market changes affecting the assembly of products). This research showed that defining strategic objectives, decision areas, internal and external fits is necessary to generate information consensus, but insufficient to use DES.

This research identified as an additional requirement the use of conceptual models to transfer information consensus to DES during production system design involving process innovations. This research confirmed the importance of conceptual models as the point of origin for the abstraction of a designed production system, and the place of return for iterations of a DES model to reach an increased level of understanding. Therefore, the framework adopts the conceptual model as means for transferring information consensus to DES during production system design. Production system design teams and DES specialists may benefit from utilizing strategic objectives, decision areas, internal and external fit as inputs to the activities of a conceptual model. Accordingly, the framework suggests a shared responsibility of staff and DES specialists in the development of conceptual models. Importantly, the framework suggests that DES specialists take responsibility for the design, develop, and deploy DES models. At the same time, the framework encourages the continuous participation of all members of production system design teams in the design, develop, and deploy DES models. For example, the input of staff may be crucial to gain understanding, share concerns, or test ideas essential for the use of DES to support decision-making under equivocality in the pre-study phase.

The Conceptual Design Phase

This research indicates that uncertainty is the salient challenge undermining the use of the use of DES for supporting decision-making in the conceptual design phase. The results of this research argue that uncertainty, in addition

71

specialists and production system design teams are likely to benefit from scoping the use of DES to decisions which may be modeled as queuing systems. For example, this research discussed the implications of a qualitative-based model, which gave precedence to visually presenting alternative layouts for a multi-product production system.

The results of this research showed that including requirements to the use of DES help support decision-making during the design of production systems involving process innovations. This research identified that adopting strategic objectives, decision areas, internal and external fit is an essential requirement for the use of DES to reduce equivocality in the pre-study phase. Identifying strategic decisions helped understand what the process innovation would achieve by specifying the company’s expectations regarding the process innovation. Determining decision areas facilitated defining what the process innovation would look like. Internal fit addressed concerns about how the process innovation would operate. External fit targeted concerns related to external settings (i.e., market changes affecting the assembly of products). This research showed that defining strategic objectives, decision areas, internal and external fits is necessary to generate information consensus, but insufficient to use DES.

This research identified as an additional requirement the use of conceptual models to transfer information consensus to DES during production system design involving process innovations. This research confirmed the importance of conceptual models as the point of origin for the abstraction of a designed production system, and the place of return for iterations of a DES model to reach an increased level of understanding. Therefore, the framework adopts the conceptual model as means for transferring information consensus to DES during production system design. Production system design teams and DES specialists may benefit from utilizing strategic objectives, decision areas, internal and external fit as inputs to the activities of a conceptual model. Accordingly, the framework suggests a shared responsibility of staff and DES specialists in the development of conceptual models. Importantly, the framework suggests that DES specialists take responsibility for the design, develop, and deploy DES models. At the same time, the framework encourages the continuous participation of all members of production system design teams in the design, develop, and deploy DES models. For example, the input of staff may be crucial to gain understanding, share concerns, or test ideas essential for the use of DES to support decision-making under equivocality in the pre-study phase.

The Conceptual Design Phase

This research indicates that uncertainty is the salient challenge undermining the use of the use of DES for supporting decision-making in the conceptual design phase. The results of this research argue that uncertainty, in addition

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to equivocality, is critical when designing production systems involving process innovations. Uncertainty concerns the lack of information necessary to complete a task. Literature advocates the use of DES for reducing uncertainty in the design of production systems. The results of this research confirmed this, and showed that the use of DES is particularly valuable in a context involving process innovations. For example, production system design teams improved understanding about the operational performance of machines, buffers, resources, or assembly stations, and their interrelation during the design of production systems. The framework proposes the alignment of all DES related activities in the conceptual design phase to reduce uncertainty.

The framework suggests identifying the conditions of use of DES for supporting decision-making in the conceptual design phase. This research showed that production system design teams adopted DES under well-structured decisions in the conceptual design phase. These decisions included non-readily solvable decisions where intuition can enact shared understanding, but normative-decision making is needed for quantitative insight. The framework recommends that design teams facing well-structured decisions use DES for quantitative-based models focused on increasing information. Production system design teams may benefit from quantitative-based DES models through increased understanding necessary for the evaluation of decisions by statistical means. This research exemplified quantitative-based DES models in the conceptual design phase such as those utilized for determining advantages and trade-offs of multi-product production system.

The framework proposes the continued use of requirements for the use of DES identified in the pre-study phase at the conceptual design phase. Therefore, the framework underscores the importance of defining strategic objectives, decision areas, internal and external fit, and including these as inputs to conceptual models of the conceptual design phase. The results of this research showed that the use of DES for reducing uncertainty is subject data availability, accurate parameters for input values, and specifying the interrelation of subsystems in a production process. Therefore, the framework emphasizes the importance of identifying additional information which further details the strategic objectives, decision areas, internal and external fit. This information can then be transferred to conceptual models for the revision of previously developed DES models or the design, development, and deployment of new quantitative-based ones. The findings of this research showed that the use of DES for supporting decision-making in the design of a production system is an iterative process. The iterative nature of DES use is further increased when designing production systems involving process innovations. In addition, the results of this research suggested that the continuous exchange of information between production system design teams and DES specialists is essential to fulfill the benefits

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to equivocality, is critical when designing production systems involving process innovations. Uncertainty concerns the lack of information necessary to complete a task. Literature advocates the use of DES for reducing uncertainty in the design of production systems. The results of this research confirmed this, and showed that the use of DES is particularly valuable in a context involving process innovations. For example, production system design teams improved understanding about the operational performance of machines, buffers, resources, or assembly stations, and their interrelation during the design of production systems. The framework proposes the alignment of all DES related activities in the conceptual design phase to reduce uncertainty.

The framework suggests identifying the conditions of use of DES for supporting decision-making in the conceptual design phase. This research showed that production system design teams adopted DES under well-structured decisions in the conceptual design phase. These decisions included non-readily solvable decisions where intuition can enact shared understanding, but normative-decision making is needed for quantitative insight. The framework recommends that design teams facing well-structured decisions use DES for quantitative-based models focused on increasing information. Production system design teams may benefit from quantitative-based DES models through increased understanding necessary for the evaluation of decisions by statistical means. This research exemplified quantitative-based DES models in the conceptual design phase such as those utilized for determining advantages and trade-offs of multi-product production system.

The framework proposes the continued use of requirements for the use of DES identified in the pre-study phase at the conceptual design phase. Therefore, the framework underscores the importance of defining strategic objectives, decision areas, internal and external fit, and including these as inputs to conceptual models of the conceptual design phase. The results of this research showed that the use of DES for reducing uncertainty is subject data availability, accurate parameters for input values, and specifying the interrelation of subsystems in a production process. Therefore, the framework emphasizes the importance of identifying additional information which further details the strategic objectives, decision areas, internal and external fit. This information can then be transferred to conceptual models for the revision of previously developed DES models or the design, development, and deployment of new quantitative-based ones. The findings of this research showed that the use of DES for supporting decision-making in the design of a production system is an iterative process. The iterative nature of DES use is further increased when designing production systems involving process innovations. In addition, the results of this research suggested that the continuous exchange of information between production system design teams and DES specialists is essential to fulfill the benefits

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for process innovations when the use of DES for supporting decision-making. Therefore, the framework recommends the continuous communication, and revision of all DES models as new insight is uncovered in the conceptual design phase.

The Detailed Design Phase

This research established uncertainty as the decisive challenge undermining the use of DES for supporting decision-making in the detailed design phase. This research identified that uncertainty in the detailed design phase compromises the specification and selection of the most suitable production system design concepts. Accordingly, production system design teams who fail to reduce uncertainty risk committing to erroneous choices that leave the benefits of process innovations unfulfilled. This research presented empirical results showing how the use of DES support decision-making in the design of production systems involving process innovations. On this basis, production system design teams may benefit from the use of DES for supporting decision-making in the detailed phase of production system design.

Paper 1 showed that the conditions of use of DES under uncertainty in the detailed design phase are distinct from those of the conceptual phase. Namely, production system design teams used DES under low equivocality and high analyzability in the detailed design phase. Production system design teams may face difficulties in identifying the decisions suitable to the use of DES. The framework facilitates identifying the conditions of use of DES in the design of production systems involving process innovations based on the structuredness of decisions. Based on the results of paper 1, the framework suggests that the conditions of use of DES include two situations. First, detailed technical decisions requiring sequential analysis of well-defined situations and explicit information. For example, evaluating buffers sizes or calculating the capacity of a production systems according to the existing practice of a manufacturing company. Second, high stake decisions or decisions different from those encountered in the past involving the aggregation of activities and managerial involvement. For example, comparing the performance of an existing production system to that of a multi-product production one.

The framework suggests the continued use of requirements identified in previous phases, namely strategic objectives, decision areas, internal and external fit and conceptual models. However, the framework underscores the need for activities leading to improved data quality that correspond to the specification of a production system design solution. The framework proposes that production system design teams may benefit from three activities facilitating the use of DES for supporting decision-making in the detailed design phase. First, increasing data quality for DES model input. This activity is essential for producing model trustworthy and statistically

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for process innovations when the use of DES for supporting decision-making. Therefore, the framework recommends the continuous communication, and revision of all DES models as new insight is uncovered in the conceptual design phase.

The Detailed Design Phase

This research established uncertainty as the decisive challenge undermining the use of DES for supporting decision-making in the detailed design phase. This research identified that uncertainty in the detailed design phase compromises the specification and selection of the most suitable production system design concepts. Accordingly, production system design teams who fail to reduce uncertainty risk committing to erroneous choices that leave the benefits of process innovations unfulfilled. This research presented empirical results showing how the use of DES support decision-making in the design of production systems involving process innovations. On this basis, production system design teams may benefit from the use of DES for supporting decision-making in the detailed phase of production system design.

Paper 1 showed that the conditions of use of DES under uncertainty in the detailed design phase are distinct from those of the conceptual phase. Namely, production system design teams used DES under low equivocality and high analyzability in the detailed design phase. Production system design teams may face difficulties in identifying the decisions suitable to the use of DES. The framework facilitates identifying the conditions of use of DES in the design of production systems involving process innovations based on the structuredness of decisions. Based on the results of paper 1, the framework suggests that the conditions of use of DES include two situations. First, detailed technical decisions requiring sequential analysis of well-defined situations and explicit information. For example, evaluating buffers sizes or calculating the capacity of a production systems according to the existing practice of a manufacturing company. Second, high stake decisions or decisions different from those encountered in the past involving the aggregation of activities and managerial involvement. For example, comparing the performance of an existing production system to that of a multi-product production one.

The framework suggests the continued use of requirements identified in previous phases, namely strategic objectives, decision areas, internal and external fit and conceptual models. However, the framework underscores the need for activities leading to improved data quality that correspond to the specification of a production system design solution. The framework proposes that production system design teams may benefit from three activities facilitating the use of DES for supporting decision-making in the detailed design phase. First, increasing data quality for DES model input. This activity is essential for producing model trustworthy and statistically

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valid DES model outputs. Second, evaluating the performance of the designed production system including capacity, resource utilization, throughput, or other performance metrics by means of DES. This activity is necessary for evaluating the designed production system and analyzing the fulfillment of benefits of process innovations. Third, communicating the results of DES models supporting decision-making in the design of production systems. The framework has emphasized the importance of communicating the results of DES models within a production system design team. However, managers responsible for the design of production systems must anticipate the need for communicating the results of DES models to individuals in the organization who do not participate directly in the design of a production system. Importantly, managers responsible for the design of production systems must take into account the dissemination and documentation of the results of DES models. Figure 6.1 presents the framework supporting decision making by means of DES in the design of production systems involving process innovations.

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valid DES model outputs. Second, evaluating the performance of the designed production system including capacity, resource utilization, throughput, or other performance metrics by means of DES. This activity is necessary for evaluating the designed production system and analyzing the fulfillment of benefits of process innovations. Third, communicating the results of DES models supporting decision-making in the design of production systems. The framework has emphasized the importance of communicating the results of DES models within a production system design team. However, managers responsible for the design of production systems must anticipate the need for communicating the results of DES models to individuals in the organization who do not participate directly in the design of a production system. Importantly, managers responsible for the design of production systems must take into account the dissemination and documentation of the results of DES models. Figure 6.1 presents the framework supporting decision making by means of DES in the design of production systems involving process innovations.

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7 CONCLUSION This chapter presents the conclusions of this thesis. The chapter outlines the contributions to theory, and the implications to practice. Finally the chapter presents the limitations of this thesis and future venues for research.

7.1 CONTRIBUTION TO THEORY Researchers recognize process innovations as critical determinants for sustained manufacturing competitiveness (Sjödin 2019). Studies underscore the importance of production system design to ensure the actual fulfillment of process innovations at manufacturing companies (Bellgran and Säfsten 2010). Against this backdrop, the literature emphasizes the need for the increased use of simulation-based techniques, such as DES, for supporting decision-making in this context. However, problems inherent to process innovations including uncertainty and equivocality call into question whether current understanding about the conditions of use, challenges, activities, and requirements of DES are sufficient for supporting decision-making in the design of production systems. Therefore, the purpose of this thesis was to support decision-making by means of DES in the design of production systems involving process innovations.

This research extended current understanding about the design of production systems involving process innovations by drawing on organization theory, and extant literature on production system design and DES. This research presents five contributions to the current understanding of supporting decision-making in the field of production system design by means of DES.

The first contribution includes extending current understanding about decision structuredness, and its importance for clarifying the selection of decision-making approaches leading to an improved outcome. Importantly, this research suggested that decision structuredness determines the conditions of use of DES for supporting decision-making during the design of production systems. This situation is crucial for implementing process innovations (Frishammar et al. 2011). This research showed that, consistent with the literature, well-structured and ill-structured decisions corresponded to normative and intuitive decision-making, respectively (Julmi 2019). However, this research showed differences with prior studies focused on decision structuredness and decision-making. The results of this study suggest that decision structuredness may not prescribe a decision-making approach but may clarify the conditions in which decisions take place. This research outlined the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions. This finding is essential as it suggests that identifying the fit of a decision-making approach to the structuredness of a problem is as important as the technical acumen, resources, and experience

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7 CONCLUSION This chapter presents the conclusions of this thesis. The chapter outlines the contributions to theory, and the implications to practice. Finally the chapter presents the limitations of this thesis and future venues for research.

7.1 CONTRIBUTION TO THEORY Researchers recognize process innovations as critical determinants for sustained manufacturing competitiveness (Sjödin 2019). Studies underscore the importance of production system design to ensure the actual fulfillment of process innovations at manufacturing companies (Bellgran and Säfsten 2010). Against this backdrop, the literature emphasizes the need for the increased use of simulation-based techniques, such as DES, for supporting decision-making in this context. However, problems inherent to process innovations including uncertainty and equivocality call into question whether current understanding about the conditions of use, challenges, activities, and requirements of DES are sufficient for supporting decision-making in the design of production systems. Therefore, the purpose of this thesis was to support decision-making by means of DES in the design of production systems involving process innovations.

This research extended current understanding about the design of production systems involving process innovations by drawing on organization theory, and extant literature on production system design and DES. This research presents five contributions to the current understanding of supporting decision-making in the field of production system design by means of DES.

The first contribution includes extending current understanding about decision structuredness, and its importance for clarifying the selection of decision-making approaches leading to an improved outcome. Importantly, this research suggested that decision structuredness determines the conditions of use of DES for supporting decision-making during the design of production systems. This situation is crucial for implementing process innovations (Frishammar et al. 2011). This research showed that, consistent with the literature, well-structured and ill-structured decisions corresponded to normative and intuitive decision-making, respectively (Julmi 2019). However, this research showed differences with prior studies focused on decision structuredness and decision-making. The results of this study suggest that decision structuredness may not prescribe a decision-making approach but may clarify the conditions in which decisions take place. This research outlined the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions. This finding is essential as it suggests that identifying the fit of a decision-making approach to the structuredness of a problem is as important as the technical acumen, resources, and experience

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necessary for using a particular type of decision-making (Dean and Sharfman 1996; Jonassen 2012). This contribution originates from the findings of this research related to research question 1.

The second contribution consists of a novel insight about the challenges of applying DES for supporting decision-making during the design of production systems. The findings of this research provided empirical insight into the importance of equivocality, which has been previously reported when studying the introduction of new production process or technologies but has received limited attention otherwise in the DES literature. In addition, this research reported findings that are additional to those previously reported in literature. The severity of this issue was increased by the high levels of uncertainty related to the probability that certain assumptions made during design were incorrect or to the presence of entirely unknown facts that have a bearing on the designed production system (Frishammar et al. 2011). This research demonstrated that specifying the interrelation of subsystems in a production process might be just as important as specifying accurate parameters for input values in DES models. This contribution originates from the findings of this research related to research question 2.

A third contribution involves transferring strategic objectives, decision areas, and external and internal fit from operations management literature to a process innovation context. In the past, strategic objectives, decision areas, and external and internal fit have guided literature in selecting the manufacturing strategy of companies (Choudhari et al. 2010). This research reveals that including these four concepts during the design of production systems when implementing process innovations is crucial for guiding information consensus in two ways. Firstly, this research confirms the importance of strategic objectives, decision areas, and external and internal fit underscored by prior studies (Soosay et al. 2016). Secondly, this research showed that strategic objectives, decision areas, and external and internal fits are essential for achieving information consensus during the design of production systems involving process innovations. This research revealed that understanding information consensus is complementary to current efforts focused on the reduction of equivocality and uncertainty inherent to process innovations (Eriksson et al. 2016; Sjödin et al. 2016). This contribution arises from the findings of this research related to research question 3.

The fourth contribution concerns extending current understanding of conceptual models from DES literature. This research confirmed the importance of conceptual models as a point of origin for the abstraction of a real or proposed systems (Robinson 2008a). In addition, this research showed that conceptual models are essential constitutes for the use of DES for supporting-decision making during the design of production systems involving process innovations. For example, this research identified that conceptual models facilitate the comprehension about the agreement and

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necessary for using a particular type of decision-making (Dean and Sharfman 1996; Jonassen 2012). This contribution originates from the findings of this research related to research question 1.

The second contribution consists of a novel insight about the challenges of applying DES for supporting decision-making during the design of production systems. The findings of this research provided empirical insight into the importance of equivocality, which has been previously reported when studying the introduction of new production process or technologies but has received limited attention otherwise in the DES literature. In addition, this research reported findings that are additional to those previously reported in literature. The severity of this issue was increased by the high levels of uncertainty related to the probability that certain assumptions made during design were incorrect or to the presence of entirely unknown facts that have a bearing on the designed production system (Frishammar et al. 2011). This research demonstrated that specifying the interrelation of subsystems in a production process might be just as important as specifying accurate parameters for input values in DES models. This contribution originates from the findings of this research related to research question 2.

A third contribution involves transferring strategic objectives, decision areas, and external and internal fit from operations management literature to a process innovation context. In the past, strategic objectives, decision areas, and external and internal fit have guided literature in selecting the manufacturing strategy of companies (Choudhari et al. 2010). This research reveals that including these four concepts during the design of production systems when implementing process innovations is crucial for guiding information consensus in two ways. Firstly, this research confirms the importance of strategic objectives, decision areas, and external and internal fit underscored by prior studies (Soosay et al. 2016). Secondly, this research showed that strategic objectives, decision areas, and external and internal fits are essential for achieving information consensus during the design of production systems involving process innovations. This research revealed that understanding information consensus is complementary to current efforts focused on the reduction of equivocality and uncertainty inherent to process innovations (Eriksson et al. 2016; Sjödin et al. 2016). This contribution arises from the findings of this research related to research question 3.

The fourth contribution concerns extending current understanding of conceptual models from DES literature. This research confirmed the importance of conceptual models as a point of origin for the abstraction of a real or proposed systems (Robinson 2008a). In addition, this research showed that conceptual models are essential constitutes for the use of DES for supporting-decision making during the design of production systems involving process innovations. For example, this research identified that conceptual models facilitate the comprehension about the agreement and

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acquisition of the information necessary to manage high levels of uncertainty prior to the assignment of model inputs. Furthermore, this research showed that conceptual models are critical for the continuous iterations of a model to reach an increased level of understanding during the design of production systems, and that adopting conceptual models facilitates decreasing the degree of equivocality and uncertainty. This contribution stems from the findings of this research related to research question 4.

The fifth contribution of this research refers to the use of DES for supporting decision-making in the design of production systems involving process innovations. The literature presents differing perspectives to the use of DES for supporting decision-making in the design of production systems. On the one hand, the use of DES may include the use of DES as a one-time exercise during production system design (Negahban and Smith 2014). Here DES supports long-term production system design decisions such as facility layout and system capacity when designing facilities, material handling areas, production cells, and flexible production systems. On the other hand, DES use is proposed as a continuum throughout a production system’s lifecycle including its design (Mourtzis 2019). From this perspective, the use of DES facilitates identifying the consequences of choices and adjusting these choices for an improved performance outcome. This research has argued for the continuous use of DES during the design of production systems. Importantly, this research showed that manufacturing companies adopting the continuous use of DES during production system design benefit from not only qualitative analysis, but also that they can be a powerful means of sharing concerns, testing ideas, and reaching consensus, which is essential when involving process innovations. This contribution originates from the findings of this research related to research question 4.

7.2 IMPLICATIONS TO PRACTICE The results of this research stem from collaborative research performed at a manufacturing company in the heavy-vehicle industry. This research is focused on the design of production systems involving process innovations and can assist companies in using DES for supporting decision-making in the design of such production systems.

The findings of this research are particularly relevant to industrial practice, and identify the conditions of use, challenges, requirements, and activities (not previously reported in the literature) essential for the utilization of DES during production system design. In addition, this research presents a framework providing direct benefits to manufacturing companies designing production systems that include process innovations. Based on this framework, manufacturing companies can supervise and anticipate activities involving the use of DES in the design of production systems. In

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acquisition of the information necessary to manage high levels of uncertainty prior to the assignment of model inputs. Furthermore, this research showed that conceptual models are critical for the continuous iterations of a model to reach an increased level of understanding during the design of production systems, and that adopting conceptual models facilitates decreasing the degree of equivocality and uncertainty. This contribution stems from the findings of this research related to research question 4.

The fifth contribution of this research refers to the use of DES for supporting decision-making in the design of production systems involving process innovations. The literature presents differing perspectives to the use of DES for supporting decision-making in the design of production systems. On the one hand, the use of DES may include the use of DES as a one-time exercise during production system design (Negahban and Smith 2014). Here DES supports long-term production system design decisions such as facility layout and system capacity when designing facilities, material handling areas, production cells, and flexible production systems. On the other hand, DES use is proposed as a continuum throughout a production system’s lifecycle including its design (Mourtzis 2019). From this perspective, the use of DES facilitates identifying the consequences of choices and adjusting these choices for an improved performance outcome. This research has argued for the continuous use of DES during the design of production systems. Importantly, this research showed that manufacturing companies adopting the continuous use of DES during production system design benefit from not only qualitative analysis, but also that they can be a powerful means of sharing concerns, testing ideas, and reaching consensus, which is essential when involving process innovations. This contribution originates from the findings of this research related to research question 4.

7.2 IMPLICATIONS TO PRACTICE The results of this research stem from collaborative research performed at a manufacturing company in the heavy-vehicle industry. This research is focused on the design of production systems involving process innovations and can assist companies in using DES for supporting decision-making in the design of such production systems.

The findings of this research are particularly relevant to industrial practice, and identify the conditions of use, challenges, requirements, and activities (not previously reported in the literature) essential for the utilization of DES during production system design. In addition, this research presents a framework providing direct benefits to manufacturing companies designing production systems that include process innovations. Based on this framework, manufacturing companies can supervise and anticipate activities involving the use of DES in the design of production systems. In

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addition, manufacturing companies can respond to the presence of equivocality and uncertainty inherent to process innovations.

A common way of working in the design of production systems involves regarding the use of DES as one more activity to create a production system. The findings of this research argue that a key to supporting decision-making involving process innovations is the ability of manufacturing companies to use DES continuously during production system design. The results of Chapter 5 argue that to achieve the benefits of DES use, manufacturing companies need to adapt the use of DES to the information processing needs of the phases of production system design. In addition, the findings of this research highlighted the need for the continuous interaction between production system design teams and DES specialists. Importantly, the findings emphasized the need for shared responsibilities when adopting DES for supporting decision-making in the design of production systems involving process innovations. This research argued that the responsibility of DES use for supporting decision-making could not fall exclusively on individuals with knowledge about designing, developing, and deploying DES models.

The present results shows that it is important that manufacturing companies realize that understanding DES can no longer be limited to the skill set of simulation specialists. Instead, the findings of this research suggest that manufacturing companies with production system design teams experienced in the use of DES benefit from increased insight during the design of production systems. The findings of this research give credibility to the claim that comprehension about DES use harnesses a competitive edge for manufacturing companies implementing process innovations (Sjödin 2019). Accordingly, this research agrees with prior findings calling for increased proficiency in the use of simulation-based techniques, including DES, as a basic skill set for increasing competitiveness of manufacturing companies (Fu et al. 2014; Konrad 2018; Zee and Sloot 2018).

7.3 LIMITATIONS AND FUTURE RESEARCH This research presents novel contributions to supporting decision-making in the design of production systems involving process innovations. Yet, all research is subject to limitations and this thesis is no exception. Three limitations are worth mentioning.

First, the case study method provides a holistic understanding of how and why events occur and allow studying events as these unfold. However, the generalization of findings needs to be approached cautiously. This research was carried out at one manufacturing company in the heavy vehicle industry. Five characteristics defined the manufacturing company including: its global manufacturing footprint, high capital intensity, established

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addition, manufacturing companies can respond to the presence of equivocality and uncertainty inherent to process innovations.

A common way of working in the design of production systems involves regarding the use of DES as one more activity to create a production system. The findings of this research argue that a key to supporting decision-making involving process innovations is the ability of manufacturing companies to use DES continuously during production system design. The results of Chapter 5 argue that to achieve the benefits of DES use, manufacturing companies need to adapt the use of DES to the information processing needs of the phases of production system design. In addition, the findings of this research highlighted the need for the continuous interaction between production system design teams and DES specialists. Importantly, the findings emphasized the need for shared responsibilities when adopting DES for supporting decision-making in the design of production systems involving process innovations. This research argued that the responsibility of DES use for supporting decision-making could not fall exclusively on individuals with knowledge about designing, developing, and deploying DES models.

The present results shows that it is important that manufacturing companies realize that understanding DES can no longer be limited to the skill set of simulation specialists. Instead, the findings of this research suggest that manufacturing companies with production system design teams experienced in the use of DES benefit from increased insight during the design of production systems. The findings of this research give credibility to the claim that comprehension about DES use harnesses a competitive edge for manufacturing companies implementing process innovations (Sjödin 2019). Accordingly, this research agrees with prior findings calling for increased proficiency in the use of simulation-based techniques, including DES, as a basic skill set for increasing competitiveness of manufacturing companies (Fu et al. 2014; Konrad 2018; Zee and Sloot 2018).

7.3 LIMITATIONS AND FUTURE RESEARCH This research presents novel contributions to supporting decision-making in the design of production systems involving process innovations. Yet, all research is subject to limitations and this thesis is no exception. Three limitations are worth mentioning.

First, the case study method provides a holistic understanding of how and why events occur and allow studying events as these unfold. However, the generalization of findings needs to be approached cautiously. This research was carried out at one manufacturing company in the heavy vehicle industry. Five characteristics defined the manufacturing company including: its global manufacturing footprint, high capital intensity, established

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processes for developing production systems, continual design of new products, and an emphasis on increasing flexibility of production systems. Future research may focus on production system design projects with different variables relating to, e.g., company size, industry type, product type, and type of production technology or process. Therefore, additional research is necessary to test and validate the findings of this research. Future studies could focus on different industrial segments. In addition, further research could benefit from complementary research methods such as surveys, which could provide statistical validity to confirm or adjust the findings presented in this research.

Second, further research is also required concerning the activities in the design of production systems facilitating the use of DES. This research did not investigate the precedence of activities leading to an improved outcome. For example, do all concepts of a decision perspective hold an equal significance in the development of conceptual models? Accordingly, future research could investigate the saliency of one of the concepts of strategic objectives, decision areas, or external or internal fits over the others. This future work is important because it could facilitate the allocation of resources during the process of designing a production system.

Third, further research is also required in relation to purpose of DES. Simulation is a fast changing field with increased relevancy in the context of smart factories and Industry 4.0. Within this field, the use of Simulation-based optimization (SBO) is of increasing interest because it contributes to the systematic search of large decisions spaces for optimal or near optimal alternatives in production systems (Xu et al. 2016). Therefore, less resources and time are employed in the selection of decision alternatives. This can influence the way in which manufacturing companies make decisions in process innovations. However, the questions remain as simulation-based optimization places strict requirements when modeling stochastic systems such as production ones. Thus, the area of simulation-based optimization in process innovations provides ample venue for future research.

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processes for developing production systems, continual design of new products, and an emphasis on increasing flexibility of production systems. Future research may focus on production system design projects with different variables relating to, e.g., company size, industry type, product type, and type of production technology or process. Therefore, additional research is necessary to test and validate the findings of this research. Future studies could focus on different industrial segments. In addition, further research could benefit from complementary research methods such as surveys, which could provide statistical validity to confirm or adjust the findings presented in this research.

Second, further research is also required concerning the activities in the design of production systems facilitating the use of DES. This research did not investigate the precedence of activities leading to an improved outcome. For example, do all concepts of a decision perspective hold an equal significance in the development of conceptual models? Accordingly, future research could investigate the saliency of one of the concepts of strategic objectives, decision areas, or external or internal fits over the others. This future work is important because it could facilitate the allocation of resources during the process of designing a production system.

Third, further research is also required in relation to purpose of DES. Simulation is a fast changing field with increased relevancy in the context of smart factories and Industry 4.0. Within this field, the use of Simulation-based optimization (SBO) is of increasing interest because it contributes to the systematic search of large decisions spaces for optimal or near optimal alternatives in production systems (Xu et al. 2016). Therefore, less resources and time are employed in the selection of decision alternatives. This can influence the way in which manufacturing companies make decisions in process innovations. However, the questions remain as simulation-based optimization places strict requirements when modeling stochastic systems such as production ones. Thus, the area of simulation-based optimization in process innovations provides ample venue for future research.

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Akpan, I. J., and R. J. Brooks. 2014. "Experimental Evaluation of User Performance on Two-Dimensional and Three-Dimensional Perspective Displays in Discrete-Event Simulation". Decision Support Systems 64:14-30.

Akpan, I. J., and M. Shanker. 2017. "The Confirmed Realities and Myths about the Benefits and Costs of 3D Visualization and Virtual Reality in Discrete Event Modeling and Simulation: A Descriptive Meta-Analysis of Evidence from Research and Practice". Computers & Industrial Engineering 112:197-211.

Alfieri, A., M. Cantamessa, F. Montagna, and E. Raguseo. 2013. "Usage of SoS Methodologies in Production System Design". Computers & Industrial Engineering 64 (2):562-572.

Ali, S. A., and H. Seifoddini. 2006. "Simulation Intelligence and Modeling for Manufacturing Uncertainties". In Proceedings of the 2006 Winter Simulation Conference, edited by L. F. Perrone, F. P. Wieland, J. L. Lawson, B. G., D. M. Nicol, and R. M. Fujimoto, pp. 1920-1928. Monterrey, CA: IEEE.

Andersen, A.-L., T. D. Brunoe, K. Nielsen, and C. Rösiö. 2017. "Towards a Generic Design Method for Reconfigurable Manufacturing Systems: Analysis and Synthesis of Current Design Methods and Evaluation of Supportive Tools". Journal of Manufacturing Systems 42:179–195.

Andersen, A.-L., K. Nielsen, and T. D. Brunoe. 2016. "Prerequisites and Barriers for the Development of Reconfigurable Manufacturing Systems for High Speed Ramp-up". Procedia CIRP 51:7-12.

Arana-Solares, I. A., C. H. Ortega-Jiménez, R. Alfalla-Luque, and J. L. Pérez-Díez de los Ríos. 2019. "Contextual Factors Intervening in the Manufacturing Strategy and Technology Management-Performance Relationship". International Journal of Production Economics 207:81-95.

Attri, R., and S. Grover. 2012. "A Comparison of Production System Life Cycle Models". Frontiers of Mechanical Engineering 7 (3):305-311.

Attri, R., and S. Grover. 2015. "Production System Life Cycle: An Inside Story". International Journal of Industrial and Systems Engineering 19 (4):483-514.

Avila, O., V. Goepp, and F. Kiefer. 2018. "Addressing Alignment Concerns into the Design of Domain-Specific Information Systems". Journal of Manufacturing Technology Management 29 (5):726-745.

Balci, O. 2012. "A Life Cycle for Modeling and Simulation". SIMULATION 88 (7):870-883. Balci, O., and W. F. Ormsby. 2007. "Conceptual Modelling for Designing Large-Scale Simulations".

Journal of Simulation 1 (3):175-186. Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. 2000. Discrete-Event System Simulation. 3rd

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Manufacturing: A Review". International Journal of Modeling, Simulation, and Scientific Computing 07 (01):1630001.

Barratt, M., T. Y. Choi, and M. Li. 2011. "Qualitative Case Studies in Operations Management: Trends, Research Outcomes, and Future Research Implications". Journal of Operations Management 29 (4):329-342.

Bärring, M., B. Johansson, E. Flores-Garcia, J. Bruch, and M. Wahlstrom. 2018. "Challenges of Data Acquisition for Simulation Models of Production Systems in Need of Standards". In

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8 REFERENCES Adler, P. A., and P. Adler. 1994. "Observational Techniques". In Handbook of Qualitative Research,

edited by N. Denizen, and Y. Lincoln, pp. 377–392. New Delhi, India: Sage Publications. Ahlskog, M., J. Bruch, and M. Jackson. 2017. "Knowledge Integration in Manufacturing

Technology Development". Journal of Manufacturing Technology Management 28 (8):1035-1054.

Akpan, I. J., and R. J. Brooks. 2014. "Experimental Evaluation of User Performance on Two-Dimensional and Three-Dimensional Perspective Displays in Discrete-Event Simulation". Decision Support Systems 64:14-30.

Akpan, I. J., and M. Shanker. 2017. "The Confirmed Realities and Myths about the Benefits and Costs of 3D Visualization and Virtual Reality in Discrete Event Modeling and Simulation: A Descriptive Meta-Analysis of Evidence from Research and Practice". Computers & Industrial Engineering 112:197-211.

Alfieri, A., M. Cantamessa, F. Montagna, and E. Raguseo. 2013. "Usage of SoS Methodologies in Production System Design". Computers & Industrial Engineering 64 (2):562-572.

Ali, S. A., and H. Seifoddini. 2006. "Simulation Intelligence and Modeling for Manufacturing Uncertainties". In Proceedings of the 2006 Winter Simulation Conference, edited by L. F. Perrone, F. P. Wieland, J. L. Lawson, B. G., D. M. Nicol, and R. M. Fujimoto, pp. 1920-1928. Monterrey, CA: IEEE.

Andersen, A.-L., T. D. Brunoe, K. Nielsen, and C. Rösiö. 2017. "Towards a Generic Design Method for Reconfigurable Manufacturing Systems: Analysis and Synthesis of Current Design Methods and Evaluation of Supportive Tools". Journal of Manufacturing Systems 42:179–195.

Andersen, A.-L., K. Nielsen, and T. D. Brunoe. 2016. "Prerequisites and Barriers for the Development of Reconfigurable Manufacturing Systems for High Speed Ramp-up". Procedia CIRP 51:7-12.

Arana-Solares, I. A., C. H. Ortega-Jiménez, R. Alfalla-Luque, and J. L. Pérez-Díez de los Ríos. 2019. "Contextual Factors Intervening in the Manufacturing Strategy and Technology Management-Performance Relationship". International Journal of Production Economics 207:81-95.

Attri, R., and S. Grover. 2012. "A Comparison of Production System Life Cycle Models". Frontiers of Mechanical Engineering 7 (3):305-311.

Attri, R., and S. Grover. 2015. "Production System Life Cycle: An Inside Story". International Journal of Industrial and Systems Engineering 19 (4):483-514.

Avila, O., V. Goepp, and F. Kiefer. 2018. "Addressing Alignment Concerns into the Design of Domain-Specific Information Systems". Journal of Manufacturing Technology Management 29 (5):726-745.

Balci, O. 2012. "A Life Cycle for Modeling and Simulation". SIMULATION 88 (7):870-883. Balci, O., and W. F. Ormsby. 2007. "Conceptual Modelling for Designing Large-Scale Simulations".

Journal of Simulation 1 (3):175-186. Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. 2000. Discrete-Event System Simulation. 3rd

ed. Upper Saddle River, NJ, USA: Prentice-Hall Barlas, P., and C. Heavey. 2016. "Automation of Input Data to Discrete Event Simulation for

Manufacturing: A Review". International Journal of Modeling, Simulation, and Scientific Computing 07 (01):1630001.

Barratt, M., T. Y. Choi, and M. Li. 2011. "Qualitative Case Studies in Operations Management: Trends, Research Outcomes, and Future Research Implications". Journal of Operations Management 29 (4):329-342.

Bärring, M., B. Johansson, E. Flores-Garcia, J. Bruch, and M. Wahlstrom. 2018. "Challenges of Data Acquisition for Simulation Models of Production Systems in Need of Standards". In

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Proceedings of the 2018 Winter Simulation Conference, edited by M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, pp. 691-702. Gothenburg, Sweden: IEEE.

Bates, K. A., S. D. Amundson, R. G. Schroeder, and W. T. Morris. 1995. "The Crucial Interrelationship Between Manufacturing Strategy and Organizational Culture". Management Science 41 (10):1565-1580.

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Bellgran, M., and K. Säfsten. 2010. Production Development: Design and Operation of Production Systems. New York, NY: Springer Science & Business Media

Bendoly, E., K. L. Donohue, and K. L. Schultz. 2006. "Behavior in Operations Management: Assessing Recent Findings and Revisiting Old Assumptions". Journal of Operations Management 24 (6):737–752.

Bendul, J. C., and H. Blunck. 2019. "The Design Space of Production Planning and Control for Industry 4.0". Computers in Industry 105:260-272.

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Bennett III, R. H. 1998. "The Importance of Tacit Knowledge in Strategic Deliberations and Decisions". Management Decision 36 (9):589-597.

Berry, W. L., J. E. Klompmaker, C. C. Bozarth, and T. J. Hill. 1991. "Factory Focus: Segmenting Markets from an Operations Perspective". Journal of Operations Management 10 (3):363–387.

Boer, H., M. Holweg, M. Kilduff, M. Pagell, R. Schmenner, and C. Voss. 2015. "Making a Meaningful Contribution to Theory". International Journal of Operations & Production Management 35 (9):1231-1252.

Bokrantz, J., A. Skoogh, D. Lämkull, A. Hanna, and T. Perera. 2018. "Data Quality Problems in Discrete Event Simulation of Manufacturing Operations". Simulation 94 (11):1009–1025.

Börjesson, S., F. Dahlsten, and M. Williander. 2006. "Innovative Scanning Experiences from an Idea Generation Project at Volvo Cars". Technovation 26 (7):775-783.

Bruch, J. 2012. "Management of Design Information in the Production System Design Process". Ph. D. Thesis, Mälardalen University, Västerås, Sweden.

Bruch, J., and M. Bellgran. 2013. "Characteristics Affecting Management of Design Information in the Production System Design Process". International Journal of Production Research 51 (11):3241–3251.

Bruch, J., and M. Bellgran. 2014. "Integrated Portfolio Planning of Products and Production Systems". Journal of Manufacturing Technology Management 25 (2):155-174.

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Proceedings of the 2018 Winter Simulation Conference, edited by M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, pp. 691-702. Gothenburg, Sweden: IEEE.

Bates, K. A., S. D. Amundson, R. G. Schroeder, and W. T. Morris. 1995. "The Crucial Interrelationship Between Manufacturing Strategy and Organizational Culture". Management Science 41 (10):1565-1580.

Bayer, S., T. Bolt, S. Brailsford, and M. Kapsali. 2014. "Models as Interfaces". In Discrete-Event Simulation and System Dynamics for Management Decision Making,, edited by S. Brailsford, L. Churilov, and B. Dangerfield, pp. 125-139. Chichester, U.K.: John Wiley & Sons.

Bellgran, M., and K. Säfsten. 2010. Production Development: Design and Operation of Production Systems. New York, NY: Springer Science & Business Media

Bendoly, E., K. L. Donohue, and K. L. Schultz. 2006. "Behavior in Operations Management: Assessing Recent Findings and Revisiting Old Assumptions". Journal of Operations Management 24 (6):737–752.

Bendul, J. C., and H. Blunck. 2019. "The Design Space of Production Planning and Control for Industry 4.0". Computers in Industry 105:260-272.

Bennett, D. J., and P. L. Forrester. 1993. Market-Focused Production Systems: Design and Implementation. New York, NY: Prentice Hall

Bennett III, R. H. 1998. "The Importance of Tacit Knowledge in Strategic Deliberations and Decisions". Management Decision 36 (9):589-597.

Berry, W. L., J. E. Klompmaker, C. C. Bozarth, and T. J. Hill. 1991. "Factory Focus: Segmenting Markets from an Operations Perspective". Journal of Operations Management 10 (3):363–387.

Boer, H., M. Holweg, M. Kilduff, M. Pagell, R. Schmenner, and C. Voss. 2015. "Making a Meaningful Contribution to Theory". International Journal of Operations & Production Management 35 (9):1231-1252.

Bokrantz, J., A. Skoogh, D. Lämkull, A. Hanna, and T. Perera. 2018. "Data Quality Problems in Discrete Event Simulation of Manufacturing Operations". Simulation 94 (11):1009–1025.

Börjesson, S., F. Dahlsten, and M. Williander. 2006. "Innovative Scanning Experiences from an Idea Generation Project at Volvo Cars". Technovation 26 (7):775-783.

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Appendix – Reflections on My PhD Journey This appendix outlines my journey as a doctoral student, and presents in chronological order the events that bolstered my research competence.

My interest for novel production processes and technologies began shortly after graduating from the mechatronics program at the Monterrey Institute of Technology and Higher Education. Prior to my PhD studies, I worked five years for a large manufacturer of automation equipment between 2005 and 2009. During this period, I helped production engineering departments introduce improvements to their production processes based on hydraulic, pneumatic, and electromechanical systems. This period was fascinating as it provided me with learning experience in diverse industrial sectors (i.e. construction, food and beverage, pharmaceutical, automotive, paper, etc.). More importantly, I witnessed the difficulties faced by manufacturing companies, and the bewilderment of engineering teams that grappled with understanding the consequences of implementing novel production processes.

The importance of this problem became clear to me while working at a newly developed business unit for a Tier 1 supplier of the automotive industry between 2009 and 2011. This business unit began operations after the company introduced a casting process for aluminum components, which was new to the company and was unlike those at other sites. The characteristics of the casting process facilitated the production of new products and entering new markets. However, we failed to comprehend the extent of changes and its consequences on logistics, quality, production, machining and assembly processes. This occurred despite relying on the experience of a cross-functional group of experts, an international task force of engineers, and highly skilled operators. At the time, I was responsible for linking the production capabilities of the site with the requirements from customers including Toyota, Ford, Nissan, and GM among others. From my role, and that of others, it became apparent that this shortcoming reduced our competitive edge.

I became aware that something was amiss, and that increased specialization was clearly necessary to support the challenges faced by competitive manufacturing companies. In 2011, sponsored by the Mexican Council of Science and Technology and the Central Bank of Mexico, I pursued master’s degree in production and logistics at Mälardalen University. During my thesis, I became familiar with the subject of DES and initiated a project at Alfa Laval, which included evaluating the implementation of a new welding process at their Eskilstuna site. The outcome of this project captured the interest of the senior management, and it convinced me of the importance of simulation-based tools for decision-making of production systems. Consequently, I looked for opportunities to further my knowledge about DES. This led to a position as a research

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Appendix – Reflections on My PhD Journey This appendix outlines my journey as a doctoral student, and presents in chronological order the events that bolstered my research competence.

My interest for novel production processes and technologies began shortly after graduating from the mechatronics program at the Monterrey Institute of Technology and Higher Education. Prior to my PhD studies, I worked five years for a large manufacturer of automation equipment between 2005 and 2009. During this period, I helped production engineering departments introduce improvements to their production processes based on hydraulic, pneumatic, and electromechanical systems. This period was fascinating as it provided me with learning experience in diverse industrial sectors (i.e. construction, food and beverage, pharmaceutical, automotive, paper, etc.). More importantly, I witnessed the difficulties faced by manufacturing companies, and the bewilderment of engineering teams that grappled with understanding the consequences of implementing novel production processes.

The importance of this problem became clear to me while working at a newly developed business unit for a Tier 1 supplier of the automotive industry between 2009 and 2011. This business unit began operations after the company introduced a casting process for aluminum components, which was new to the company and was unlike those at other sites. The characteristics of the casting process facilitated the production of new products and entering new markets. However, we failed to comprehend the extent of changes and its consequences on logistics, quality, production, machining and assembly processes. This occurred despite relying on the experience of a cross-functional group of experts, an international task force of engineers, and highly skilled operators. At the time, I was responsible for linking the production capabilities of the site with the requirements from customers including Toyota, Ford, Nissan, and GM among others. From my role, and that of others, it became apparent that this shortcoming reduced our competitive edge.

I became aware that something was amiss, and that increased specialization was clearly necessary to support the challenges faced by competitive manufacturing companies. In 2011, sponsored by the Mexican Council of Science and Technology and the Central Bank of Mexico, I pursued master’s degree in production and logistics at Mälardalen University. During my thesis, I became familiar with the subject of DES and initiated a project at Alfa Laval, which included evaluating the implementation of a new welding process at their Eskilstuna site. The outcome of this project captured the interest of the senior management, and it convinced me of the importance of simulation-based tools for decision-making of production systems. Consequently, I looked for opportunities to further my knowledge about DES. This led to a position as a research

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assistant in 2013, in which I focused on the use of DES in the design of production systems. The research environment drew me in, and I recognized that much in this field remained unknown beyond the detailed use of software. I began my PhD journey in 2014, which I have summarized below in relation to the papers appended in this thesis.

My first paper (paper 6) focused on the use of DES in the design of production systems. This paper resulted from my participation in the design of a production system at one of Sweden’s largest manufacturing companies between 2014 and 2015. As a new PhD student, this project was highly motivating as it allowed me to take part in an initiative of critical concern to the manufacturing company and interact, on a daily basis, with the international and diverse staff responsible for its development. This project was a valuable learning experience, and exemplified the importance of a close partnership between manufacturing companies and academia. As a first time academic writer, I struggled to understand the purpose of theory and the concerns of an academic audience. My supervisors guided me through the process of developing an idea of practical concern into a topic of academic interest. After several iterations, this paper was accepted at the Winter Simulation Conference of 2015. This paper was valuable because it helped me realize the importance of balancing practice and academic-oriented work, and taught me differences between its activities, interests, and outputs.

My second paper (paper 2) proposed DES as a means for reducing uncertainty during the design of a production system. This paper summarized the reflections of my daily participation in the design of a production system at a Swedish manufacturing site between January and June of 2016. This was significant for practical and academic reasons. From the practical perspective, the manufacturing site received a national production prize. In addition, the positive results of this case initiated a series of discussions including the European Operations Management team of the manufacturing company. These focused on the importance of DES in production system design and supporting decision-making. Academically, this paper required familiarizing with organization theory for understanding the concept of uncertainty. I presented this paper at the ASIM conference in Kassel, Germany, in 2017. Attending this conference was invaluable because I realized the significance of the exchange of ideas to research quality. This realization has been crucial and led to networking with a research community, which has provided indispensable input for my publications. In addition, this contributed to my organizing a one-week research visit to Japan along with 20 other PhD students and 3 senior researchers focused on the topic of industry and academia research collaboration.

My third paper (paper 5) focused on understanding how to reduce uncertainty when DES parameters are first identified and abstracted,

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assistant in 2013, in which I focused on the use of DES in the design of production systems. The research environment drew me in, and I recognized that much in this field remained unknown beyond the detailed use of software. I began my PhD journey in 2014, which I have summarized below in relation to the papers appended in this thesis.

My first paper (paper 6) focused on the use of DES in the design of production systems. This paper resulted from my participation in the design of a production system at one of Sweden’s largest manufacturing companies between 2014 and 2015. As a new PhD student, this project was highly motivating as it allowed me to take part in an initiative of critical concern to the manufacturing company and interact, on a daily basis, with the international and diverse staff responsible for its development. This project was a valuable learning experience, and exemplified the importance of a close partnership between manufacturing companies and academia. As a first time academic writer, I struggled to understand the purpose of theory and the concerns of an academic audience. My supervisors guided me through the process of developing an idea of practical concern into a topic of academic interest. After several iterations, this paper was accepted at the Winter Simulation Conference of 2015. This paper was valuable because it helped me realize the importance of balancing practice and academic-oriented work, and taught me differences between its activities, interests, and outputs.

My second paper (paper 2) proposed DES as a means for reducing uncertainty during the design of a production system. This paper summarized the reflections of my daily participation in the design of a production system at a Swedish manufacturing site between January and June of 2016. This was significant for practical and academic reasons. From the practical perspective, the manufacturing site received a national production prize. In addition, the positive results of this case initiated a series of discussions including the European Operations Management team of the manufacturing company. These focused on the importance of DES in production system design and supporting decision-making. Academically, this paper required familiarizing with organization theory for understanding the concept of uncertainty. I presented this paper at the ASIM conference in Kassel, Germany, in 2017. Attending this conference was invaluable because I realized the significance of the exchange of ideas to research quality. This realization has been crucial and led to networking with a research community, which has provided indispensable input for my publications. In addition, this contributed to my organizing a one-week research visit to Japan along with 20 other PhD students and 3 senior researchers focused on the topic of industry and academia research collaboration.

My third paper (paper 5) focused on understanding how to reduce uncertainty when DES parameters are first identified and abstracted,

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namely the conceptual model. This paper was important in developing my research skills for two reasons. First, I initiated a collaboration with a research group from another university. To do so, I spent a considerable amount of time refining the research problem and positioning its theoretical contribution. In addition, I tested my ideas with my supervisors before proposing the research collaboration to those who would become my co-authors. Second, I became aware that reducing uncertainty alone was not sufficient, and that reducing equivocality held equal importance. I presented this paper at the 2018 CIRP conference in Stockholm at memorable night at City Hall. During the activities leading to the publication of this paper, I realized the significance of achieving rapport with research groups different from mine. During this effort, I was made the national representative for doctoral students in the area of production research, and a temporary member of the Swedish Production Academy. This opportunity allowed me to gain an in-depth understanding of the diverse research groups in Sweden, and discuss research ideas with the professors heading those groups.

My fourth paper (paper 3) involved analyzing the challenges of DES use in the early stages of production system design. This paper outlined my learning from three process innovation projects spanning three years, and drawing empirics from sites in Sweden, Brazil, and Germany. Having the first draft of this paper rejected by reviewers of the Winter Simulation Conference of 2017 was both humbling and disappointing. This experience taught me the importance of narrowing the scope of an academic study, and defining its contributions early on in the writing process. After working extensively with the text, I presented an improved version of this paper at the APMS conference in Seoul in 2018, which resulted in an invitation to a special issue from IJIETAP. In addition, the outcome of this paper was an essential input to develop material for piloting a course focused on the use of DES in the design of production systems for the manufacturing industry, as part of the KKS-funded project, PREMIUM. This was an exciting experience as the conference included representatives from Volvo, Scania, AstraZeneca, and Bombardier. I consider necessary volunteering for this pilot and contributing to the manufacturing practice and disseminating the results of research, including my own findings.

My fifth paper (paper 4) focused on analyzing information consensus and the reduction of equivocality in the design of production systems. I presented the first version of this paper at the Swedish Production Symposium of 2016 in Lund, and IJMR invited me to submit an extended version in a special issue. I proposed my supervision team a strategy for submitting the text to IJMR by improving my scientific contributions in two ways. Firstly, my paper would no longer be of exploratory/descriptive nature. Instead, I positioned my contribution as a framework based on a set of claims. To do so, I focused on the use of empirical data as an integral part

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namely the conceptual model. This paper was important in developing my research skills for two reasons. First, I initiated a collaboration with a research group from another university. To do so, I spent a considerable amount of time refining the research problem and positioning its theoretical contribution. In addition, I tested my ideas with my supervisors before proposing the research collaboration to those who would become my co-authors. Second, I became aware that reducing uncertainty alone was not sufficient, and that reducing equivocality held equal importance. I presented this paper at the 2018 CIRP conference in Stockholm at memorable night at City Hall. During the activities leading to the publication of this paper, I realized the significance of achieving rapport with research groups different from mine. During this effort, I was made the national representative for doctoral students in the area of production research, and a temporary member of the Swedish Production Academy. This opportunity allowed me to gain an in-depth understanding of the diverse research groups in Sweden, and discuss research ideas with the professors heading those groups.

My fourth paper (paper 3) involved analyzing the challenges of DES use in the early stages of production system design. This paper outlined my learning from three process innovation projects spanning three years, and drawing empirics from sites in Sweden, Brazil, and Germany. Having the first draft of this paper rejected by reviewers of the Winter Simulation Conference of 2017 was both humbling and disappointing. This experience taught me the importance of narrowing the scope of an academic study, and defining its contributions early on in the writing process. After working extensively with the text, I presented an improved version of this paper at the APMS conference in Seoul in 2018, which resulted in an invitation to a special issue from IJIETAP. In addition, the outcome of this paper was an essential input to develop material for piloting a course focused on the use of DES in the design of production systems for the manufacturing industry, as part of the KKS-funded project, PREMIUM. This was an exciting experience as the conference included representatives from Volvo, Scania, AstraZeneca, and Bombardier. I consider necessary volunteering for this pilot and contributing to the manufacturing practice and disseminating the results of research, including my own findings.

My fifth paper (paper 4) focused on analyzing information consensus and the reduction of equivocality in the design of production systems. I presented the first version of this paper at the Swedish Production Symposium of 2016 in Lund, and IJMR invited me to submit an extended version in a special issue. I proposed my supervision team a strategy for submitting the text to IJMR by improving my scientific contributions in two ways. Firstly, my paper would no longer be of exploratory/descriptive nature. Instead, I positioned my contribution as a framework based on a set of claims. To do so, I focused on the use of empirical data as an integral part

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of a reasoning process that ranged from a set of theoretical precepts to a set of claims as suggested by Ketokivi and Choi (2014). Secondly, I focused on what Pratt (2009) refers to as improving the quality of qualitative based research, by developing a narrative in the text that matched empirical data leading to scientific claims. Writing this paper was challenging, and I benefited from my attending the course, “Get your paper published,” which was sponsored by the Swedish Production Academy. The constructive criticism from peers and the instructor helped me mature the text to the point that my supervisors would only make minor suggestions for improvement.

My sixth paper (paper 1) was centered on understanding the effects of decision-making on the decision areas of production systems in process innovations. This paper included a new stream of literature, decision-making. To position the contribution of this paper, I invested significant time in understanding the decision-making literature, and in how organization theory contributes to selecting a decision-making approach. Furthermore, I set myself the objective of improving the nature of my contributions in relation to extant theory. Writing this paper was challenging and required considerable effort. This paper was finally accepted after three rounds of revision at the JMTM. I am hopeful about the outcome of this paper, and the impact it may have to my academic career after my PhD.

This five-year journey of doctoral studies has been transformational, and has helped me progress from having crude understanding of research to attaining independence as an active member of the scientific community. In addition, this journey has helped me comprehend the importance of research in relation to advancing knowledge, and the value of this knowledge for increasing manufacturing competitiveness. I am hopeful and I believe that this journey has provided me with a unique set of tools that will help me contribute and bring positive changes to my community.

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of a reasoning process that ranged from a set of theoretical precepts to a set of claims as suggested by Ketokivi and Choi (2014). Secondly, I focused on what Pratt (2009) refers to as improving the quality of qualitative based research, by developing a narrative in the text that matched empirical data leading to scientific claims. Writing this paper was challenging, and I benefited from my attending the course, “Get your paper published,” which was sponsored by the Swedish Production Academy. The constructive criticism from peers and the instructor helped me mature the text to the point that my supervisors would only make minor suggestions for improvement.

My sixth paper (paper 1) was centered on understanding the effects of decision-making on the decision areas of production systems in process innovations. This paper included a new stream of literature, decision-making. To position the contribution of this paper, I invested significant time in understanding the decision-making literature, and in how organization theory contributes to selecting a decision-making approach. Furthermore, I set myself the objective of improving the nature of my contributions in relation to extant theory. Writing this paper was challenging and required considerable effort. This paper was finally accepted after three rounds of revision at the JMTM. I am hopeful about the outcome of this paper, and the impact it may have to my academic career after my PhD.

This five-year journey of doctoral studies has been transformational, and has helped me progress from having crude understanding of research to attaining independence as an active member of the scientific community. In addition, this journey has helped me comprehend the importance of research in relation to advancing knowledge, and the value of this knowledge for increasing manufacturing competitiveness. I am hopeful and I believe that this journey has provided me with a unique set of tools that will help me contribute and bring positive changes to my community.

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