1

EvaluationofManufacturingCostModels‐Anevaluationofmanufacturingcostmodelsforanassemblyline

andanevaluationofadatacollectionmethod

Matilda Garbe, Rebecca Olausson

Department of Industrial Production, LundUniversity,2014

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AbstractThemanufacturingindustriesstruggletokeepupwiththeglobalcompetitionthatisspreading.Inordertobeabletodeliveraproductattherighttime,totherightpriceandatthesametimestay profitable it is essential for companies to increase the efficiency of their manufacturingsystemandtoreducecosts.Plentyofmanufacturingcostmodelshavebeendevelopedtohelpcompanies for thispurposeandoneof thesemodels ismanufacturingcostdeployment (MCD),developedbyYamashina&Kubo,whichfocusesonreducingwasteandlosses.Itisamethodtoinvestigate the relationship among cost factors, i.e. the processes generating costs. MCDwasimplemented two years ago at Volvo Construction Equipment (VCE) inWroclaw, a companyassemblingbackhoeloaders.Themethodhasyetnotbeensuccessfulandisalossinitselfsinceithasbecomemoreofanadministrationaltaskwithscarceresults.

OneofthedatainputsourcesVCEusesforMCDistheoutputofamethodcalledworksampling.It is a raw data collection tool to gain knowledge in the distribution of value and non‐valueaddingactivities intheassembly.Thismethodhasbeentestedbrieflywithoutconsideringtheaccuracyoftheresults.WithoutknowingthereliabilityofthedataattainedfromworksamplingitcanbediscussedwhetheritshouldbeusedasasourceforMCD,orforanyotherevaluationofa production process for that matter. Without reliable input it is impossible to get reliableresults.

ThisthesisinvestigateshowMCDisusedatVCEtogainadeeperunderstandingoftheproblemsconcerning themethod in order to give recommendations for a better implementation of themodel. Investigationsarealsomade tosee if there isanalternativemanufacturingcostmodelsuitable for VCE. The work sampling method is examined to determine the reliability of theresults generated. The studies have mainly been conducted through interviews, literaturereviewsandparticipationinpreparationsandexecutionofworksamplingatVCE.

OneofthemainissueswithMCDatVCEtodayisthelackofdedication.Withoutdedicationitishardtoachievethedesiredresultsandthisissomethingtheyneedtoworkwith.MCDconsistsofsixmatrices,butonlythefirstthreeofthemareusedatVCEandboththeBandCmatricescontainsdeficienciesof relevance.Furthermore, theBandCmatricesneed tobeupdatedandstandardized.

An alternative manufacturing cost model that would be suitable for VCE is the systematicproductionanalysis,developedbyStåhl.Thepurposeofthismethodistoidentifythelosstermsof quality, downtime andproduction rate. It is a user‐friendlymethodwith a strong focusonimprovementworkthrougheliminatingdeviations.

Regarding the reliability of using work sampling as a source of data, it is essential that theassessors are aligned to avoiddifferent interpretationsof theobservations.Another aspectofimportanceregardingreliabilityistheselectionofoperators,machinesandstations;theseneedtobe representative for the line. Themore observationsmade, thebetter the accuracyof theresults.Using confidence intervals ensure accurate results and observations need to bemaderandomly. Ifallof theabovecriteria is fulfilledtheresults fromworksamplingcanbeseenasreliable.

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SammanfattningFöretag inom tillverkningsindustrin kämpar för att hålla jämna steg med den globalakonkurrensensomständigtökar.Förattkunnalevereraproduktervidrätttidpunkt,tillrättprisochsamtidigtskapalönsamhetärdetavstörstaviktförföretagattökaeffektivitetenochsänkakostnader.Underårensloppharfleraolikakostnadsmodellerutvecklatsföratthjälpaföretagidetta syfte. En av modellerna är manufacturing cost deployment (MCD) som utvecklats avYamashina & Kubo. Det är en metod som används för att undersöka sambandet mellankostnadsfaktorer;processersomgenerarkostnaderochförluster.MCDimplementeradesförtvåårsedanpåVolvoConstructionEquipment(VCE)iWroclaw,dockutanstörreframgång.Idagärmetoden snarareett administrativt arbete än ett verktyg för att sänkakostnaderpå grundavuteblivnaresultatavmetoden.

EndelavdedatasomärinputtillMCDkommerfrånworksampling,enmetodsomanvändsförattberäknatidsfördelningenmellanolikaaktiviteter.DennametodärocksårelativtnyförVCEochharbaratestatstvågångerutanvidarereflektionöverhurnoggrannaresultatsomerhålls.Om inte tillförlitligheten på de data sommetoden genererar är känd så kan det ifrågasättashuruvidadenskaanvändassomdatakällaförMCD.Utantillförlitligainputärdetomöjligtattfåtillförlitligaresultat.

IdennastudieundersökshurVCEanvändersigavMCDförattfåendjupareförståelseförvarförimplementatinen avmetoden inte fungerar, samt för att ge rekommendationerpåhurdekanförbättraarbetetmedmetodenochpåsåsätt fåönskvärdaresultat.DåMCD inteärdenendakostnadsmodellen kommer även andramodeller att undersökas i syfte att finna en alternativmetod och klargöra på vilka sätt den skiljer sig från MCD. Dessutom undersöks även worksampling för att avgöra hur tillförlitliga resultat denna metod kan generera. Studierna harhuvudsakligengenomförtsgenomlitteraturstudier,intervjuerochmedverkandeiförberedelserochutförandeavworksamplingpåVCE.

EttavdestörstaproblemenmedMCDidagärbristpåhängivenhet.UtantropåmetodenärdetsvårtattfåönskvärtresultatochdettamåsteVCEjobbamed.MCDbeståravsexmatriser,menendast de första matriserna används och då det finns stora brister ibåde B‐ och C‐matrisenbehöver de stora förbättringar. Ett annat problem är bristen på standardisering för hurkostnaderskaberäknas,vilketomöjliggörjämförelseravresultatfrånMCD.

En annan modell som skulle passa bra för VCE är den systematiska produktionsanalysen,utvecklad av Ståhl. Syftet med denna metod är att identifiera förluster itermer av kvalitet,stilleståndstid och taktförluster. Det är en användarvänlig metod med starkt fokus påförbättringsarbete.

Gällande tillförlitligheten förwork sampling som datakälla är det av yttersta vikt att de somutför observationerna tolkar aktiviteter på samma sätt. Andra aspekter att ta hänsyn till ärurvalet av operatörer, arbetsområden och maskiner; dessa måste vara representativa förproduktionslinan. Ju fler observationer som görs desto högre blir noggranheten. För attsäkerställaattnoggranhetenblirtillräckligthögkankonfidensintervallanvändasföratträknautminsta antaletobservationer somkrävs.Observationernamåsteutföras slumpmässigt.Är alladessakriterieruppfylldasåkanresultatenfrånworksamplingsessomtillförlitliga.

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Acknowledgements

This thesis is the final part of ourMaster of Science degree inMechanical Engineering at theFacultyofEngineeringatLundUniversity.ThethesishasbeenconductedatVolvoConstructionEquipmentinWroclaw,Poland.

First,wewould like to express our gratitude to our kind and considerate supervisor,TomaszMaleszkaatVolvoConstructionEquipment.Yoursincerecommitment,guidanceandexceptionalsupporthavebeenpriceless.WewouldalsoliketothankTommyBengtssonandallpersonnelatVCE,notonly for theirhospitality andenthusiasm,but also for their timeand effort spentonhelpingusandwewishthemallthebestandgoodluckinthefuture.

Finally,wewant to dedicate special thanks to our supervisor at the LundUniversity, Jan‐EricStåhl,forhisvaluablecomments,ideasandsupport.

Lund,September2014,

MatildaGarbe&RebeccaOlausson

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ListofAcronyms

VCE Volvo Construction Equipment, in this thesis VCE refers to the plant inWroclaw,Poland.

MCD Manufacturing cost deployment, in this thesis the manufacturing costreductionprogramdevelopedbyH.Yamashina&Kubo.

VPS VolvoProductionSystem

VA Valueadding

S‐VA Semi‐valueadding

IMPM Integratedmanufacturingperformancemeasure

SPA Systematicproductionanalysis,amanufacturingcostmodeldevelopedbyJ‐E.Ståhl.

PPM Productionperformancematrix

DT Downtime

TBF Timebeforefailure

MTBF Meantimebeforefailure

MDT Meandowntime

Worksampling Datacollectingmethodusedformeasuringtimeutilization.

QRPS Quickresponseproblemsolving

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Tableofcontents1  INTRODUCTION ......................................................................................................................... 1 

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

1.2  PROBLEM DESCRIPTION ............................................................................................................... 1 

1.3  OBJECTIVE ................................................................................................................................ 1 

1.4  RESEARCH QUESTIONS ................................................................................................................. 2 

1.5  DELIMITATIONS AND FOCUS .......................................................................................................... 2 

1.6  REPORT STRUCTURE .................................................................................................................... 2 

2  VOLVO ...................................................................................................................................... 4 

2.1  VOLVO HISTORY ......................................................................................................................... 4 

2.2  THE VOLVO GROUP .................................................................................................................... 4 

2.3  VOLVO CONSTRUCTION EQUIPMENT, WROCLAW .............................................................................. 5 

2.4  THE VOLVO PRODUCTION SYSTEM ................................................................................................. 5 

3  METHODOLOGY ........................................................................................................................ 6 

3.1  THE SCIENTIFIC VIEW ................................................................................................................... 6 

3.1.1  THE ANALYTICAL VIEW ...................................................................................................................... 6 

3.1.2  THE SYSTEMS VIEW .......................................................................................................................... 6 

3.1.3  THE ACTORS VIEW ........................................................................................................................... 6 

3.1.4  THE SCIENTIFIC VIEW OF THIS THESIS ................................................................................................... 6 

3.2  RESEARCH METHOD .................................................................................................................... 7 

3.2.1  QUANTITATIVE AND QUALITATIVE DATA .............................................................................................. 7 

3.3  DATA COLLECTION ...................................................................................................................... 7 

3.3.1  PRIMARY AND SECONDARY DATA ........................................................................................................ 7 

3.3.2  INTERVIEWS.................................................................................................................................... 8 

3.3.3  LITERATURE REVIEW ......................................................................................................................... 8 

3.5  THE PROCEDURE OF THE STUDY ..................................................................................................... 9 

4.  MANUFACTURING COST MODELS .......................................................................................... 11 

4.1  MANUFACTURING COST DEPLOYMENT .......................................................................................... 11 

4.1.1   MANUFACTURING COST DEPLOYMENT .............................................................................................. 11 

4.1.2  CURRENT SITUATION AND IDENTIFIED GAPS ........................................................................................ 16 

4.1.3  IMPROVEMENT POSSIBILITIES ........................................................................................................... 17 

4.2  ALTERNATIVE MANUFACTURING COST MODELS ............................................................................... 19 

4.2.1  MANUFACTURING COST MODEL BY SON ............................................................................................ 20 

4.2.2  SYSTEMATIC PRODUCTION ANALYSIS ................................................................................................. 22 

4.2.3  COMPARISON ............................................................................................................................... 25 

4.2.4  CHOICE OF ALTERNATIVE MODEL ...................................................................................................... 28 

4.2.5  ANALYSIS OF A POSSIBLE IMPLEMENTATION OF SPA AT VCE ................................................................. 28 

 

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5.  WORK SAMPLING ................................................................................................................... 31 

5.1  WORK SAMPLING METHODOLOGY................................................................................................ 31 

5.2  CONFIDENCE INTERVAL .............................................................................................................. 32 

5.3  CURRENT SITUATION ................................................................................................................. 32 

5.4  IMPROVEMENTS OF WORK SAMPLING AT VCE ................................................................................ 33 

5.5  RESULTS OF WORK SAMPLING AT VCE .......................................................................................... 35 

5.5.1  WORK SAMPLING ON ALL WORK AREAS TOGETHER .............................................................................. 35 

5.5.2  WORK SAMPLING ON INDIVIDUAL WORK AREAS .................................................................................. 35 

5.5  ANALYSIS OF WORK SAMPLING .................................................................................................... 36 

5.6  DISCUSSION ............................................................................................................................ 37 

5.7  RECOMMENDATIONS ................................................................................................................ 38 

6  CONCLUSIONS ......................................................................................................................... 39 

6.1  ANSWERS TO THE RESEARCH QUESTIONS ....................................................................................... 39 

6.2  FUTURE RESEARCH .................................................................................................................... 41 

8  DISCUSSION ............................................................................................................................ 42 

9  REFERENCES ............................................................................................................................ 43 

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1 IntroductionThis initiatingchapterbeginswith thebackgroundof issuesassociated to thesubject ingeneral.Theproblemdescriptionisclarifiedfollowedbythepurposeofthisthesis.Finally,thereportoutlineispresented.

1.1 BackgroundThemanufacturingindustriesstruggletokeepupwiththeglobalcompetitionthatisspreading.Manufacturingorganizationsseektoproducehigh‐qualityproductsfasterandtoacompetitiveprice due to the increasing competitiveness. In order to be able to deliver the product at therighttime,totherightpriceandatthesametimestayprofitableitisessentialforcompaniestoincrease theefficiencyof themanufacturing systemand to reducecosts.Tobeable to reducecoststhecompaniesneedtofindoutwhyandwherecosts incur.Plentyofmanufacturingcostmodels have been developed to help companies for this purpose. One of these models ismanufacturing cost deployment, developed by Yamashina & Kubo, which focuses on reducingwasteandlosses.

Many investigationssuffer frominadequateand incorrectdata,argueDoganaksoy&Hahn[1].No matter what model companies use, the crucial part of every method is to have correct,adequatedatainputtogetreliableresults,sincetheanalysescanonlybeasgoodasisthedataupon which they are based. As the saying goes; ‐ Shit in, shit out. The essence is havingstandardizedandsuccessfulmethods forcollectingdataand tomeasureaccurately toachievereliabledatainput.Itiscommonlythoughtthatthedatarequiredisjustthere,butknowingwhatdataisrequiredandhowandwhentocollectitismorechallengingthanonecouldassume.Onedata collectingmethod that isused atVolvoConstructionEquipment,Wroclaw (VCE) isworksampling, a method to measure time distribution of activities. The method has existed forseveralyears,butitcanbequestionedwhetherornotitisareliablesourceofdata.

1.2ProblemdescriptionVCEhasintheprevioustwoyearsconductedtwoloopsofYamashinaandKubo’sMCDmodel,but without success. MCD is a quite general model and the design and how it is bestimplementeddependsonthetypeofindustryandfactory.Hence,MCDneedstobecustomizedforeachplantusingit.ThebenefitforVCEofobtainingbetterresultsfromMCDcanbeusedasanargumentforabiggerbudgetforsolvingtheproblemsidentifiedthroughMCD.Finally,MCDcouldresultincostreductionandifso,themethodwillhaveachievedsuccess.

OneofthedatasourcesusedfortheMCDatVCEcomesfromworksampling.Thismethodhasonlybeen testedbrieflywithout considering theaccuracyof theresults.Withoutknowing thereliabilityofthedatagainedfromworksamplingitcanbediscussedwhetheritshouldbeusedasasourceofdataforMCD,orforanyotherevaluationofaproductionprocessforthatmatter.

1.3ObjectiveThe objective of this thesis is to investigate how MCD is used at VCE and to gain a deeperunderstandingofwhyitisnotworkingproperlyinordertobeabletogiverecommendationsfor

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abetterimplementationofthemodelatVCE.InvestigationswillbemadetodetermineifthereareothercostmanufacturingmodelsthatcouldsuitVCEintheirstrivetooptimizeandreducecosts. If there isanothermodel thatwillbesuitable forVCE itwillalsobe investigatedhowitdiffersfromMCD.

Aspartoftheobjectivetheworksamplingmethodwillbeexaminedtodeterminehowreliableresultsthemethodgenerates.

1.4ResearchquestionsTo achieve the objective, three research questions have been formulated. The first questionconcernsmanufacturingcostdeployment,thesecondothermanufacturingmodelsthatcouldbeappliedonVCEandthelastconcernsworksampling.

1. HowcanVCEimprovetheirworkwithMCD?2. Isthereanothermanufacturingcostmodel,apartfromMCD,thatwouldbesuitablefor

VCEandifthereis,howdoesitdifferfromMCD?3. Can work sampling be seen as a reliable source of data when evaluating production

processes?

1.5DelimitationsandfocusDue to the time constraints for this thesis the focus has beenon the assembly department atVCE,i.e.thelogisticsandqualitydepartmentsatVCEareexcludedfromthisthesis.

Regardingthefirstresearchquestiontherearemanytypesofmanufacturingcostmodelsandtofind all of them and evaluate whether they could be suitable for VCE would be too time‐consuming for this thesis. Jönsson [2] has in his PhD thesis presented twelve differentmanufacturingcostmodels,whichwillbethestartingpointforthethirdresearchquestion.

1.6ReportstructureThisisacomprehensivethesisandallchaptersmightnotbeofinterestforalltypesofreaders.Therefore, a brief description of each chapter is presented for readers to easily orientthemselvesinthereport.Sincethetwofirstresearchquestionscanbetreatedseparatelyfromthethird,thetwofirstonesaretreatedinonechapterandthethirdinanotherchaptertomakeiteasyforthereadertofollow.Thethesishasthefollowingstructure:

Introduction Theintroductiongivesabriefbackgroundoftheissue,includingproblemdescription,purposeanddelimitationsofthisthesis.

Volvo This chapter includes thehistoryofVolvo, adescriptionofVCEand the

currentsituationatVCE,Wroclaw.Methodology Inthischapterfactsanddiscussionsofseveralapproachesandmethods

are provided to justify the chosen methodology for this thesis. Theprocedure of the thesis is also presented. Readers without interest forhowthethesiswasconductedcanignorethischapterwithoutlosingthecontentsofthereport.

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Manuf.costmodels Thefirsttworesearchquestionsareaddressedinthischapter.ThetheorybehindMCD is presented aswell as the evaluation ofMCD at VCE andpotentialimprovements.OthermanufacturingcostmodelsarepresentedandcomparedwithMCDandfinallyadescriptionofhowthealternativemethodcanbeimplemented.

Worksampling Thischaptertreatsthethirdresearchquestion.Thetheorybehindwork

sampling isdescribedaswell ashow ithasbeenusedatVCE.Togetherwiththeoryandtheparticipationinworksamplingconclusionsaboutthereliabilityofworksamplingasadatasourcearedrawn.

Conclusions This chapter concludes the answers to the research questions and the

chapterendswithaproposalforfutureresearchwithinthisarea.

Futurevisions Achieving improvements from manufacturing cost models results inincreased unbalanced lines of the stations and therefore this chapterproposeshowthelinebalancecanbeenhanced.

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2 VolvoTheaimwiththischapter isto introduceTheVolvoGroupandVolvoConstructionEquipment ingeneralaswellasthefactoryinWroclaw,wherethisthesishasbeenperformed.

2.1VolvohistoryThe very beginning of the Volvo history starts already in the 18th centurywith aman calledJohanTheofronMunktell.HeworkedasaforemaninacoinfactoryinEskilstuna,Sweden,andwas an initiative and creativemanwho hadmany projects. One of themwas starting a beerbrewerysothathisemployeesat thecoinfactorywoulddrinkbeer insteadof the2.5 litersofhardliquortheydrankeveryweekduringwork.Notsurprisingly,thisresultedinbetterhealthandimprovedworkingabilityfortheemployees.

After leaving the coin factory,Munktellwentona study trip toEngland.This iswherehegotinspiration and knowledge to start a production of locomotives. Later, in 1853, MunktellsMekaniskaVerkstaddeliveredthefirstlocomotiveinSweden.

WhenMunktellleftthecoinfactory,amannamedJeanBolinderreplacedhim.TogetherwithhisbrotherCarlGerhard,BolinderonceagainfollowedinMunktellsfootstepsandwenttoEnglandlearningmoreaboutmoldingandworkshops.AyearlatertheycamebacktoSwedenandstartedtheirownworkshop.Lateron,MunktellandtheBolinderbrothersmergedtheircompaniesintoABBolinder‐Munktell.

In 1913 Munktell created the first tractor in Sweden. This was a big step towards thedevelopmentofVolvoConstructionsEquipment.Volvowas created in1927and in 1950 theyacquiredBolinder‐Munktell.Tomirror theacquirementandstrengthen theVolvo identity, thecompanylaterchangedthenameintoVolvoBMAB[3].

2.2TheVolvoGroupVolvo was divided into two divisions in 1999, when Volvo Cars was sold to Ford MotorCompany.Theotherdivision,TheVolvoGroup,stillhasitsheadquartersinGothenburg,Sweden.The company is a manufacturer of trucks, buses, construction equipment and marine andindustrial engineswhich are all divided into the business areas; Volvo Trucks,Mack, RenaultTrucks,VolvoBuses,VolvoPenta,VolvoAeroandfinallyVolvoConstructionsEquipment[4].

TheVolvoGrouphas about 110,000 employees andproduction facilities in19 countrieswithproductssoldon190markets.ThenetsalesofTheVolvoGroupin2013wereSEK273billion[5].

ThecompanycultureatTheVolvoGroupiscalledTheVolvoWayanditexpressestheculture,behavior and values at the division. The Volvo Way is based on the conviction that “everyindividual has the capability to improve our business operations and the desire to developprofessionally” [6]. The core values are quality, environmental care and safety. These valueshave been a tradition since a long time ago and according to the organization, the companyculturehasbecomeauniqueasset,whichishardforcompetitorstocopy[7].

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2.3VolvoConstructionEquipment,WroclawTheassembly line forbackhoe loaders (BHL) inWroclawstarted in2002.ABHLhasa loaderunitinthefrontandanexcavatorunitintheback,seeFigure2.1.Thefactoryhas210employeesatthemoment.TheBHLthatisassembledinWroclawwasdesignedfromablanksheetofpaperwith direct input frombackhoe customers around theworld. There are twomodels ofBHL’s,BL61BandBL71B.Thelatermodelhasalargerengineandstrongerhydraulicpumpscomparedtotheprevious.Onemachineconsistsofaround2,100parts,requiresaround3,000operationsandhasaleadtimeofthreedays.Eachdaysixmachinesareassembledandduringhighseasonuptotwelvemachinesareassembled.Tocustomizethemachinethereare150options[8].

Figure2.1exhibitsabackhoeloader[9].

2.4TheVolvoProductionSystemTheVolvoProductionSystem(VPS)isacommonbasicsystemforemployeeswithinTheVolvoGroup. It is both a framework formanufacturing operations and a toolkit includingmethodssuchasLeanandSixSigma[10].TheVPSAcademyensuresthatthesystemis introducedandusedwithinTheVolvoGroup.TheaimofusingVPS is toachieveworld classperformance, tocreate value for customers, for the employees and for the owners. This is accomplished bysecuring quality, delivery times, safe and environmentally‐soundworkplaces, reducing wasteandusingbestpractice[8].ThefiveprinciplesforVPS’svisionare[10]:

Processstability‐reducevariabilityandwasteandstriveforpredictabilityandefficiency. Teamwork–allemployeesarepartoftheimprovementprocess. Built‐inquality–doingthingsrightthefirsttime. Just‐in‐time–therightthingontherightplaceattherighttime. Continuousimprovement–along‐termapproachtoalwaysimprovetheprocesses.

TheVPSmodelconsists,apartfromthefiveprinciples,ofthecustomerandTheVolvoWay.

ApartfromensuringtheuseoftheVPS,theVPSacademyalsoassesstheperformancelevelatallplantsintheVolvoGroup,recommendimprovementopportunitiesandhowtoimplementthemtoreachthenextperformancelevel[11].

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3 MethodologyThischapteraimstoexplaintheresearchmethodsandtojustifyeachchoiceofmethodologyusedinthisthesis.First,thescientificviewandresearchmethod ispresented,afterwhichthevalidity,reliabilityandcredibilityoftheworkisdiscussed.

3.1ThescientificviewDependingonfromwhatviewaresearchstudyisperformeddifferentmethodscanbeusedanddifferent results gained.Therefore, it is crucial to account for theviewused.Arbnor&Bjerkepresentsthreedifferentapproachesthathavebeengenerallyaccepted;theanalyticalview,thesystemsviewand theactorsview [13].All of the viewsdiffer inhow the reality is perceived.Belowisabriefintroductionofeachview.

3.1.1 TheanalyticalviewAccordingtoArbnor&Bjerke, theanalyticalviewpresumesthat thereality is filledwith factsandthatitisthesameregardlesswhoobservesit[13].Therealitycanbeseenassummativeandallthepartsinitmakethewhole.Thismeansthatbyunderstandingtheparts,conclusionsaboutthewholesystemcanbedrawn. It is important for theresearcher to influencetheresearchedobjectaslittleaspossiblewhenapplyingtheanalyticalview.Also,whenapplyingthisviewitiscommontousequantitativedatafortheanalysis.

3.1.2 ThesystemsviewUnlike theanalyticalview,realitycontainssubjectiveopinionsofsuchstructures,except fromfact‐filledstructuresthatareobjective,andthosearealsotreatedasfactsinthesystemsview.Duetothis,Arbnor&Bjerkearguesthattherealityisnotsummative.Whiletheanalyticalviewtreated the parts of the systems as independent of each other, the systems view takes intoconsiderationsthepotentialsynergies,orthenegativeeffects,thatcanoccurwhenpartsinteract[13].Therefore,itisimportantfortheresearchertotrytounderstandtheinteractionsbetweenthedifferentpartsinthesystem.Bothquantitativeandqualitativedataareusedfortheanalysiswhenapplyingasystemsview.

3.1.3 TheactorsviewTheactorsviewpresupposesthattherealityisasocialconstruction.Tounderstandthewholesystemitisofgreatimportancetostudythesingleindividualsandtounderstandtheirbehaviorsaccording to Arbnor & Bjerke [13]. When applying the actors view it is common to usequalitativedata.Theresearcherparticipatesintheobservationasoneofthepartsinthesocialconstruction.Theresultgainedwhenusingthismethodistherebydependentontheresearcherandhisorhersocialcontext.

3.1.4 ThescientificviewofthisthesisThescientificviewchosenisthesystemsview,whichconsidersdifferentpartsofthesystemtointeractandaffecteachother.Consideringthemethodworksamplingthehumanfactorneedstobetakenintoaccountsinceit isbasedonobservationsmadebyhumans.Also,consideringtheresearchregardingMCDandothermanufacturingmodels,muchoftheworkismademanuallyand the human factor needs to be taken into account. Therefore the analytical view is notsuitable. The actors viewwould imply that the results from the research is dependent of theauthorssocialcontextandwilldifferdependingonwhoconductsthem,whichisnotdesirable.

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3.2ResearchmethodWhenconductingaresearchstudyitisimperativetodecidewhatresearchmethodtouse.Themethoddependson thenatureof the studyanddetermineshowresultswillbeachievedandhowtoanalyzedata.

3.2.1 QuantitativeandqualitativedataAquantitativeanalysiscanbeconductedonresearchwithnumericaloutcomeand,asHöstetal.argue, quantitative data is often processed through statistical analysis [14]. Quantitativetechniquescanbeusedforimprovedunderstandingortoshowrelationsandhypotheses. Themeasures used to explore and describe quantitative data are average value and variance, ameasure of deviation. Numerical data can also be described in histograms, box‐plots or xy‐graphs.Thecorrelationcoefficientcanthenbecalculatedtoexploretherelationoftwofactors,whichdescribestowhatthefactorscorrelate.

AnimportantaspectthatHöstetal.pointsoutistoinvestigateifthedatacontainanyincorrectvalues due tomisunderstandings,measurement errors and such [14]. A typical datamust becorrectedearlyintheprocessandcanbeidentifiedinbox‐plotsorxy‐graphs.

Inaqualitativemethodthedatatoanalyzearewordsanddescriptions,andcanthereforenotbequantified. A quantitativemethod aims to get a deeper understanding of an area based on asmallnumberofresearchobjects,accordingtoHöstetal.[14].Thedatasuitableforaqualitativemethodaretextdocuments,e.g.transcribedinterviewsorarchivematerial.

Qualitativedatacollectionisaprerequisitetogetadeeperunderstandingoftheresearchobject.Hence,theprimarilymethodwillbequalitativetounderstandthecurrentsituation,findingoutwhatdatatocollectandthemethodsusedtocollectitatVCE.

Astheprojectproceeds,themethodwillaimtowardsaquantitativemethodwhenquantitativedata is collected. The analysis of collected data will be based on quantitative data. Manualcollectionofquantitativedata iserrorprone inawiderextent thanautomateddatacollectionandifsuchmeasureddataisnotcorrectfromthebeginningthereissubsequentlylittlechanceofcorrecting it afterwards. Hence, manual data need to be collected and documented correctlyfromthestart.

3.3DatacollectionThesourcesof information inthisthesiswillbe literaturestudies, interviewsandquantitativedata. According to Yin there are six sources of information for a study; documents, archivalrecords,interviews,directobservations,participantobservationsandphysicalobjects[15].

3.3.1 PrimaryandsecondarydataTwotraditionaltechniquesforcollectingdata,accordingtoArbnor&Bjerke,arecollectingnewdata, so‐called primary information and secondary information [13]. Primary data are datadirectlyconnectedtotheconductedstudysinceithasbeendesignedforit.Primarydatacanbecollectedthroughinterviews,observationsorexperiments.Secondaryinformationispreviouslycollected data, which has originally been collected for another purpose. The drawback withsecondarydata is thetrustworthinesssincetheresearcherscannotbesuretowhatextentthesecondarydataarecorrect.

8

Interviews and collected data will be primary data while literature studies of differentmanufacturing cost models are secondary due to not being information brought up for thisthesis.Alargepartofthesecondaryinformationisarticlesusedtogetadeeperunderstandingofdifferentmethodswithinmanufacturingsystems.

3.3.2 InterviewsConducting interviews is a source of background information and can be described as asystematic interrogation of someoneon a certain subject. The interview can be conductedbyphoneorinadirectmeetingandtheanswerscanbedocumentedeitherbyhandorbyanyaudiomedium.Höstetal.claimthatifaninterviewisapartofaqualitativestudytheselectiondoesnothaveafocusonrandomselectionofthepolled[14].Instead,itisimportantthattheselectioncoversthevariationofthepopulation.Therefore,theselectionismadebystratification,i.e.thepersonstointerviewarechosenfromanumberofcategories.Sincetheselectionisnotrandom,itisnotpossibletodrawgeneralizedconclusionsaboutthepopularityfromwhichtheselectionhasbeenmade.However,itispossibletoexploretheareaquantitatively.

Höst et al. present three different interview structures; unstructured, semi‐structured andstructured[14].Anunstructuredinterviewisusedinanexploringpurposewherethediscussionisbasedonan interviewguidewithquestionareas insteadofclearquestions.Theriskof thismethod is that thepolledcancontrol the interviewby talkingmoreof certainareas, trying toavoidotherareasonpurpose.Tomakesurethateachareagetsenoughattention,makesuretodedicate time for each area. An unstructured interview is a qualitative method resulting indescriptivedata.Secondly,thesemi‐structuredinterviewhasamixofquestionareasandclearquestions. The purpose of such method is to get describing or explanatory data. Finally, astructured interview is similar to an oral questionnaire with prepared, clear questions. Theadvantage,comparedtoawrittenquestionnaire, isthatthepolleddoesnothavetofilloutthesurvey themselves and answers can be clarified. The disadvantage is that the survey ismoretime‐consumingtoconductcomparedtohavingawrittensurvey.

Aninterviewcanbedividedinfoursteps,argueHöstetal.[14].Thefirststepistopresentthecontextandexplainwhatthepurposeoftheinterviewis.Thepolledshouldbeinformedofhowthe data will be processed. The next step is to ask initiating, simple questions to start theconversationand to get thepolled in to the right context.Themainquestions should thenbeaskedina logicalorder.Finally, theinterviewshouldbesummarized,whichgivesthepolledachancetoaddsomethingthatseemstobemissing.

The first step for this thesis will be to investigate the current situation to get a deeperunderstandingofhoweverythingworksandthustheveryfirstinterviewswillbeunstructured.Subsequently, the interviewswill bemore structuredwhen having a better understanding ofwhich information is missing. When collecting raw data for manufacturing cost models thesurveyswillbeclearlystructured,filledoutbytheauthorsandothers.

3.3.3 LiteraturereviewAliteraturestudyisrelevanttoimprovetheknowledgeoftheauthorswithinthearea.Itisalsoimportant foran independentreader tobeable toget insight into thesubjectandto facilitateunderstandingofthecaseconcept,analysisandconclusions,argueHöstetal.[14].

9

The literature review used for this thesis contains terms, models and other tools, which arerequiredtofulfillthepurposeofthisthesis.

Literaturestudies relyonsecondary information,which imposesa thoroughunderstandingofvalidityandreliability.Hence,thetheoreticalframeworkofthisthesiswillbebasedongenerallyacceptedtheoriesandmodels.

3.4CredibilityThe credibility ensures the trustworthiness of the study and depends on validation andreliability,whichisdiscussedbelow.

New research is subject for scrutiny; hence, it is in the researcher’s interest to ensuretrustworthiness of a study. Howwell the researchmethodologymeasures the characteristicsthattheresearcherintendstodescribeisreferredtoasvalidity.AccordingtoYin,therearethreedifferentmethodstoinvestigatethevalidityofastudy.Themethodsareconstruct,internalandexternalvalidity.Constructvalidityisaboutconstructingaccuratemeasuresoftheinvestigatedtheoryandavoiding subjective judgments in thedata collection.Lettingkey informantsverifythe facts or havingmultiple sources of evidence strengthens the construct validity. Secondly,internalvalidityconcernscausal‐effectrelationships,e.g.howcertainincidentsarerelated.Thisis amethod that only applies to explanatory and causal studies. One tactic to attain internalvalidation is to conduct pattern‐matching, explanation building and time‐series analysis. Thethirdmethodtoinvestigatethevalidityofastudyistomakeexternalvalidations,whichdescribetowhatextentastudycanbegeneralized.

Reliabilityreferstoobtainingreliableresultsgeneratedbythemethodusedaswellasdrawingwell‐underpinnedconclusions.Itcanalsobedescribedastowhatextentthesameresultscanbeobtainedwhen repeating the research. To obtain high reliability the data collectionhas to beaccuratelyconducted,argueHöstetal.[14].

Validity concerns how well the research methodology reflects reality and will be assuredthrough feedback from the supervisor at the university and at VCE, who has been presentthroughouttheresearch.Toensurethereliability,sourcesfrompeerreviewedscientificjournalsandliteraturearechosen.NoneoftheauthorsofthisthesishaveworkedforVolvobefore,whichisanotherfactorenhancingthereliabilitysinceitdecreasestheriskofsubjectivity. 

3.5TheprocedureofthestudyThefirstweekatVCEwasspentontheassemblyshopfloorwiththemainpurposetoseeandunderstand theprocesses.Workmethods,physicaland information flowwasobservedduringthisweek,whichareallfactorsofimportanceforunderstandingthesituationatVCE.

Tomasz Maleszka, Manufacturing Manager and supervisor at VCE, introduced a number ofpersonsfromtheproduction,quality,logisticsandfinancedepartmentsthatwouldbehelpfulforthe thesis and contribute to a holistic view of VCE. Interviews, unstructured and semi‐structured, andmeetings were held in order to identify the current situation and the issueswithin the research area. The literature review has been of great importance from the verybeginning. The Internet search engine used for the review was Lovisa, the catalog for Lund

10

University libraries. The key words used for literature research were; Manufacturing CostDeployment,WorkSamplingandRatioDelay.

InformationregardingMCD,howitworks,whatthecurrentstateatVCEisandwhichproblemsVCE is facing concerningMCDwas gained throughmeetingswith both TomaszMaleszka andPrzemekGorazd,BusinessControlleratVCE, interviewswithpersonswhoworkswithMCDatVolvoPowertrain,literaturereviewandacompanyvisittoNewHollandinPlock,Poland.NewHolland in Plock constructs harvesting equipment and the plant has similar processes to theonesatVCE,whichiswhyMCDatNewHollandwasofinterest.MCDhasbeenusedtheresince2008andavisittotheirfactorywasapartofbenchmarkingforthesubject.

Researchconcerningworksamplingwasconductedbyinterviewsandliteraturereviews.Togeta deeper understanding of the work sampling method, the authors participated in thepreparationsandexecutionofthelatestroundofworksamplingatVCE.

11

4. ManufacturingcostmodelsThis chapter startsby introducing themodel calledmanufacturing costdeployment.The theorybehindthemodelwillbepresentedfirst,followedbyhowVCEusesitandananalysisofthecurrentsituation andwhich actions should be taken to improve the performance of themodel at VCE.Finally,alternativemethodsarepresentedandcomparedtomanufacturingcostdeploymentanditisdescribedhowthealternativemethod,thatcouldbesuitableforVCE,canbeimplemented.

4.1ManufacturingcostdeploymentThere are various manufacturing cost models and manufacturing cost deployment (MCD),developedbyYamashinaandKuboin2002,isoneofthem.VCEhasconductedtwoloopsofthismethodbutdoesnotachievesignificantresultstobenefitfrom.

4.1.1ManufacturingcostdeploymentMCD is a methodology that establishes a cost‐reduction program scientifically andsystematically.Thepurposeistouseitforaproductionsystemwithmultiplefacilities[16].Itisa method to investigate the relationship among cost factors; processes generating costs andvariouskindsofwasteandlosses.Italsoaimstoclarifytheavailabilityoftheknow‐howofhowtogetridofwasteandloss.MCDisthenusedtoranktheitemsforwasteandlossaccordingtopriority based on cost and benefit analysis. Finally, remedies for improvements are to beidentifiedaswellascostofimprovementsandexpectedcostsavings.ThegeneralideawithMCDis to create six matrices based upon data input concerning waste and losses from amanufacturingcompany.AccordingtoYamashina&Kubo,MCDsolvesthefollowingissues:

Identifyingproductionlosses;resultandcauses Findingrelationsbetweenlossreductionandtheirpossiblecostreduction Clarifyingiftheknowhowoneachlossisavailable,ifnotitneedstobeobtained Estimatingthecostofreduction

AccordingtoVolvo,wasteisdefinedasthrowingabucketofwaterawayfornothing,whilstlossisdefinedasaleakingtap[17].Botharenon‐valueadding,butshouldbeseparatedsincetheyoccur for different reasons. Losses can be categorized into causal losses; loss caused by aproblemofaprocess,equipmentorpeoplethatcanbedirectlyidentified,andresultantloss;losssuchasmaterial,manpowerorenergy,whichareresultsfromcausallosses.Typicalwasteandlosscouldbewithinanyofthefollowingareas:equipment,material,labor,quality,environmentorlogistics.TheprocedureofMCDcanbedividedintosevensteps[16]:

1. Collectdataandestablishtargetsforcostreduction.2. Identifylosses,A‐matrix.3. Separatelossesintocausalandresultant,B‐matrix.4. Translatelossesintocosts,C‐matrix.5. Identifymethodstoeliminatelosses,D‐matrix.6. Estimatecostforimprovementandcostreduction,E‐matrix.7. Establishimprovementplanandimplement.F‐matrix,follow‐up.

Yamashina&Kuboexemplifiessomelosseswithinthreecommoncategories[16]:

12

Facility:

Breakdown Setup,toolchange Startup Shortstoppage Speeddown Defectivelosses

Operator:

Management Operatingmotion Logistic Lineorganization Measurementlosses

Material:

Yield Indirectmaterial Dieandjig Energy

13

Thematricesaredescribedinthefollowingsectionsbelow[16].

AmatrixTheAmatrixistheloss‐wherematrix.Itdescribeswhichlossappearsinwhatprocessstepandtowhat extent. The loss types are on the vertical axis and the horizontal axis represents theprocesssteps.Thelossesareranked;1,2or3where3hasthehighestpriority,2thesecondand1thethird,seeTable4.1foranexampleofanAmatrix.Ifasquareisemptythereisnoneedforlossreduction.

Table4.1exhibitsanexampleofapartofanAmatrix[16].

BmatrixTheBmatrix, shown in Table 4.2, is used to clarify the cause‐effect relationship of identifiedlossesintheAmatrix.Toreacheffectivesolutionsforlossreductionitisimportanttofocusonthecausallossesandnottheresultantones.

Table4.2exhibitsanexampleofapartofaBmatrix[16].

CmatrixThelossesareconvertedintocostsandthenusedforCmatrix,seeTable4.3.Itisusedtomaketheconnectionsbetweenlossesandcostvisible.Therearetwotypesoflosses;thetime‐relatedlosscanbemeasuredintermsoftime,suchasmachinestoppages,andthephysicallossthatcanbemeasuredintermsofquantity,suchasnumberofdefectives.TheCmatrixproducesasetofdata where the cost type, process step and loss type can be analyzed. The results can bepresentedinParetocharts,forinstance.

Storage

Drilling

Turning

Hardening

Cleaning

Packing

Storage

1.Plannedmaintenance 1 12.Breakdownandstop 1 1 1 33.Machineprocess 14.Start‐up 15.Changeover 1 1 36.Otherdowntime 27.Speed 3 28.Machinere‐work 2

AMATRIX

(I)MachineLosses

StartupTurning Hardening Cleaning Drilling Turning Hardening Turning Cleaning

Storage X XDrilling X XTurning X X XHardening X X XPacking X X XClearing X X XStorageStorage XDrilling XTurning XHardening XPacking XClearing XStorage

Resultantlosses

Machinelosses

Break‐down

Process

BMATRIX

Causal lossesCausal machine losses

Breakdown and stop Machine process Change over

14

Table4.3exhibitsanexampleofapartofaCmatrix[16].

CMATRIX

CausallossFixedcost Variablecost

TotalDepreciationcost Directmaterialcost ... Costk ...

Machine

Breakdow

nloss

Process

1 ... ... ... ... ... ...... ... ... ... ... ... ...j ... ... ... ... ... ...... ... ... ... ... ... ...J ... ... ... ... ... ...

S ... ... ... ... ... ...

... ... ... ... ... ... ...Lossl j ... ... ... ... ... ...... ... ... ... ... ... ...

Total ... ... ... ... ... ...DmatrixDifferent losses require different improvement strategies. The D matrix, see Table 4.4, isnecessary toconsiderwhich improvement techniques thataresuitable foreach loss. It isalsousedtoidentifywhichKPI´sthatwillbeimpactedbyeachaction.Generally,therearetwokindsof approaches to choose between when dealing with the losses. The first is the focusedimprovement approach, which focuses on eliminating waste in the short‐term. The secondapproachisthesystematicapproach,whichrequirescontinuousactivitiesinthelong‐term[17].

Resultantloss

15

Table4.4exhibitsanexampleofapartofaDmatrix[17].

EmatrixTheEmatrix,seeTable4.5, isaprojectsummarywhichshowstheestimatedcostofhandlingandimprovingthelossesaswellastheexpectedcostreduction.

Table4.5exhibitsanexampleofapartofanEmatrix[17].

FmatrixThe F matrix is used as a planning tool to make sure that the chosen projects will beimplementedandthattheresourcesarewellbalanced[17].Table4.6showsanexampleofwhatanFmatrixcouldlooklike.

Breakdownanalysis

Setuptimereduction

Toollifeim

provem

ent

Startuptimereduction

Shortstopanalysis

PManalysis

Cycletimereductiom

Cp,Cpkim

provem

ent

Operationmethod

Layoutim

provem

ent

Inspectionmethod

Yieldimprovem

ent

Materialsavingmethod

Energysavingmethod

Operativemaintenance

Preventivemaintenance

Predictivemainentance

Qualitymaintenance

Qualityassurance

Educationandtraining

1 X X X X

…

j X X X X X X

…

J X X

…

1 X

…

j X

… X X XJ X X X X

…

Process

Breakdown loss

Defective loss P

rocess

Machine

S

S

SystematicapproachIndividual approach

Causalloss

DMATRIX

Workshop

Projectdescription Processstep Losstype

Year0loss

Teamleader St

artdate

Plannedcompletion

Duartion[months]

Plannednotbenefit

Projectsponsor

Actualcoststodate

Actualnetsavingstodate

Plan.savingsyear1

Acutalsavingsyear1

Plan.savingsyear2

Actualsavingsyear2

Plan.savingsyear3

Acutalsavingsyear3

Costreduction(plan.)

Costreduction(actual)

Utilization(plan.)

Utilization(actual)

Yield(plan)

Yield(actual)

Costreduction(plan.)

Costreduction(actual)

Utilization(plan.)

Utilization(actual)

Yield(plan.)

Yield(actual)

Costreduction(plan)

Costreduction(actual)

Utilization(plan)

Utilization(actual)

Yield(plan)

Yield(actual)

BP

Reducebreakdownsinthecascadesectioncausedbylackofbasicconditions

DryerBreakdownandstop

BP

Reducebreakdownsinthedryeroutfeedcausedbyafailuretorestore

DryerBreakdownandstop

BP

Reducebreakdownsatthebundlercausedbyhumanerror

Trimandtape

Breakdownandstop

… … … …

FromDmatrix Benefitting KPI(s) year 1 Benefitting KPI(s)year2 BenefittingKPI(s) year 3

EMATRIX

Resultantloss

16

Table4.6exhibitsanexampleofapartofaFmatrix[17].

4.1.2 CurrentsituationandidentifiedgapsMCDisarelativelynewmethod,whichhasbeenimplementedatVCEtwoyearsago.Themethodis also used at Volvo Powertrain, where it was implemented successfully together withYamashina,accordingtoGorazd[18].TheconcepthasbeencopiedtoVCEandduringthetwoprevious years themethodhas been conducted twice, but it is still not usedwith confidence.Today it is more of an administrative task than a method for evaluating and improving theperformanceatVCEsincetherehasbeennoresultintermsofaction.

Thematrix‐templatesusedatVCEoriginsfromVolvoPowertrainbuthavebeenmodifiedinanattempttomakethemsuitablefortheplant.OnesizedoesnotfitallandthesameappliesfortheMCDmatrices.ThefoundationforMCDissixmatrices,whichareexplainedinsection4.1.1,butVCEonly uses the firstmatrices:A, B andC. The last steps ofMCDhave been treatedduringmeetingsbuttherewerenoofficialresultsintermsofcostreduction,claimsGorazd[18].HealsoclaimsthattheanalysisofMCDconfirmedwhatwasalreadyknownaboutwherelossesoccur.

ThereisalackofdedicationofusingMCDsincetheprojectresponsibleforMCDdoesnotbelieveintheconcept.Asmentionedaboveitismoreofanadministrativetask,somethingthatshouldbedoneindependentofitscontributions,andwithoutresultsitbecomesalossinitself,anon‐value adding activity. One of the keys for a successful MCD, and this is true for successfulimplementationofanymethod,istomakesurethatthepersonnelinvolvedaredevoted.

Thepurposeof theAmatrix is to show losses andwhere theyoccur. Since theprocesses aredividedamongmanystationsintheassembly,theprocessesintheirmatrixhavebeendividedintoassemblyareassuchasmainline,subassembly,cabassemblyandafewworkareas.Theseareas containmany stations and thus it is hard to identify the area in which the root causeoccurred.TheBmatrixistobebasedonthepreviousmatrixandiscomplextogatherinputforsince it is supposed to reflect how losses in processes affect losses in other processes. Thisreflection ismissing inVCE’smatrix,whichsimplyconnectsaresultant losswithacausal lossanddoesnotincludetheaffectionoflossesonotherprocesses.TheCmatrixissupposedtobebasedon theBmatrix, but since thismatrix is not correctlymade it results in an incorrect Cmatrix.Infact,theAandCmatricesarealmostidentical.Themajordifferencesisthattheoutputfor theAmatrix are losses, ranked1,2or3, and theoutput for theCmatrix are costsof thelosses. Also, there are more process steps in the A matrix than in the C matrix. Equipmentbreakdown,waitingandlinebalancingareshownonthey‐axiswheninfactwaitingisalossthatcanoccurwhenthelineispoorlybalancedorwhenequipmentbreakdowncausesastopofthe

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

WorkshopProcessstep Losstype

Projectdescription

Projectno.

Projectleader

Plannedsavings

Plannedcosts

Projectresources(man‐hours) 23

/01/2007

05/02/2007

14/02/2007

06/03/2007

21/03/2007

06/04/2007

25/04/2007

18/05/2007

21/05/2007

07/06/2007

24/06/2007

05/07/2007

15/08/2007

31/08/2007

09/09/2007

22/09/2007

10/10/2007

31/10/2007

06/11/2007

26/11/2007

08/12/2007

15/12/2007

03/01/2008

10/01/2008

BP Mixer Breakdown Mixerarea 2 TO

BP FormingProcessdowntime

AM:Formingbelttransfer 3 FG

BPDunnagemachine

Lineorganisation

Reducebreakdowns 4 MO

BP PackingMaterialprocess

AMFI:Takeoffpart1 5

BP PackingMaterialprocess

AMFI:Takeoffpart2 6

Step 0 Step 3

Step 0 Step 1

Step 1 Step 2

FMATRIX

Step 2 Step 3

Step 3Ongoingprojects

Ematrixprojects

Step 0 Step 1 Step 2

Step 0 Step 1 Step 2 Step 3

Step 0 Step 1

17

line. If noaccount is takenof this it leads todouble counting for someof the losses,which iscurrentlythecase.

VCE bases one loop of MCD on data from three months. Three departments at VCE arecontributing with input for the matrices; quality, logistics and assembly. Each department isresponsibletofill inthecostforthelossesconcerningthem.Therearenostandardsofhowtocalculatethedifferentcoststoensurethevalidityoftheresultsortobeabletocomparethemfromyeartoyear.Forinstance,whencalculatingthecostoftransportduringthecurrentperiod,there are different ways of doing it. The information of how much time is spent ontransportationisavailableasapercentageofthetotaltimeofdirectwork.Oneworkdayiseighthours,480minutes,andplannedbreaksandmeetingsare47minutesaday,whichmeansthattheactualdirecttimespentonworkcorrespondsto433minutesaday.Duringthelatestloopthe transport timewas based on aworkday of 433minuteswhile the loop before based thetransporttimeonaworkdayof480minutes.Shouldthetransporttimebebasedonthewholeworkday,theexpecteddirectworkintheproductionorshouldstoplines,kaizen,extrameetingsetc.alsobetakenintoaccount?Ifastandard isnotset, there isariskofcalculatingthe lossesdifferentlyandacomparisonbetweenresultsfromtwodifferentloopswillnotsaymuchaboutthepotentialimprovementsmadeandintheendtherewillbedoublecounting,i.e.losseswillbebeenrepresentednotoncebuttwice.

The factoryatVCE includes fewmanufacturingoperations,mostoperationsareassemblyandtheyaremademanually.Hence,thelevelofautomationislowanddatainputforMCDhastobegathered manually as well. Raw data input collection is made with a method called worksampling,seechapter5.

4.1.3 ImprovementpossibilitiesThefirststeptoimprovetheworkwithMCDatVCEistoconvincethemanagementteamabouttheimportanceofusingMCDandwhatthebenefitsofusingitimplies.Theteaminvolvedneedstobefullydevotedtothemethodandbelievethatitcanmakeachange.Unlessthemanagementisnotonehundredpercentcommittedtothemethod,VCEwillneverget thebenefitsofusingMCD and it will continue to be a time‐consuming method causing more losses than it willcontribute to reduce. In the beginning itwill require both effort and time to fully implementMCDbutwhenthatisdone,whentheconceptfordatacollectioniswell‐functioningandwhenimprovementsarestartingtoshow,MCDwillrunsmootherandmostimportantly;seeingresultsintermsofsavedcostswillkeepupmotivationforcontinuing.

AllpartiesinvolvedinMCDshouldhavetheunderstandingofhowthemethodworks,whyitisused and the benefits of it. As recommended in the next chapter about work sampling, it isessentialthatalsoteamleadersandoperatorsareconvincedabouttheadvantageswithMCDtokeepupmotivationforgatheringdataandlaterontoimplementimprovements.Theadvantageofhavingtheteamleadersconductingthesamplingsisthattheyarehighlyknowledgeableintheoperationsandiftheybecometrainedinthegeneralleanthinkingofvalueandnon‐valueaddingactivities, the team leaders can become an even more useful resource in the work forimprovements.

Atthemoment,therearenoexpectationsofachievingresultsfromMCD.IfthereisnopressureorobligationtoachieveresultstherewillbenomotivationtostriveforcompletingtheD,EandF matrices – which is where the real work and effort for results are starting to show.

18

Additionally,whenworkingwith improvements therehas tobeagoodsetof tools to find therightremedyforeachloss.

Theresultantlossesinthematricesaredividedintolargeprocessgroupssuchasmainline,sub‐andcabassembly.Havingsuchbroadcategoriesmakesithardtolocatewherethelossesoccur.Since the assembly is divided by stations and work areas, where one work area consists ofseveralstations,onesolutionistouseoneofthesecategoriesinsteadofactualprocessesinthematrices. When applying work sampling as input for MCD, an extensive amount of work isrequired to gather raw data for each station compared to theworkload of data collection foreachwork area. This is because there aremore stations thanwork areas, and sampling eachstationwould therefore require an extensively largernumberof observations to reach ahighreliabilityofresults.Therefore, it isrecommendedtoputtheworkareasasprocesscategoriesinsteadofmainlineandsubassemblies.

ResultantlossesinthematricesaresometimestreatedascausallossesinVCE’smatrices,whichresultsintherootcausesofthelossesnotbeingidentified.Causallossesneedtobewelldefinedanddistinguishedfromtheresultantlossestocreateprerequisitesforfindingasolution.

To make it easy to follow and understand the matrices it is essential to include explainingcommentsinthem;itisbetterhavingtoomanythantoofew.Also,thecommentsneedtobeeasytofindandread.Ifcommentsarefarawayfromwhatiscommentedthereisariskthattheywillnotbeusedatall.Thecommentswillmakeiteasytogobackandseewhathasbeendoneandwhy.Theuser‐friendlieramethod is, theeasier it is tokeep theparties involvedmotivated. Ifthere is littlemotivation to use themethod there is a risk of having data inputs of uncertainqualityandresultsthereafter.Objectivesneedtobesettohaveagoaltostrivefor,e.g.identify30%ofthecostsaslossesandreducethelosseswithfivepercent.

Standardsofhowtocalculatecosts in theCmatrix shouldbeset toensure thevalidityof theresults.ThiswillalsoenablethecomparisonandevaluationofresultsfromearlierloopsofMCD.Double counting of causal losses in the matrices creates a false image of reality and shouldconsequentlybeavoided.Astheexamplewithcalculatingtransporttime,mentionedinsection4.1.2, transport for instance can be based on 433 minutes per workday excluding time forKaizen,QPRS, linestoppagesetc.wheretransport isnotperformed.Thiswould implyabettervalue for the cost of transportation in the C‐matrix. If the time for Kaizen, QPRS and linestoppagesarenotexcluded,itwillindicatethatmoretimeisspentontransportationthanitisinreality. Concurrently, the time for kaizen, QPRS and line stoppages is counted as losses, butunderothercategories,whichimpliesdoublecounting.

TheAmatrix is currently, and originally, using a scale of 1, 2 and 3 to prioritize losses. Thisimplies that three losses with the lowest priority will be as important as a loss having thehighest priority, which could create misleading results. To separate the losses better, onerecommendationistoratelosseswith1,3and5instead,whichmeansthatlossesofthehighestpriority will also be highlighted the most. Also, there cannot be too many losses having thehighestpriority,whichmeansthattherewillhavetobeaprioritywithinthehighestprioritizedlosses.AccordingtoLundgren[19],agoalisnottohavemorethan10%losseswiththehighestpriority.Thisistobeabletofocusandnotloosinggripbecauseofhandlingtoomanylossesatthesametime.

19

EachandeverylossidentifiedintheAmatrixshouldbeinvestigatedtodocumenttheaffectionof each loss on other processes. This is to increase the understanding of the processes, butprimarilytocalculatetherealcostoftheloss.SincetheBmatrixisbasedontheAmatrixandtheCmatrixistobebasedontheBmatrix,theybecomeincorrect.Aprerequisitebeforestartingtomakeshortcutsinthemethodistoknow,understandandhaveexperienceinthemethodology.

Whenitcomestoremediesoflossesthereisaneedforgreattransparency.Ifonelosshasbeenremedied it could affect another loss and result in having moved loss from one category toanother.Also,iftransportdecreasesonsomestationsbutnotonstationswiththehighestcycletimes,thiswillonlyimplymorewaitingwhichiswhyitisimportanttorebalancethestationstogainfullbenefits.Thus,lossrelationsneedtobeidentifiedinthematrices.

MCD should not be seen as something additional to the already established methods forimprovementworkatVCEoranadministrativetaskmadefortheVPSacademy.Itshouldbearegularpartoftheimprovementworkjustaskaizenand5Sareforcontinuityandproceedingwork for improved results. Therefore, it is also important that themethods for collecting thedatawillbewellestablishedandsystematic.MCDcannotbeamethodusedonlyonce; firstly,because it is hard to get good results from the first loop and secondly, because there are nopreviousresultstocomparetheresultsto,whichisanecessitytomeasuretheprogress.Also,asimprovementsareachieved,newproblemswillarisewithneeds fornew improvements.AfterseveralloopsofMCDtheenhancementsofusingitcanbeidentifiedanditisnotuntilanumberofloopshasbeencompletedthatMCDcanbefullymastered.

The matrices for MCD will eventually grow quite big, in fact both matrix A and C are quiteinconvenient to look at on the computer screen. During the visit at New Holland the visualadvantagesofhaving thematricesprintedon thewallswasdiscovered.Thematricesbecomemoretransparentandvisualizedcomparedtohavingthemonascreentobeseenonebyone.

4.2AlternativemanufacturingcostmodelsMCDisacomprehensivemanufacturingcostmodelbutitisnottheonlyone.ThissectionaimstoinvestigatewhetherthereisanothermanufacturingcostmodelthatwouldsuitVCEintheirstrivetoreducecosts.AcomparisonbetweenMCDandthealternativemethodsisdonetoseehow it differs fromothermodels, to visualize pros and conswith eachmethod,withinwhichaspectstheyaresimilarandinwhichtheydiffer.

Throughtheyearsmanydifferentmanufacturingcostmodelshavebeendevelopedtoevaluateproduction processes. Some models are more similar than others, some focus on specificproblemsandsomearemoregeneral. Jönsson [2]presents twelvedifferentmodels, includingMCD. Table 4.7 summaries themodels he presented and the corresponding parameters. ThemodelschosenforthecomparisonwiththeMCDaretheonesincludingthehighestnumberofparameters,sinceitisdesirabletogetthemostholisticviewpossible.ThemodeldevelopedbyStåhl[20]isoneofthetwelvemodels,butitisnotpresentedinthetable.Sinceitincludesthemajorityoftheparameters,accordingtoJönsson,itwillbeapartofthecomparison.Twomodelswills be compared toMCD, and themodel developedby Son [21] is the secondmodel that ischosenforthecomparison.

BoththemodeldevelopedbyÖzbayraketal.andtheonedevelopedbyChiadamrongincludethegreaterpartoftheparameters.ThemodelbyÖzbayrak[22]isanactivity‐basedcostingmodel

20

whereathirdoftheincludedparametersinTable4.7arenotincludedasseparateparameters,whyitwillnotbeincludedinthecomparison.ThemodeldevelopedbyChiadamrongissimilartotheonedevelopedbySon,butdoesnotincludeasmanyparametersandhasaheavyfocusonquality[23].BecauseofthesimilarityandSon’swiderperspective,themodelbyChiadamrongisnotincludedinthecomparison.

Tabell4.7exhibitsdifferentmanufacturingmodelsandwhichparameterstheyinclude[2].

Koltai et al. [67]

Özbayrak et al. [68]

Aderoba [69]

Dhavale [70]

Yam

ashina and Kubo

[75]

Son [76]

Chiadam

rong [77]

Cauchick-M

iguel and C

o ppini [71]

Branker et al. [78]

Noto L

a Diega et al.

[72]

Needy et al. [74]

Material x x x x x x x

Labor x x x1 x x x x x x

Machine depreciation x1 x1 x x x x x x1 x1 x3

Floor space x1 x x x x

Utilities (e.g., energy) x1 x x1 x x x x1 x

Tools x x x1 x1 x x x x x

Maintenance x1 x1 x1 x1 x x x x1 x1

Repairs x1 x x1

Material handling x x x1 x x x1 x

Computer x1 x1 x x1 x1

Inventory x x x1 x

Quality: prevention x2

Quality: appraisal x1 x x x x

Quality: failure (scrap) x2 x x x

Reworking x2 x x x1

Downtime x2 x x1

Speed loss x

Set up x x x x x x x x

Waiting x x1 x x

Idling x x x

Environmental x

1Included but not as a separate parameter. 2Mentioned as considered, but the equation is not presented in the paper. 3The cost is expressed as a leasing cost.

4.2.1 ManufacturingcostmodelbySonSon has developed amanufacturing costmodel that serves as support for strategic decision‐making for factory automation and has developed the integratedmanufacturing performancemeasure (IMPM), which is based on this model. Son argues that productivity, quality andflexibilityareboththemostcriticalcomponentsofmanufacturingstrategyandthemostcriticalmeasuresofperformanceofmanufacturingsystems,whichiswhythesecomponentsshouldbequantifiableandtransformedtofinancialterms.Thepurposeofthearticle,wherethemethodisprovided, is to “..identify cost elements which should be included in the analysis of advancedmanufacturing systems and propose a way of estimating them. Also discussed are variousapproaches to obtain parametric values of the cost model and applications to performanceevaluationandprojectjustification”[24].

21

Productivity,qualityandflexibilityarethreegroupsinwhichSondividescosts.Theproductivitygroup includes costs of labor,material, depreciation,machine, tool, floor space and computersoftware.Thequalitygroupincludespreventionandfailure.Flexibility,whichisthelastgroup,includessetup,waiting,idleandinventory[21].Thethreegroupsareregroupedintotwonewgroups;relativelywell‐structuredcostsandrelativelyill‐structuredcosts.Sincetheproductivitycosts isactual,concrete inputrequiredformanufacturingofproductstheyare includedintherelativelywell‐structuredcosts.Qualitycostsandflexibilitycostsareincludedintherelativelyill‐structured costs because of two main reasons; lack of knowledge and unwillingness toexploretheproblemindepth[21].

Son’smodel isused to calculate the total of each typeof costduring apredeterminedperiod.Belowtheequationsforcalculatingeachcostarepresented.

Thelaborcost,CL,isdefinedas[21]:

4.1

Thematerialcost,CR,isthecostofmakingthematerialreadyformanufacturing,suchasdirectmaterial, ordering, purchasing, transportation and lubricants. Thematerial cost is defined as[21]:

4.2

Themachinecost,CM,isdefinedas[21]:

4.3

Thetoolchangecostsisnotincludedinthetoolcostsinceifthechangeismademanuallyitisincludedinthelaborcostandtheautomaticallytoolchangesarenegligible.Thetoolcost,CT,isdefinedas:

∗ 4.4

Thefloorspacecost,CS,isdefinedas[21]:

4.5

The computer software cost, CC, is the cost of maintaining computer software and does notincludecostssuchassalariesofprogrammersandoperators,maintenance,repair,insuranceandspacecost forcomputerfacilitiessincetheyarealreadyincludedinothercosts.Thecomputersoftwarecostisdefinedas[21]:

4.6

Son has not defined the depreciation cost; instead he is referring to the accounting recordswhereitusuallyisavailable.

The prevention cost, CP, is defined as the cost of preventing defective finished products. Theprevention actions can be checking and correcting quality problems. The costs of sampling,

22

assignable cause and process capacity are included in the prevention cost per hour. Thepreventioncostforaspecificproductinaspecificmachineisdefinedas[2]:

4.7

The failure cost, CF, is the cost that occurs when the finished products do not meet qualitystandardssetbyboththecompanyandthecustomers.Itincludesthecostofreworkingagoodpart because of misclassification, reworking a defective part, scrapping and dissatisfying acustomerbysellingadefectivepart[21].Thefailurecostofspecificpartisdefinedas[2]:

4.8

Thesetupcost,A,isthecostofgettingthemachinesreadyforeachproductionrun.Thesetupcostforaspecificmachineisdefinedas[2]:

4.9

Thewaitingcost,CW,isthecostofwork‐in‐processinventoryandisdefinedas[21]:

4.10

Thecostforunder‐utilizationofmanufacturingequipmentistheidlecost,CI,whichisdefinedas[21]:

4.11

Theinventorycost,CH,onlyincludestheinventorycostsforrawmaterialandfinishedgoodsandisdefinedas[21]:

4.12

TheIMPMmeasuresmanufacturing‘’effectiveness’’andremediestheproductivityparadox[21].Theproductivityparadoxindicatesthatahighproductivitydoesnotnecessarilyresultinhigherprofitability. The efficiency of transforming tangible inputs into output is a productivitymeasurement.Itisnotaffectedoftheabilityofthemanufacturingsystemtoadapttocustomers’whimorproductsthathavenotbeensoldduetopoorquality.TheIMPMtakestheproductivitycost,qualityandflexibilitycostintoaccount.

4.13

4.2.2 SystematicproductionanalysisStåhl provides a manufacturing cost model called the systematic production analysis (SPA),whichincludesaproductionperformancematrix(PPM)[20].Thepurposeofthemethodis“tosetthefoundationforchangesanddevelopmentforaproductionsystem...toidentifythelosstermsofquality,downtimeandproductionrate”[25].Thematrixisbuiltupbyresultantsandcausesfortheresultantlosses.Ståhlalsopresentsacostrelationtoturnoutputintocostswhenprioritizinglosses,butoutputcanalsobepresentedintimeorfrequency.

Thetotalefficiency,equation4.14,isameasurebasedonthelossterms;quality,downtimeandproduction rate. The parameters are explained in Table 4.8. The Lean‐triangle in Figure 4.1illustrates the priority of effort between the loss terms when using total efficiency as the

23

objectivefunction.Thefirstpriorityshouldbetoeliminatequalitydeficiencies,qQ,afterwhichdowntime, qD, and rate losses, qP, should be addressed. According to Ståhl, solving capacityproblems by increasing production rate or investing in additional equipment or automationwhen having problemswith the losseswill only increase them [20]. After eliminating qualityproblems anddowntime the final step can be approached; increasing the production rate. Toavoid increasing quality disruptions or downtime it is required to undergo technicaldevelopmentandimprovedcompetencies.

Table4.8exhibitstheparametersfortotalefficiency.

Shareofqualityloss(‐) N0 Nominalbatchsize(numberofunits) Shareofdowntime(‐) Realproductiontime(h) Shareofproductionrateloss(‐) Nominalcycletimeperitem,including

downtime,butexcludingrateloss(h)N Realbatchsize(numberofunits) Realcycletime,includingrateloss,but

excludingdowntime(h)

1 1 1 4.14

4.15

4.16

4.17

Figure4.1exhibitstheLean‐triangle,whichillustratestheprioritybetweenthedifferentlossterms[20].

Environment and lifecycle issues are other important aspects in addition to the resultparameters presented in accordance with the Lean‐triangle since it contributes with animportant perspective in the assessment of production results and its effects. The resultparameters;quality,downtime,productionrateandenvironment,combinedwithseveralfactorgroupsconstitutethefoundationofthePPM.AtemplateforthematrixcanbeseeninTable4.9,wherehorizontalsummerygivesthecostforagivenfactorandverticalsummerygivesthecostforagivenresultparameter.Thefactorsarethecausesoftheresultparameters.Briefly,aPPMis used to study the relation between disturbances and the cause of themwith a structuredmethod.ThePPMcanbeanalyzedinthreeways;thesamematrixisused,butnumberscanbe

Developmentlevel

(1‐qD)2

(1‐qQ)3

(1‐qP)Productionratelosses

Downtime

Qualitylosses

24

translatedintofrequency,timeorcost.Independentofwhatanalysisischosenthemostcriticalresult parameters and their respective factor groups must be identified to undergoimprovements first. SPA and its PPM is primarily used in manufacturing industry and hasseveralpurposes:

Followupongoingproductiontofindopportunitiesofimprovement. Constitutethefoundationforconstructingfutureproductionsystems. Assessmentofresultandfactorparameterscanhelpwhenmakingdecisionsregardingworkmaterial,toolsandmachineryequipment.

Documentationofexperiencesandcompetencies.

ThefactorgroupsinthePPMconsistofmanyindividualfactors,whichcanbedividedintomoredetailedcategories:

Tools–geometrical,surfaceandmaterialrelatedfactors. Workmaterial–geometric,surfaceandmaterialrelatedfactors. Process–equipmentrelatedfactors,processdataandadditivesandotherpreparationrelatedfactorsfortherefiningprocess.

Personnelandorganization–instructions,actionplans,routines,administrationresponsibilitiesetc.

Wearandmaintenance–toolrelatedfactors,plannedandunplannedmaintenance. Specialfactors–uniqueprocesscharacteristics. Peripheralequipment–materialhandlingequipment,conveyersetc. Unknownfactors–acategoryusednottomixanythingintheotherfactorgroupsthatdonotbelongthere‐thatwouldaffectthequalityoftheirresults.Thenumberofregistersunderthiscategoryhighlightstheneedsforageneralcompetencedevelopment.

Table4.9exhibitstheprincipalstructureofaproductionperformancematrix[20].

Successful production development leads to releasing production capacity in the currentproduction system. To assimilating the results of this development the workload needs toincrease, else overcapacity increases and utilization decreases. Overcapacity can be used as areserve for increased demand, if it is possible to increase by market shares. If there is noforecasting of increased demand or other plans for assimilation of the increased capacity thedevelopment could cause closure of the production unit due to collocation with similarproductionunitsortheovercapacitywillhavebeenaninvestmentwithnoreturn.

4.2.2.1 DatacollectionforaproductionperformancematrixSPA has had greatest impact when being used as a foundation for continuous improvementwithincriticalproductionsections,accordingtoStåhl[20].HeprovidesthefollowingstepswhenapplyingSPAforproductionimprovements:

Factorgroups Qualityparameters Downtimeparameters Productionrateparameters Environmentandlifecycleparameters ∑FactorsA.ToolsB.WorkmaterialC.ProcessD.PersonnelandorganizationE.WearandmaintenanceF.SpecialfactorsG.PheriphericalequipmentH.Unknownfactors∑Resultparameters

Result parameters

25

1. Identify the result parameters and requirements critical for the function of the part,whichwillbe theparameters forcalculating thequalityparameters,qQ.Thedowntimeparameters,qS, are identified by disturbances causing downtime. Intended productionrateneedstobeidentifiedtonoteratelosses,whicharerepresentedintheproductionrate parameters. Consumption of supplies, process additives etc. are identified asenvironmentandrecycleparameters.

2. Identifyimpactingfactorsforeveryfactorgroup.3. Identifypotentialrelationsbetweenresultparametersandfactors.4. Identifyandprioritizerelationsbetweenresultparametersandfactors.5. Registertheeventsconnectedtotheresultparameterstofindtheunderlyingfactors.6. Analyzethecollecteddata.7. Constructanactionplantooptimizeandchangetheproduction,basedontheoutcomeof

theanalysis.8. Implementtheactionplan.9. Followupandevaluatetheimplementedactions.

4.2.2.2ConstructingtheproductionperformancematrixTheintroductorytaskwhenconstructingaPPMistoidentifytheresultparameters.Thequalityparameters for instance shouldbebasedon the reasons forwhyapart is scraped, e.g.wrongdimension,surfaceorproperty,arguesStåhl[20].Whensettingtheresultparameters it is thereasonforwhyanitemisscraped,notwhytheerrorarose,thatneedstobeidentified.

Downtime parameters can be divided into two main groups: planned and unplanned. Also,downtimedue to changeover,whichbelongs to the category of planned downtime, should bereportedseparately.

Theproductionrateparametershouldbebasedonthedecreasedrateduetoshortageofstaff,testing, leakage etc. Sometimes it is suitable to have different levels of rate loss, i.e. 25% ofintendedproductionrate.

Environmentandlifecycleparametersaresometimeshardtofindandtomeasure.Examplesofparameterswithinthecategoryarecuttingfluid,energyconsumptionandscrapmaterial.

WhencalculatingcostsforthePPMintermsofmaterial,downtimeandwage,thefollowingcostrelation, calculating the production cost per item, can be used. The cost relation should becalculatedforeachproductionprocessstepwherethelossesqQ,qDandqPcanbeidentified.

4.2.3 ComparisonMCDandSPAbothexpressaclearaimofstrivingfordevelopmentandalsoconsiderlossasanimprovement opportunity. The purpose of Son’s model does not discuss improvementopportunitiesorreducingcosts.Ingeneral,onewayofreducingcostsistoreduceandeliminatewasteandlosses,butneitheraretheresuggestionsofdoingsointhemodelbySon,comparedtothepurposesofMCDandSPA.ThefocusofSon’smodelliesmoreintheevaluationofwhetherornot theproductivity isprofitableusing IMPM,which isan importantaspect,but isoutsidetheframeofcostreduction.

WhatMCDandSPAalsoconsider is thecause‐resultantrelationshipbetweenthe losses,whileSon focuses on large and general cost items.With Son’smodel, it can easily happen that thevariouscost itemsarecomparedwitheachother,butthatwillnottellwheretheactualwaste

26

andlossesaresincethecausesofthecostsarenotidentified,aswithMCDandSPA.Forinstance,iftheresultforSon’smodelisthatmaterialcostisthehighestcostitdoesnotnecessarilyimplythat material cost has the greatest prerequisites for cost reduction, compared to other costitems.

WhenusingMCD,therecanbeasmanyorasfewlossesorwastesasisdesirabletoinvestigate,whileSonhasafixsetofcostitemsforevaluation.Itscostsincludemanyfactorsandfindingtherootcausesforthecostbecomesdifficult.ThemodelbySoncanthusbeseenasamoreshallowmethod than theother two, since itdoesnot seek to identify the cause‐resultant relationship.ThecostswithinMCDandSPAthoughareclearlyconnectedto therootcauses.Consequently,waste and losses canbe identifiedand theneliminatedor improved. It canbe concluded thatSonsmodeldoesnotfocusoneliminatingwasteandlosses,asforMCDandSPA.Instead,Sonsmodelshouldbeusedtoevaluatecostitemsovertime,e.g.tocomparematerialcostfromyeartoyeartoseethedevelopmentofcostitems.

MCDfocusesonreducingwasteandlossesandenhancingprocessesusingaholisticview,whileSPA focuses primarily on correcting deviations, e.g. scrappedparts andunplanneddowntime,and the secondary focus is to evaluate planned downtime such as set up and tool change.Eliminatingscrappedpartsanddowntimeimpliesreleasingproductioncapacity,whichcanbeusedinmanyways.TheprioritywithinSPAofprimarilymakingthecurrentprocessesdowhattheyaresupposedtoandsecondlyattackingtheprocesstoenhanceit,islogicalintheorywhilein reality itmay seem easier to invest inmore resources and equipment tomake up for themalfunctioning ones. This aspect is not discussed inMCD,where the provided approach is tostartwhere theareas identifiedhave thebiggestwasteand losses,according to theassessors.MCD suggests no clear method for how these areas would be identified more than fromestimationsandanalysis throughoccurrence frequency.AdisadvantageofMCD is that itdoesnottakethetotalmanufacturingcostintoconsideration,onlythecostsoflosses.StåhltakesthisintoconsiderationinthecostrelationwhileSon’smethodisbasedoncalculatingthecostsforthedifferentpartsofamanufacturingplant.

MCDisanentireconceptofhowtoidentifywasteandlosses,whichimprovementactionsshouldbetakenandhowtoimplementtheminastructuredway.Thebenefitisthatifallmatricesareused there will be a plan for how to proceed after identifying where the big losses occur.Improvementactionsarevisualizedbythematrices,aswellasbytheexpectedsavingsfromtheactions.Drawbacksare,asforVCE,thatwhenstartingwithMCDtherecouldbetoomanywastesand losses identified and themotivation for continuingwith the rest of thematrices risks todecrease,matrixbymatrix.MCDisthemethodthatencouragesthecostevaluationtoresultinactualcostsavingsbymakingremediesaclearstepinthecostreductionprocess.SPAincludestheremediesasapartofanumberofstepstofollow,wherecreatingaPPMisoneofthesteps.

Son discusses strategic decision‐making about factory automation, but as Ståhl argues, it isimportanttofirsthaveaccurateprocesseswithhighutilizationoftheresourcesathand,beforemaking further investments in resources [20]. If there is noplan for how to use the releasedcapacitytheinvestmentisawastesinceitwillnotpayoffiftheadditionalcapacityisnotused.ThisfactisnotmentionedinMCD,butthentherecanbeimprovementswithoutreducingcostsand as long as it contributes to general improvements, and not just for a specific function oroperator,itcanbegoodenoughbyitself.MCDhighlightstheinvestmentcostinrelationtothe

27

costreduction,whichshouldbeconsideredbeingaself‐evidentaspect,but isnotdiscussed ineitherSPAorSon’smodel.

Investinginnewresourcesorautomationrequireshighutilizationoftheavailableresources,asStåhlargues,andMCDdoesnotspecificallytreatthisissue.Notonlyshouldwasteandlossesbecut,butalsounnecessarycostsshouldbepreventedfromarisingbyefficientuseofequipmentandresourcesathand.Sondiscussesasimilartopic;theintegratedmanufacturingperformancemeasure (IMPM), measuring the productivity in contrast to sales, i.e. whether or not thecompany makes money from the output. If this measure is negative it does not matter howefficient manufacturing is, profitability will not increase with efficient manufacturing if salesdoesnotincrease.

ThematricesintheMCDandinSPAhelpvisualizetheresultsanditiseasytogetanoverviewoftheproblemareas.Son’smodelalsovisualizesthedifferentcostitems,butthecausesforthemare not part of the resulting cost matrix. MCD is especially systematic with its matricescomparedtobothSPAandSon’smodelandhavingmatriceswithclearpurposeshelpstheuserintherightdirectionandhelpstheuserto focusonwhat is important.AnadvantagewiththePPM is that there is a clear structure to startworkingwith from thebeginning,which canbefurtherdeveloped,whereasthecategoriesneedtobeidentifiedwithMCD.

AdrawbackwithbothMCDand themodel bySon is thatneitherof themconsiders the cycletime,somethingthat theSPAdoes.Thecycle time isaparameter that isusedeverydayand iseasy to understand.Deviations from thenominal cycle time indicate errors in the productionprocess.

SPA is the only method taking the cumulated effect of material cost into consideration. Thematerial cost for every production process step is calculated. Hence, it becomes evident thatscrappedpartsinalaterprocessstepwillresultinhighercoststhaninanearlierstep.Thisfactisanimportantaspectthatmakesiteasytoknowwheretostarteliminatingwaste.

LogisticsissuggestedtobepartofMCDandsomelogisticsisincludedinSon’smodel,butitisnotincludedinSPAsincethemainfocusisidentifyingandeliminatingdeviationsfromtheactualmanufacturingprocesses.Ofcourse,itisfullypossibletoaddlogisticsonownterms.

To summarize thedifferencesbetween the threemodelsa tablehasbeencreated tovisualizethem,seeTable4.10.Eachofthemodelshastheirbenefits.Sonpresentsamethodtocompareandevaluatecostitemsinaholisticview,buthaslittlefocusontherootcausesofthecostsandhowtoachieveimprovements.MCDisastructuredconceptofhowtoidentifywasteandlossesby considering improvement actions and implementation of them using a holistic view. Adrawback with both MCD and Son’s model is that cumulatedmaterial cost is not taken intoconsideration.Thisaspecthighlightstheimportancetodecreasescrappeditemsduetoerrorsinoperationsattheendofamanufacturingprocess.AnadvantagewithMCDistheconsiderationofcostsavingsandthereturnof investments.ThefocusofSPAlies ineliminatingdeviationsandstrivesforusingcurrentequipmentandresourcesefficiently,whichisanimportantaspectwhencapacity appears to be scarce. The importance of having a plan for released capacity ishighlighted and it becomes evident that development resulting in decreased utilization is awaste of investment if the released capacity is not used. Furthermore, it is a user‐friendlymethod that is simple to use and understand. Also, it includes the cycle time and cumulatedmaterialcost,whichneitheroftheothertwomodelsdoes.

28

Table4.10exhibitsasummaryofthecomparisonbetweenthethreemanufacturingcostmodels.

4.2.4 ChoiceofalternativemodelVCEisnotatypicalmanufacturingproductionanditisdifficulttoapplythemanufacturingcostmodels.Primarilysincethemajorityoftheactivitiesaremanualassemblyconsistingofvariableoperationsinsteadofstandardizedworkprocesses.Forthesamereasonitishardtocollectdatainput.

SPAisasuitablemanufacturingcostmodelforVCEsinceitishasastrongimprovementfocus,which is important for VCE, it has a clear course of action, it considers the cause‐resultantrelationshipbetweenlosses,itisuser‐friendlyandconsidersalsothetotalmanufacturingcost.Son’smodelisapplicableonVCEbutsincethereisnofocusontherootcausesofthecostsandwheretheyoccurtheSPAisabetteroption.

4.2.5 AnalysisofapotentialimplementationofSPAatVCEA PPM could be created for the entire shop floor, but to get closer to the problems VCE isrecommendedtomakePPMsontheindividualstations.MakingPPMsontheindividualstationsmakesiteasiertofindtherootcausesandtofindsolutionstothoserootcauses.Ifitisdesirabletogetangeneraloverviewof thewholeproductionthePPMsforeachstationcanbesummedtogetherandformaPPMfortheentireshopfloor.

SincethereareseveralstationsintheassemblytherecommendationistostartdoingPPMsforthemostproblematicstations.AproposalforhowthePPMcanbecreatedispresentedinTable4.11.Thedataunittodocumentcanforastartbetimesinceit,moreorless,correlatestocost.Lateron,whenbeing familiarwithSPA, theunit canbe translated into costs,but this ismorecomplicated and it is better to focus on learning and conducting themethod correctly beforemakingitmorecomplicated.

MCD Son SPA

FocusEliminate waste and losses in manufacturing processes

Visualizes costs items for maufacturing processes

Eliminate deviations in manufacturing processes

Cause‐resultant relationship Visualized in matrix B Not considered Visualized in the PPMWaste and losses Identified in matrix A Not identified Identified in the PPMTotal profitability Not included In the IMPM Not included

ApproachClear and easy to follow due to the matrices Includes no approach in particular Has an eight step methodology

Utilization Does not include such details Consideres the utilization of equipment

Calculates it and emphasizes the importance of using released capacity instead of just decreasing utilization

Cumulated material cost Not included Not included

Material costs cumulate as parts are processed, calculated with the cost relation

Total manufacturing cost Not included Is calculated but divided into item costs Is considered using the cost relation

Cycle time Not included Not includedIs considered in the cost relation when calculating manufacturing cost

29

Table4.11exhibitsaPPMdevelopedforVCE.

AscanbeseeninTable4.11theproductionrateisexcludedamongtheresultparameters.Thisisbecause it ishard toconsider theproductionrateatVCEsince it ismostlyassemblyand toavoiddifferent interpretations.The extra time it took toperformanoperation can aswell becountedasdowntimeaslowerproductionrate.Itisimportanttobeconsequentandthereforethe production rate is excluded to avoid some people filling in production rate and othersdowntimeforthesamefactors.

Themost optimal is if the operators by them self can collect the data for the PPM,why it isimportanttoeducatebothteamleadersandoperatorsinSPA.Everyoccurringdeviationshouldbedocumentedonatemplatetellingthetimeitoccurredandthetimeconsumptionoftheerrorand explaining what the error was. Data is then to be forwarded into the PPM for thecorrespondingstationandputunderthecorrectfactorgroupandresultantloss.Forinstanceifthereistroublewithacranesandittakes15minutesbeforetheoperatorcancontinuehiswork,15minutesshouldbeaddedtotheboxunderCranes(factorgroupG)andUnplanneddowntime.It is not always realistic to document every error that occurs since some of themmight onlyconsume little time relative to the cycle time. A lower boundary for which errors to bedocumentedcouldbebeneficial.

TheamountoftimespentonkaizenisrecommendedtobeapartoftheSPAtoeasilydocumentthetime‐consumptionforthisactivity.Thenumberofpersonnelpresentfortheactivityfromthestationshouldbemultipliedwiththetimespentonconductingakaizen.

ThereisnostandarddurationofdatacollectionforPSM,itdependsonthedowntime(DT).Generallythetimebeforeafailure(TBF)periodcanbelinkedtothesubsequentDTperiod.OneproblemthatcanarisewhenprocessingthecollecteddataishowtotreatthetimebeforethefirstDTandafterthelastDTduringseveralmeasurementperiods,e.g.beforeandafterbreaks.AgoodwaytodoitistoaddallperiodssothatthetimeafterthelastDTinthefirstperiodisaddedtothetimebeforethefirstDTinthesecondperiod.SincetheTBFperiodcanbelinkedtothesubsequentDTperioditispossibletocalculatekeyratiosforeachcycleofTBF/DT.ForinstancetheDTratio,qsi,canbedefinedas[20]:

4.18

Factorgroups Scrap Rework Planned downtime Unplanned downtime Planned maintenance Unplanned maintenance Packing Energy ∑Factors

A.Tools‐ScrewdriverB.Workmaterial ‐ Lack of material

‐Defectedmaterial‐DimensionerrorC.Process‐DocumentationD.Personnelandorganization‐Incorrectassembly‐QPRS‐Kaizen‐MeetingsE.WearandmaintenanceF.SpecialfactorsG.Pheriphericalequipment‐Conveyer‐CranesH.Unknownfactors∑Resultparameters

Result parametersQualityparameters Downtime parameters Environmentandlifecycle

30

Thetotalamountoftimeforcollectingdatacanbedeterminedthroughanumberofcycles,k=i,ofTBF/DT.Thetotalamountofqsisdefinedas:

∑

∑ ∑ 4.19

AruleofthumbisthatwhenqSmean,qs(k)/k,isstabilizeditisenoughmeasurementsmade.

ToachieveastablevalueofqSmeanandtogetrepresentativedataforanalysisoftheproduction,theshortestamountoftime,Tk,requiredtocollectdatacanbeestimatedas[20]:

30 60 4.20

wherencisthenumberofcycles,MBTFisthemeantimebetweenfailureandMDTisthemeandowntime.

MDTisdefinedas[20]:

∑

4.21

wherekisthenumberofstopsduringthemeasuredperiod.MTBFisdefinedas[20]:

4.22

wherettotisthetotaltimeofthemeasuredperiod.

ItisimportanttodothecalculationsfortheamountoftimerequiredtocollectdataotherwisethereisariskofPPMbecominginaccurateandapoorbaseforfurtherinvestigations.IfthedataisproperlycollectedduringanenoughamountoftimeanditisfilledinthePPMtherewillbeagoodbaseforwithinwhichareasimprovementworkneedstobedone.Thereshouldbecontinuouslyfollow‐upsofthePPMstoseeiftheimprovementworkhasgivenintendedeffectandtoalwayskeeptrackoftheareas,whichneedtobeimproved.

WhenconfidentwiththePPM,theoptimalscenarioistohaveaPPMforeverystationwheretheteamleadersareresponsibleforfillinginthematriceswithintheirareastobeabletooptimizingeverystationintheassembly.ItisimportanttokeepinmindthatthefocusofSPAiseliminationofdeviationsanderrorsandnotprocessdevelopment.Hence,usingSPAwillnotreducenominalcycletimesbutisatooltodecreasetheriskofexceedingthenominalcycletime.

31

5. WorksamplingThischapteraimstoexplainthemethodofworksampling,amethodusedatVCEtocollectdatainputforMCD.Worksamplingwillbeanalyzedtoevaluateitsreliability.Theauthorsinthisthesisparticipatedinthelatestroundofworksamplingandtheresultswillbepresentedtogetherwithananalysisofthemethodingeneral,itsbenefitsanddisadvantages.

5.1WorksamplingmethodologyWork sampling ismethodused formeasuring timeutilization and sometimes alsousedas anindirect measurement of productivity [26]. The method does not only provide informationabout the amount of time the operators spend on value adding respective non‐value addingactivities, it can also identify factors affecting the productivity, either positively or negatively[27].

Work samplings are series of instantaneous observations of the operators on the shop floorduring theirwork. At the endof the study the individual observations, assessed either to bevalueaddingornon‐valueadding,arecompiledtogethertoshowthedistributionbetweentheboth. An advantage with work sampling is that it is an inexpensive, easy and quick way toanalyzeseveraloperatorscontinuouslycomparedtoothermethodssuchastimestudies,wherethefocusonlyisonafewoperators.Inordertoextractasmuchaspossiblefromworksamplingitisessentialtoconductitcontinuously[27].

Since it is desirable to allocate the different non‐value adding activities, e.g. transportation,waitingandrework,itisimportanttodefinewhatcategoriesinwhichtodividework.Oneofthechallenges with work sampling is to attain well‐defined and significant categories in areasonable amount to keepdocumentation of the observations simple and accurate. Differenttools such as papers and handheld computers can be used during sampling, but it isrecommendedtousecomputerswhenanalyzingthelargeamountofdata[27].

Beforesamplingit isessentialtodecidesamplesizeandaccuracy,frequencyandlengthofthedata collection aswell as the route of how to sample andwhich tools to use [28]. To avoidanxietyoftheobservedoperatorsanddeviancefromnormalbehaviorduringtheobservations,allpartiesinvolvedmustbeinformedaboutthemethodinadvance.Itshouldbepointedoutthatthemainpurposeistoevaluatetheconditionsforproductivityinordertomakeimprovements,nottoevaluatetheoperators[27].

The choice of assessors is crucialwhen sampling. Tobe able todocument thework activitiescorrectly, theassessors’knowledgeabout theworkprocess isof great importance.Withgoodknowledge of thework process itwill also be easier for the assessor to analyze productivityproblemsandtoseeimprovementopportunities[27].

Thesamplingprocedureitselfissimple.Theassessorhasaroutehewalksandeachoperatoronthe route can be seen as a sample. An observation is like a snapshot. The assessor shoulddocument which activity the operator is performing at the exact instant moment that theobservationtakesplace[27].Toensurethatallactivitieshaveanequalchanceofbeingobservedthe observation times should be chosen randomly. It is important not to guess the activityperformed. If it is unclear which category it belongs to it is better to leave a comment and

32

discuss itafterwards instead.Tomakesure togetrepresentativedata thesamplingshouldbeconductedduringnormalworkingconditions,i.e.notduringpeakoroff‐season[28].

5.2ConfidenceintervalWhen determining the accuracy of the results fromwork sampling a confidence interval canidentifythenumberofobservationsrequired.Ahighconfidencelevelleadstoahigherdegreeofsafety, but also to awider interval.Thehigher thenumberof observations is, the smaller theintervalis[29].Acommonconfidencelevel,whichisoftenusedasastandard,is95%.Usinga95% confidence levelmeans that the real value is covered by the calculated intervalwith acertaintyof95%.Thecalculationsfortheconfidenceintervalarepresentedbelow.

n=numberofobservations

P(valueaddingactivities)=p

X=numberofvalueaddingactivitiesofthenobservations

, , ∗

∗ 1 ∗ ∗ 10 aconditionforthebinomialdistributiontoapply 5.1

: ∗∗ ∗

, ∗∗ ∗

5.2

95% → , 1,96

Throughderivationitisclearthatp*=½willleadtothebroadestinterval.Iftheprobabilityofthevalueaddingactivitiesisunknownandtheconfidenceintervalhastobenobroaderthanapredeterminednumber,usingp*=½willguaranteethewidthoftheinterval.Ifthewidthoftheinterval for example needs to be decreased to half, the number of observations needs to beincreasedfourtimes[30].

5.3CurrentsituationVCEusesworksamplingasanindicatorofhowefficienttheassemblyis.Theobjectiveisalsotosee if thereareany improvementopportunities,especiallywithin linebalancingor ingeneral.Furthermore,work samplinghasalsobeenusedasdata input forMCD.VCEhas someearlierexperiencewithinthemethod,butitwasonlyconductedonceonacoupleofstations,duetolackof time. Except forbeing abasis for theMCD, there is aplan forusingwork sampling in thebeginningofeveryperiodwithanewtakttimeandintheendoftheperiodstoevaluatehowtheassemblyprocesshasdevelopedineachworkarea.Aworkareacontainsacoupleofstations;often thereare three stations inoneworkarea.Foreachworkarea there isone team leader.Work sampling will also serve as a support for the team leaders to identify areas ofimprovementpossibilities.

Themachinemodelsvarywidelydue to if theyare lightlyorheavilyequipped.Thevarietyoftotalassemblytimerangesbetween41and66hours.ThemethodtobalancethestationsatVCEhas been done by leveling, a method to control variability by sequencing jobs to increasecapacity utilization. Laborwill bemore evenly distributed between high and low demanding

33

productsanditwillprotectthelaborfromvolatilitywhenusingleveling[31].Stationsandtypeofmachines assembled for sampling has been chosen randomly even though theworkloadofthem varieswidely. To reflect reality, the operators chosen for sampling have been ordinaryworkerstogetrepresentativedata.Thus,operatorsnewtothestationshavebeenexcludedfromworksampling.

When sampling, a template is filled in with the names of the observed operators and theirrespectivestation.Thetemplatesalsocontaincategories;onecolumnforvalueadding(VA),oneforsemi‐valueadding(S‐VA)andseveralcolumnsfornon‐valueaddingactivities.Thenon‐valueadding categories on the first samplings at VCEwerewaiting, transportation, and other. Theobservationsareconductedduringafullcycletimesinceworksamplingissupposedtoreflectnormalcircumstancesandhavetostopifsomethingunpredictablehappens,forexamplewhentherearelinestops.

When VCE first implemented work sampling, groups of two or three persons discussed thedifferent categories and how work sampling should be implemented to avoid differentinterpretations.

VCEhashadnoconcernsregardingtheconfidenceintervalsoftheirworksamplingtoknowthestatisticalsignificanceoftheirresults[32].

5.4ImprovementsofworksamplingatVCEThetemplateforworksamplingsdevelopedforthisthesiswasbasedonthepriortemplateandcanbeseeninFigure5.1.Whenanewcycletimebeginsobservationsweremadeeveryminuteand filled in by the corresponding number, i.e. the first observation is documented as a dashunder thecorrectcategory.Changes forhowVCEperformsworksamplingwereprimarily theaddingofcategoriesandclearlydefiningthem,whichhadnotbeendonebefore.Also,thetimeperspectivewasadded toseewhencertainactivitieswereobservedduring the takt time.Theresultofthelatteraspectshowedthatmostwaitingoccursduringthelastminutesofthecycletime,indicatingthatthestationsarenotwellbalancedandsincewaitingnormallyoccursintheendof thecycle time, the timeaspectcouldbeconsideredtobeunnecessary.Thetimeaspectshows atwhich time the operator finished hiswork by using the time line, but this could bedocumented by noting the time when the operator was finished. The results can then becomparedtotheexpectedtimeofwork,showinghowwellintendedandactualworkloadmatch.A mismatch indicates that an update of the distribution of the workload needs to be done.CategoriesapartfromWaitinghavenosignificanceofhowtheyaredistributedofoverthecycletime.

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Figure5.1exhibitsthetemplateusedforworksampling.Tisthetakttime.

Thepurposeofworksampling is tovisualizethetimedistributionofvalueadding(VA),semi‐valueadding(S‐VA)andnon‐valueaddingactivitiesandtoevaluatetheworkandthenmakeitmore efficient by reducing waste and losses. Another achievement with work sampling is tocompareresultswithtimetofollowtheimprovements.Thenon‐valueaddingactivitiesneedtobeclearlydefinedtobeabletoidentifythemforimprovements.Thecategoriesusedforthefirstsamplings at VCEwere not clearly defined and this they have been expanded and defined asfollows:

VA;connectingelectronics,hydraulicsandmechanicsandgluing. S‐VA;unpacking,applyingprimer,documenting,andbleeding. Walking;walkingemptyhanded. Picking;collectingparts/toolswithinthreestepsfromtheworkingspot. Transporting;walkingmorethanthreestepswithparts/tools. Waiting;talkingordoingnothing. Other;testing,cutting,checking,adjusting,regulating.

ThecategoriesS‐VAandOther containsmanysub‐categories,not tohave toomanycategorieswhilesampling,butthereisroomforcommentsonthetemplatetofurtherdescribetheactivity.The drawback of having too few categories is to find solutions for widely defined activities.Havingmanycategoriesmakesiteasiertoseetherealsituationandtofindarootcauseofittoresolvetheproblem.However,havingtoomanycategoriesmakesitdifficulttochoosetherightone due to time pressure and increases the risk for the assessors of having diverseinterpretationsoftheactivities.

Thenon‐valueaddingactivitiesneedremediesandbelowfollowssuggestionsofsuch:

S‐VA;dependsonactivity,forinstanceunpacking,ifnotnecessary,couldbereducedbynegotiationwithsuppliertoreceivegoodswithoutplasticscoversifnotnecessary.

Walking;makespaghetti‐mapstovisualizethemovementofoperators. Picking;investigatethepossibilityofusingkitting. Transporting;havinggearclosertoworkareaandkitting. Waiting;balanceoperatorsworkload. Other;remedydependsonthetypeofloss.

Categorieswere chosen to represent themost common activitieswithouthaving toomanyofthem.Whatisimportantaboutthemistohavearemedyforeachcategory,whichisnotthecase

ResultsModel: BL Value addingIntended workload: (min) Semi‐value adding

Walking

Picking

Transport

Waiting

Other

1 4 VA/All (%)

2 …

3 T Comments

Takt time:Date:

Station:

Operator:

Assessor:

Walking

Picking

No.

No.

Other

VA

S‐VA

Walking

Picking

Transport

VA

S‐VA

Transport

Waiting

Waiting

Other

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with S‐VA and Others, which can only be further treated through comments made duringsampling. Ideas such as having more categories or writing the categories instead of dashingthem were rejected to keep documentation simple. Overdesigned methods tend to get lessdevotionandarehenceerror‐proneandsubsequentlyresultswillbeofpoorerquality.

Another change made was that a trial round were implemented. The categories were firstdiscussedmutuallyamongtheassessors,asinthefirstroundsofworksampling,butthistimeatrialroundwereconductedfollowedbyanotherroundofdiscussionstoaligntheinterpretationoftheassessors.

5.5ResultsofworksamplingatVCEThe results from the work samplingmade after the improvements are presented in the twosections below. In section 5.5.1 the result for the entire assembly line are presented and insection5.5.2theresultsforeachworkareaontheassemblylinearepresented.

5.5.1WorksamplingonallworkareastogetherThedistributionoftheactivitiesonthesampledworkareasispresentedinTable5.1.Thetablealsoshowsthewidthoftheinterval,forwhichitis95%confidentthattheintervalcoverstherealvalue, foreachactivity.Forexample,therealvaluefortheshareofvalueaddingactivitiescanbefoundintherangeof38.9±3.2%with95%confidence.

Sinceanintervalofanumberofpercentagesdoesnotsaymuchabouthowthevariancesaffecttheresults,calculationsofdata fromworksamplinghavebeenmadetoshowthevariances incostandhoursperyearforalloperatorsintheobservedareas,seeTable5.1Itisassumedthatoneworkday,withoutbreaksandmeetings,correspondsto433minutes(accordingtoVCE)ofdirectworkontheassemblyline,thenumberofworkingdayspermonthare20,thenumberofoperatorsare54andthewageis60zl/hperoperator.

Table5.1exhibitstheresultfromworksamplingatVCE.Zlisshortforzloty,thePolishcurrency.

Seeinghowthevariancesimpactcostandtimemakesiteasiertodeterminehowmucherrortoallow,sinceitiseasierforamanagertosaythat±5000hoursisanacceptableerrorinsteadof±5%,notknowingthenumbertranslatedintotimeandcost.

5.5.2WorksamplingonindividualworkareasInTable5.2thedistributionoftheactivitiesineachworkareaisshownanditscorrespondingconfidence interval. As it shows, the intervals are broader than in the result for the entireassemblyline.Hence,theaccuracyispoorerforonlyoneworkareaatatimethanforallofthemtogether.Theworkareawiththepoorestaccuracyhasanintervalof±9.9%,whileforallworkareas together it is ±3.2%, amuch lower number. The reason for this is a higher number of

Activities No.obsv. Share[%] Interval[+/‐%] Time[h/yr] Cost[zl/yr] Time[h/yr] Cost[zl/yr]Valueadding 345 38.9 3.2 2998 179903 36337 2180214S‐VA 170 19.1 2.6 2420 145217 17905 1074308Walking 52 5.9 1.5 1444 86663 5477 328612Picking 105 11.8 2.1 1986 119180 11059 663543Transport 83 9.3 1.9 1791 107440 8742 524515Waiting 70 7.9 1.8 1658 99462 7373 442362Other 63 7.1 1.7 1579 94760 6635 398126Total 888 100 93528 5611680

Error margin Expected values

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observationsontheassemblylinethanoneachworkarea.Thisimpliesthattheresultsforeachworkareaarelessreliablethantheresultforallworkareastogether.

Table5.2exhibitstheresultsoftheshareofeachactivityforeachworkareaandtheirconfidenceintervals.Allvaluesareexpressedin%andtheintervalsare±%.

5.5AnalysisofworksamplingWhenwork samplingwas first conducted at VCE the groups did not get to discussmutuallyafterwardswhatwasconcludedintheindividualgroupmeetingsandtherewasnotrialroundbefore the realwork samplingwas conducted. After the first round it became obvious to theobservers that theystillhaddifferent interpretationsof thecategories,even though therehadbeendiscussionsbeforetheroundtheinterpretationsoftheactivitiesweredifferentandsomemorediscussionswereneededtoaligntheobservers.

Duringworksamplings itwasevident that the interpretationsweremademore similarlyandtheobservationsreflectedrealitybetterwhenhavingsomeone,inthiscaseateamleader,inthegroup who knew the processes. This person should be able to tell for instance when extraordinarytasksoroverproductionisperformed.Itisalsoimportantnottointerrupttheobservedperson by asking the operator about the operation performed at themoment, if it is hard todeterminewhat kindof activity that is performed.This is because theoperatorwill probablyanswerwhat the purpose of the operation andnotwhatwas actually performed in the exactmomentoftheobservation.Interruptionscanalsomaketheobservedpersondeviatefromtheirnormalpattern.Hence,theobservationswillnotreflectreality.

Togainobjectiveresultsof theobservations theassessorshouldbe inmovement, i.e.walkingbetween the observed people. If the observer is idle there is a risk of getting affected by theobservedoperatorsearlieractivitiesand log those insteadof theobservedactat the intendedmoment.Forexample,iftheobserverstaysonthesamespotduringtheobservationsandseesthattheoperatorperformsvariousvalueaddingactivitiestheremightbeariskthattheassessorchooses the observation moment when the operator is performing a value adding activity,instead of taking a random observationmoment. Using a time advice that generates randomtimesisasolutiontokeeptrackofwhentologobservationsandgainasmuchrandomnessaspossible.

Other parameters affecting accuracy and reliability of work sampling are the variety ofmachines,thenumberofstationsobservedandtheskillsoftheoperators.Itisessentialfortheresult to make a selection of operators, stations and machines for work sampling that isrepresentative for the assembly line. Only observing operators with low skills operating onheavymachines at only a few of the existing stationswill give amisleading result. Operators

Activities Share Interval Share Interval Share Interval Share Interval Share Interval Share IntervalValueadding 27,5 6,1 48,0 9,9 44,4 7,9 40,3 7,7 40,1 7,9 40,2 8,4S‐VA 21,6 5,6 21,4 8,1 11,8 5,1 24,0 6,7 11,6 5,2 25,0 7,4Walking 8,3 3,8 1,0 2,0 4,6 3,3 3,2 2,8 9,5 4,7 6,1 4,1Picking 11,3 4,3 12,2 6,5 15,0 5,7 11,0 4,9 10,2 4,9 11,4 5,4Transport 14,7 4,9 8,2 5,4 7,8 4,3 9,7 4,7 3,4 2,9 9,8 5,1Waiting 9,8 4,1 0,0 0,0 15,7 5,8 3,9 3,1 9,5 4,7 4,5 3,6Other 6,9 3,5 9,2 5,7 0,7 1,3 7,8 4,2 15,6 5,9 3,0 2,9No.obsv

WA6

204 98 153 154 147 132

WA1 WA2 WA3 WA4 WA5

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withlowskillswillmostlikelynothavethesamedistributionbetweenactivitiesasanormallyskilledoperator.Forexample,alowskilledoperatorwouldneedmoretimetoassemblethananormallyskilledoperator,whichwouldleadtoahigherfrequencyofvalueaddingactivitiesandlower frequency ofwaiting. The same reason accounts for only observing highly experiencedpeople; there is a chance that they finish their operations earlier than expected, implying ahigher frequencyofwaitingandmaybealsoofoverproduction.Togainmoreaccurate resultsthegroupofobservedpeopleshouldbenormallyskilledorconsistofamixtureofskills.Also,beingobservedmightmakethemfeelstressedandduetothattheymightworkfasterordeviatefromthenormalinotherways.Tominimizethisriskitisimportanttoinformtheoperatorsofthepurposeoftheworksampling.

Asforthemachines,theoperationsforassemblingamachinevariesbetween41and66hours,duetothe150optionsavailableforasinglemachine.Dependingonwhattypeofmachinethatisbeingassembledduringobservations theresultswillbedifferent.Aheavymachinewill implyhigher frequency of value adding activities and a lower frequency of waiting and overproductionthanforalightmachine.Asolutiontomakebetterestimationsofwhichmachinestoobservecouldbe to identify thedistributionof themachines assembled to seewhich type,orwhatassemblytime,ismorecommonortochooseaselectionofmachinesrepresentativefortheaverageproduction.

Since all stations are different the allocation of activities will also differ. Therefore, it is notsuitabletoonlydoobservationsonafewstationswhenevaluatingtheentireassembly.Asmanystationsaspossible,ideallyallofthem,shouldbeincludedintheobservations.

Using confidence intervals is a good way to ensure the accuracy of the sample but thecalculationsdoesnottakethehumanfactorintoconsideration.Evenifanerrormarginof±3,2%(theresultofvalueaddingactivitiesinthiscase)isagoodaccuracyforthispurpose,itonlyappliesiftheobservershaveinterpretedthedifferentactivitiessimilarlyandhavenotmadeanyguesseswhenbeinguncertain.Alignedassessorsareessentialforgoodsamplingresultsandthatiswhyitisimportanttoputeffortinensuringthatinterpretationsamongtheassessorsarethesame.Without alignment, the accuracyof the results cannot be ensured and the observationscanonlybeusedaspureobservationswithoutanystatisticsforthedifferentactivities.

Since the same assessors did themajority of the observations andmost of the stationswereincludedduringthisround,thesesourcesoferrorscouldbeseenasrelativelysmall.However,thetypesofmachineswerenottakenintoconsideration,whichimpliesthattheaccuracyoftheresultcannotbedetermined.

5.6DiscussionIf thealignmentof theassessors is ensured, the selectionofoperators,machinesandstationschosenisrepresentativefortheassemblyline,theaccuracyisensuredbyaconfidenceintervalandtheobservationsaremaderandomly,worksamplingcanbeseenasagooddatagatheringmethodandareliablesource for theMCD. Ifoneormoreof these factorsarenot fulfilled thesourcesoferrorswillincreaseanditbecomeshardtodeterminetheactualaccuracyofsamplingandtheresultsoftheMCDwillbeofpoorerquality.

Anothermethod,acceptforworksampling,thatcanbeusedinthesamepurposeforMCDisatimestudywherethetimeforeachactivityisdocumentedinsteadof lookingatthefrequency.

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Sincea timestudy ismore time‐consuming (onlyonepersonat a timecanbeobserved) thanworksamplingandhasthesamesourcesoferrors,theriskofdifferentinterpretationsandtheselectionofoperators,machinesandstationstoobserve,theworksamplingisamoresuitableandeffectivemethodforVCE.

Usingwork sampling as an evaluationmethod to see the development of assemblyprocessesfromthebeginningofaperiodwithanewtakttimetotheendoftheperiodforeveryworkarearequiresahighernumberofobservationsthanduringtherecentimplementation,seeTable5.2.The same principles forwork sampling apply for each individualwork area as for the entireassembly line.Withtoofewobservationsor for instanceonlyheavymachinesobservedat thebeginningof theperiodandonly lightmachinesat theendof theperiod, theresults fromthebeginningandtheendoftheperiodwillnotbecomparableandnoreliableconclusionscanbedrawnabouttheprocessdevelopment.

Therealbenefitofworksamplingcanbegainedifgettingstatisticsandbeingabletoreflectonthe overall evaluation of operators work. Noticing improvement opportunities without goingthrough statistics implies, if possible, that a solution can be implemented faster. However,statistics is important to show where the biggest losses are and some losses might not beidentified by pure observations. Nevertheless,making pure observationswould be tiring andthereisariskofloosingfocusduetomonotonouswork.

As mentioned above work sampling can function as more than just a tool to measures timeutilization;itcanalsohelpthepersonsconductingworksampling,e.g.engineers,teamleadersandoperators, toopenup their eyes to really seewhat is goingonat the shop floor.Besides,implementingthephilosophyofvalueaddingandnon‐valueaddingamongpersonnelgivesnewperspectives when analyzing the assembly and can open up for further improvements.Reflecting on the various activities conducted by the operators andwriting comments in thecommentfieldcanbeaseffective,ifnotmore,todiscoverareasofproblemandofimprovementpossibilitiesasgettingtheresultofthedistributionofactivities.

Worksamplingisamethodthatcanbeusedinvariousindustriesanditisanefficientmethodformeasuringthetimeutilizationas longasthesourcesoferroraretakenintoconsideration.Thehigherdegreeofstandardization,theeasieritistogetaccurateresults.

5.7RecommendationsTherecommendationforVCEistocontinuewithworksamplingasasourceofdataforMCD,buttheselectionof stations,machinesandoperatorsmustbedonemorecarefully than it isdonetodaytoachieveareliableresult.Beforetheystarttosampleit isessentialthattheydoatrialrun to ensure the alignment of the assessors’ interpretations. The minimum number ofobservationsneededshouldbecalculatedbyusingconfidenceinterval.

Using work sampling to see how the development of a work area has proceeded from thebeginningofaperiodwithnewcycletimetotheendofthatperiodwillrequireahighernumberofobservationsthanduringthepreviousloopsofworksampling.Alsohereshouldaconfidenceintervalbeusedforthecalculationsofthenumberofobservationsneededforeachworkareaifthestatisticsshouldbecompared.

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6 ConclusionsInhischaptertheanswerstotheresearchquestionsarepresentedbasedontheliteraturereview,observationsandanalysisatVolvoConstructionEquipment. In theendof thischapter there isasectionwherefutureresearchareasarepresented.

ThisthesishasbeenaccomplishedinordertoevaluatehowVCEcan improvetheirworkwithMCD.Furthermore,thecostmodelSPAandamethodbySonhavebeenevaluatedinsearchforanalternativecostmodel forVCE.Thedatacollectiontool,worksampling, isusedasan inputcollectorforMCD.WhathasnotbeenevaluatedatVCEisthereliabilityofworksamplingandthesecondpurposeofthisthesishasbeentoinvestigatethereliabilityofthismethod.

6.1AnswerstotheresearchquestionsHowcanVolvoConstructionEquipmentimprovetheirworkwithmanufacturingcostdeployment?Dedicationisessentialforhowsuccessfulanimplementationofamethodonacompanywillbe.The lackof it atVCEhas led to absenceof significant results.Unless this changes, the resultsfromMCDwillnobeimproved.Therefore,itisofgreatimportanceanditshouldbeafirststepofimprovement to convince themanagement team about the benefits of themethod andmakesure that they are completely committed to it. To set goals and to clarifywhich expectationsthereareofachievingresultsfromMCDisawayofgettingtheinvolvedpersonnelmotivatedtocompletetheD,EandFmatricesandachievedesirableresults.

TheBandCmatricesaredeficientcomparedtotheoriginalmatricesandneedtoberedesignedinordertobettermatchtheoriginalmatrices.Thebroadprocessstepsinthematricesmakeithardtolocatethecausesofthelossesandshouldinsteadbedividedintoworkareastoeasierlocate the losses. The reason for choosingwork areas instead of stations is that a substantialamountofworkisrequiredtocollectreliabledataforthestations.

Whenimplementinganewmethoditrequiresthattheinvolvedpersonnelputenougheffortintoitbeforeitcanrunsmoothly.ThisissomethingVCEstillhastodoeventhoughtwoloopsofMCDhave already been finished. MCD should be a regular part of VCEs improvement work withcontinuous follow‐ups. To achieve continuity there are some prerequisites: there need to besystematic ways of how to collect the data needed. The team leaders are preferably to beresponsible for most parts of the data collection since they are highly knowledgeable in theoperationsandcanbeagreatresourceintheimprovementwork.

TodayVCEhasdividedtheresultantlossesinthematricesintolargeprocesssteps.Tomakeiteasiertolocatewherethelossesoccurtheprocessstepsshouldbesmaller,forinstancetheycanbedividedintoworkareastobeabletolocatewherewasteandlossesoccurmoreprecisely.

Standardsofhowtocalculatecostsshouldbesettoensurethevalidityoftheresults.ThiswillenablethecomparisonandevaluationofresultsfromearlierloopsofMCD.

TheAmatrixiscurrently,andoriginally,usingascaleof1,2and3toprioritizelosses.Togetabetter view of which areas to prioritize and to easier distinguish the losses of high and lowprioritization, a recommendation is to rate losseswith 1, 3 and 5 instead, whichmeans thatlossesofthehighestprioritywillalsobehighlightedthemost.Havingtoomanylosseswiththe

40

highest priority makes it harder to focus and there will have to be a priority within theprioritizedlosses.Agoalistohavemaximumtenpercentlosseswiththehighestpriority.

Isthereanothermanufacturingcostmodel,apartfromMCD,thatwouldbesuitableforVCEandifthereis,howdoesitdifferfromMCD?MCDisacostmodelwiththepurposetoeliminatewasteandlosses.Anotherimportantfactorwhenlookingatcostistoeliminatedeviationsandmakeexistingprocessesflowwithouterrors.Therefore, SPA would be an alternative cost model since it reduces cost by focusing oneliminatingdeviations.

ThemajordifferencesbetweenMCDandSPAaresummarizedinTable6.1.TheydifferprimarilyintheirfocuseswhereMCDhasastrongfocusonreducingwasteandlossesusingaholisticviewwhileSPAfocusoneliminatingdeviationsmainlyintheoperations.ThedifferencebetweentheMCDswasteandlossesandSPAsdeviationsismerelythenames.Whattheybothstriveforandhaveincommonistheidentifyingofcause‐resultantrelationships.Theapproachesareeasytofollow,thoughMCDhasamorestructuredapproachsinceallstepsareincludedinthematrices.SPA considers the utilization, whichMCD does not, and emphasizes the importance of usingreleased capacity. Finally, cumulatedmaterial cost, totalmanufacturing cost and cycle time isconsideredonlyinSPA.

Table6.1exhibitsthemajordifferencesbetweenMCDandSPA.

SPAisastructured,well‐definedanduser‐friendlymethodtosetthefoundationforchangesanddevelopment foraproductionsystemby identifying loss termssuchasqualityanddowntime.Additionally, the factors are well defined and include cycle time, cumulated material andespeciallyutilization,whichareimportantfactorsthatarenotconsideredinMCD.

Canworksamplingbeseenasareliablesourceofdatawhenevaluatingproductionprocesses?Worksamplinginitselfisasimpleconcepttoattainthedistributionbetweenvalueaddingandvariousnon‐valueaddingactivities,butthepotentialerrorsourcesmakethemethodcomplex.Asdiscussedinchapter5,theerrorsourcesmentionedneedtobetakenintoconsiderationandeliminatedforworksamplingtobeconsideredasreliable.ForcompaniessimilartoVCE,wherethemajorityof theworkload isperformedmanuallyandwhere there is a significantvarianceamongproducts,thesourcesoferrorsinworksamplingaremoreandlargerthanforcompanieswithstandardizedproducts,processesandlessmanualwork.Ontheotherhand,thiscountsformostdatacollectingmethods:themoremanualworkandthemorecomplexthemanufacturingsystemis,theharderitisandthemoreeffortisrequiredtoachieveaccuratedata.

MCD SPA

FocusEliminatewaste and lossesinmanufacturingprocesses

Eliminate deviations inmanufacturingprocesses

Cause‐resultantrelationship Visualizedin matrix B Visualized in the PPMWasteandlosses Identifiedin matrix A Identified in the PPMTotalprofitability Notincluded Not included

ApproachClearandeasy to follow duetothematrices Hasaneightstepmethodology

Utilization Notincluded

Calculates it and emphasizestheimportanceofusingreleasedcapacityinsteadofjustdecreasingutilization

Cumulatedmaterialcost NotincludedMaterial costs cumulateaspartsareprocessed,calculatedwiththecostrelation

Totalmanufacturingcost Notincluded Is considered using thecostrelation

Cycletime NotincludedIs considered in the costrelationwhencalculatingmanufacturingcost

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Conductingworksamplingwithgreatcare,awarenessandwell‐madepreparationswillleadtoreliableresults, thoughtheywillneverbecompletelyaccurate.Usingconfidence intervalswillindicateonwhatleveltheaccuracyis,providedthattheerrorsourcesareeliminatedorreducedtothemaximum.

If thecompany ispreparedtoput theeffortsneededtoeliminate thesourcesoferrorsandtoconductthenumberofobservationsneededtoacquirethedesirableaccuracyofworksamplingitcanbeseenasareliablesourceofdatawhenevaluatingproductionprocesses.Eventhoughthe result will not reflect reality to one hundred percent, it will be accurate enough to, forinstance,evaluatetheefficiencyofaproductionlineorbeusedasinputforamanufacturingcostmodel.

6.2FutureresearchThefocusofthisthesishasbeentheassemblyatVCEandthereforethePPMwasdevelopedforthestationsintheassembly.EventhoughtheSPA,whichincludesthePPM,wasdevelopedformanufacturingproductionsystemsitwouldbeinterestingtoinvestigateifandhowitcouldbeappliedonlogisticsatVCE.Windmark&Anderssonhasbeeninvestigatingtheissueofapplyinglogistics on SPA and has developed a costmodel to determine the cost for inbound logistics,which they connect to Ståhl’s manufacturing cost model [33]. This approach would beinterestingtoinvestigatesinceaddinglogisticswouldgiveamoreholisticviewoftheproductioncost.

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8 DiscussionThis chapter aims to evaluate thework of this thesis, presenting reasons forwhy and how theresearchquestionswerechosenaswellasgeneralthoughtsanddifficultiesthroughoutthework.WhensearchingforathesisprojectintheareaofindustrialproductiontheauthorsgotintouchwithVCE.Themainaimwastofindaprojectinwhichthecompanyhadgreatinteresttogetthefulldevotionfromthem,sinceitwouldbegreatmotivationfortheauthorstosupporttheminthe issue.Theprojectareaof this thesiswas initially tobeMCD.Whenarriving toVCE itwasclear that they had little experience within the method and had made little success withimplementing it.Thepurposeof thethesiswas initiallyto improvetheexecutionandthustheresultsofMCD,butastheproblemwithMCDappearedtobethedatacollectionitcametobeanadditional focusof the thesis.Amajor aspect,which theauthorswere fully awareof,was therelationbetweenMCDanditsdataandonlyfocusingondatacollectionwithoutconsideringtheintegration with MCD changed the direction of the thesis. The result became a deeperinvestigationinthemeasurementtoolworksampling,partlyasatoolbyitselfbutalsoasapartofMCD.

Considering the focus of data collection the thesis did not get the depth within MCD as theauthorshadwishedfrominitially,althoughthemaintopics,MCDandworksampling,areclearlyconnected sincework sampling is a tool usedwithinMCD. Another tool forMCD called timestudywasconsideredbutwasrejectedduetotheheavyworkloadandtimeconsumption.

ThecurrentsituationandconclusionsregardingrecommendationsofhowtoimprovetheirMCDissometimesnotprofoundlymotivatedandunderpinnedwithreferencestotheliterature.Theresults from the cost model area derives from interviews and observations and has had noplatform for how to interpret the information in addition to the theory of the costmethods.Hence, therehasbeen little results tobaseanalysis and resultson.Also, this facthasmade ithard to separate results, interpretations, analysis andconclusions, since conclusionsare tobedrawnfromresultsandanalysis.

When evaluating the part about work sampling, the authors conclude that the results andconclusions for the third research question concerning work sampling are mainly based onliterature reviews and the execution of work sampling at VCE. The samplings were maderandomlyandthegoalwastosampleonallworkareas.Thetypeofmachinewasnottakenintoconsideration for the samplings. If the study was to be made again it would have beeninteresting to investigate how much the results would differ by comparing observationsincluding only heavily equipped machines with observations including only lightly equippedmachines,oronlyobservingexperiencedoperatorscomparedtobeginners.

Thetheoryusedtocomparethemanufacturingcostmodelswasthetheorypresentedaboutthecostmodels.Anideacouldhavebeentouseaplatformforevaluatingthemodels.

The language barrier at VCE has not had great impact on the work but has indeed been aninfluencingfactor.Thegreatestadvantagewouldhavebeentobeattendingmeetingsatplanninglevel; instead theauthorswere informedof theoutcomesof themeetings.Also, itwouldhavebeenpossibletodiscussworksamplingandgeneralproblemsforapplicationofPPMwiththeteamleaders.

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9 References[1] DoganaksoyN.&HahnG.J.,“GettingtheRightDataUpFront:AkeyChallenge’’,Quality

Engineering,no.24,pp.446‐459,2012.

[2] Jönsson,M.,“Cost‐consciousmanufacturing–Modelsandmethodsforanalyzingpresentand future performance from a cost perspective”,DivisionofProductionandMaterialsEngineering,LundUniversity,2012.

[3] “Looking back ‐moving forward, VolvoConstructionEquipments fӧrsta 175 år”,VolvoCE, 2007. [Online]. Available:http://www.volvoce.com/SiteCollectionDocuments/VCE/History/Company/175%20years%2012A1003961‐0711.pdf.[Accessed2014‐04‐29].

[4] “A group of high‐power companies”, Volvo CE, 2014. [Online]. Available:http://www.volvoce.com/constructionequipment/corporate/en‐gb/AboutUs/about_volvo_group/Pages/about_volvo_group.aspx.[Accessed2014‐04‐29].

[5] “A Global Group”, Volvo Group, 2014. [Online]. Available:http://www.volvogroup.com/group/global/en‐gb/volvo%20group/Pages/aboutus.aspx.[Accessed2014‐04‐29].

[6] “Company culture – The Volvo Way”, Volvo Group. [Online]. Available:http://www.volvogroup.com/group/global/en‐gb/career/life%20at%20volvo%20group/ourculture/Pages/ourculture.aspx. [Accessed29‐April‐2014].

[7] “Volvo Group core values”, Volvo Group, 2014. [Online]. Available:http://www.volvogroup.com/group/global/en‐gb/volvo%20group/ourvalues/Pages/volvovalues.aspx.[Accessed2014‐04‐29].

[8] Pawlik, G. Production Engineer. Volvo Construction Equipment, Wroclaw. [Interview]2014‐04‐07.

[9] “Volvo Construction Equipment”,Al‐FuttaimAuto&Machinery Company LLC. [Online].Available: http://www.famcouae.com/13/Volvo‐Construction‐Equipment.aspx.[Accessed2014‐07‐22].

[10] “Manufacturing quality the Volvo Way”, Volvo CE. [Online]. Available:http://www.volvoce.com/constructionequipment/corporate/en‐gb/quality/product_life_cycle_quality/manufacturing_quality/pages/manufacturing_quality.aspx.[Accessed2014‐04‐29].

[11] “The Volvo Way mot ӧkad konkurrenskraft”, Lindstrӧm, K. [Online]. Available:http://www.leanforum.se/dokumentation/Lindstrom.pdf.[Accessed2014‐04‐29].

[12] “The Volvo Production Systems’s Model”. [Online]. Available:http://media.aerotrainees.se/2011/12/ma0901C.gif.[Accessed2014‐05‐06].

44

[13] Arbnor, I. & Bjerke, B., “Methodology for Creating Business Knowledge”, 3rd edition,SAGEPublications,California,2009.

[14] Hӧst, M., Regnell, B & Runesson, P., “Att genomfӧra ett examensarbete”, 1:6 edition,StudentlitteraturAB,Lund,2006.

[15] Yin, R. K., “Case study research, Design and methods”, 4th edition, SAGE Publications,California,1994.

[16] Yamashina, H., Kubo T., “Manufacturing cost deployment’’, International Journal ofProductionResearch,vol.40,no.16,pp.4077‐4091,2002.

[17] Yamashina, H., “Cost Deployment‐establishing a SMART cost reduction programme”,Volvo Construction Equipment. [Online] Available:https://teamplace.volvo.com/sites/bhl/VPS/CI/default.aspx?RootFolder=%2Fsites%2Fbhl%2F

VPS%2FCI%2FShared%20Documents%2FPrioritisation%2FCost%20Deployment%2FTraining&

FolderCTID=0x012000864406804B95224B9B08D171B7C6E4ED&View={82FC11BB‐22BC‐

4CD4‐A011‐

979812692021}&InitialTabId=Ribbon%2ERead&VisibilityContext=WSSTabPersistence

VolvoConstructionEquipment.Internal.[Accessed2014‐04‐10].

[18] Gorazd, P., Business Controller. Volvo Construction Equipment, Wroclaw. [Interview]2014‐04‐15.

[19] Lundgren,R.,BusinessController.VolvoPowertrain,Skövde.[Interview]2014‐07‐10.

[20] Ståhl,J.E.,“IndustriellatillverkningssystemdelII–Lӓnkenmellanteknikochekonomi”,2ndedition, Division of Productionand Materials Engineering, Lund University, Lund,2012.

[21] Son,Y.K.,“Acostestimationmodelforadvancedmanufacturingsystems”,InternationalJournalofProductionResearch,vol.29,no.3,pp.441‐452,1991.

[22] Özbayrak, M., Akün, M. & Türker, A.K., “Activity‐based cost estimation in a push/pulladvancedmanufacturingsystem”,InternationalJournalofProductionEconomics,vol.87,pp.49‐65,2004.

[23] Chiadamrong,N.,“Thedevelopmentofaneconomicqualitycostmodel”,TQM&BusinessExcellence,vol.14,no.9,pp.999‐1014,2003.

[24] Son,Y.K.,“Acostestimationmodelforadvancedmanufacturingsystems”,InternationalJournalofProductionResearch,vol.29,no.3,pp.442,1991.

[25] Ståhl,J.E.,“IndustriellatillverkningssystemdelII–Lӓnkenmellanteknikochekonomi”,2ndedition,pp.161,DivisionofProductionandMaterialsEngineering,LundUniversity,Lund,2012.

[26] Liou, F‐S. & Borcherding, J. D., “Work Sampling Can Predict Unit Rate Productivity”,JournalofConstructionEngineeringandManagement,March,pp.90‐103,1986.

[27] Jenkins, J. L. & Orth, D. L., “Productivity Improvement throughWork Sampling”,AACEInternationalTransactions,csc.05,pp.1‐7,2003.

45

[28] Konorti,E.,“Giveratio‐delayachance”,IndustrialEngineer,November,pp.34‐39,2012.

[29] “Föreläsning 5, Statistiska metoder”, Linköpings universitet. [Online]. Available:http://www.ida.liu.se/~732G60/Forelasning%205.pdf.[Accessed:2014‐07‐22].

[30] Zetterqvist,L.,AssociateProfessor.LundUniversity,Lund.[Interview]2014‐05‐20.

[31] Hüttmeir, A., de Treville, S., van Ackere, A., Monnier, L. & Prenninger, J., ”Trading offbetweenheijunkaand just‐in‐sequence”, International JournalofProductionEconomics,vol118,pp.501‐507,2009.

[32] Maleszka,T.,ProductionManager.VolvoConstructionEquipment,Wroclaw.[Interview]2014‐05‐05.

[33] Windmark, C.&Andersson, C., “Costmodels of inbound logistics activities: supportingproductionsystemdesign”,