STAKEHOLDER ENGAGEMENT AND INCLUSION IN COMPUTER SIMULATION PROJECTS: A CROSS-SECTOR SURVEY OF

COMMERCE, DEFENCE AND HEALTH-CARE

M. Jahangirian¹, T. Eldabi¹, P. Harper², A. Naseer¹, B. Patel³, L.K. Stergioulas¹

¹Brunel University, Uxbridge, UB8 3PH, UK ²Cardiff University, Cardiff, CF10 3XQ, UK

³University of Southampton, Southampton, SO17 1BJ, UK

mohsen.jahangirian@brunel.ac.uk, Tillal.eldabi@brunel.ac.uk, harper@Cardiff.ac.uk, Aisha.naseer@brunel.ac.uk, Brijesh.Patel@soton.ac.uk, lampros.stergioulas@brunel.ac.uk

Abstract Computer simulation has been applied in various sectors, such as manufacturing, services, defence, health-care, and public services. However, there seems to be an inconsistency in its application across the sectors, with some sectors in a more mature position than others. This paper principally aims to offer a cross-sector comparison of the results of large-scale reviews on the application of simulation in three sectors, namely Commerce, Defence, and Health-care. The focus of the comparison will be on the level of stakeholder engagement. The reviews spanned peer-reviewed academic literature over a 10-year period of 1997-2006. Results of our survey show that health-care lags far behind the other two sectors in terms of stakeholder engagement in simulation projects. We also report on how various simulation techniques, application areas as well as commerce industries have performed with respect to stakeholder engagement. It appears that there are three sets of factors influencing the level of stakeholder engagement in simulation projects, namely Technical, Organisational and Industry-Specific factors. Finally, we provide a list of recommendations for the health-care sector, relating to a number of issues: 1) Developing Health-Care simulators, 2) Adopting Simulation Gaming as a training tool to promote simulation in the health-care, 3) Promoting the concept of ‘User Leaders’ and their success stories, 4) Adopting Agent-Based Simulation (ABS) and Intelligent Simulation to manage complexities of health-care and modelling human behaviour, and 5) Communicating with stakeholders through user-friendly simulation tools and techniques.

Keywords Computer Simulation, Stakeholder Model, User Involvement, Stakeholder Engagement, Commerce, Defence, Health-care

1 INTRODUCTION

Computer Simulation is recognised as the second most widely used technique in the field of operations management, the most popular being ‘Modelling’ ([12], [2]). Its use has been transformed by the invention and evolution of the computer, which has boosted the uptake of practical simulation tools and techniques. Since its inception, simulation has been applied in various sectors, such as manufacturing, services, defence, health-care, and public services. Designing simulation models and representations of user scenarios are considered to be critical processes when planning business operations, military exercises, or even health-care pathways. However, there is a question as to ‘whether there is a consistency in its application across various sectors’, and also ‘if there is any correlation between the level of stakeholder engagement and simulation techniques, application areas, or industries across different sectors’. This paper examines these questions. Some researchers such as Young et al. [17] and Kuljis et al. [9] have asserted a need to learn from other sectors to enhance the benefits of applying simulation in the Health-care sector. We principally aim to contribute towards such cross-sector insight through an extensive literature review. Several field studies within the UK health-care system have also added value to our study.

2 BACKGROUND AND PREVIOUS STUDIES The simulation field holds a longstanding record of applications in various domains probably dating back to 1777 when a manual Monte Carlo technique was used by Buffon to estimate π [7]. Computer simulation began during the World War II in early 1940s and Discrete-Event Simulation probably originated in the late 1940s [11]. Industrial environments claim the first applications of Discrete Event Simulation (DES), System Dynamics (SD), Petri-Nets, and Hybrid Simulation, while the military domain has recognized a long-standing history of applications using Simulation Gaming mostly for training purposes. Therefore, commerce and defence sectors have accumulated a massive amount of simulation-related knowledge over the past 60 years, which can be extracted and transferred to other sectors such as Health-care.

The paradigm of ‘business and society’ was articulated by several researchers among whom Preston and Post [14] is probably a landmark work in this process. They generally argued for links between business and society, suggesting that businesses need to be responsive to the external issues and social entities via a series of strategies ranging from reactive, to proactive and interactive. More recently, theorists have proposed a stage model in which individual firms increase their level of engagement with stakeholders over time. In this view, in the first stage, companies identify or map stakeholders and their interests; in the second stage, they attempt to manage stakeholders and the social issues of concern to them; and, in the third stage, they actively engage stakeholders for long-term value creation. Stakeholder engagement, in contrast to earlier stages, involves a stance of mutual responsibility, information-sharing, open and respectful dialogue, and an ongoing commitment to joint problem-solving [16], [1].

There is a good deal out there about the theories of the business-stakeholder relationship and the stages through which it may progress. Also, considerable effort has been devoted to developing and enhancing ‘Stakeholder Models’ where stakeholders are identified and classified, and their interactions are modelled [4]. However, there seems to be a gap in research on the drivers and factors influencing the level of stakeholder inclusion and engagement. This paper examines such factors in the context of computer simulation projects, with a focus on the health-care domain.

3 STAKEHOLDER MODEL

The ‘Stakeholder’ term constitutes a rather more comprehensive meaning than the ‘User’ does. It can be defined as “any individual, group or organization that can affect or be affected (positively or negatively) by the system under study and that has direct or indirect influence on its requirements” [13]. The engagement of stakeholders plays a vital role in the successful implementation of any ICT project.

To start with, we present a stakeholder model of UK health-care system in the context of simulation projects (Figure 1). This model identifies a wide variety of internal and external stakeholders, including:

1) Internal stakeholders: e.g. clinicians, carers, commissioners, Strategic Health Authorities, National Institute for Clinical Excellence, managers, Department of Health (DoH).

2) External stakeholders: e.g. patients, private health sector, regulatory agencies, technology suppliers/manufacturers, universities and professional communities, consulting companies.

Internal stakeholders can be considered as the direct users of simulation, within a spectrum in which some contribute more to providing information for simulation projects, such as clinicians and carers, and some others receive information as a result of simulation project implementation, such as DoH, NHS managers, etc.

While this model illustrates a high degree of complexity in terms of stakeholder management, this study addresses internal stakeholders only, sometimes also called ‘Users’.

4 LITERATURE REVIEWS

This paper compares the results of extensive reviews of literature on the application of simulation in three sectors: commerce (including manufacturing and business), defence (including military and aerospace), and health-care. The three reviews were carried out as part of the RIGHT project (Research Into Global Health-care Tools) which aims to

map and connect modelling and simulation tools and to develop a systematic way of selecting the best tools for a given problem scenario.

Figure 1: Stakeholder model of UK health-care sector for Simulation projects

The reviews spanned peer-reviewed academic literature over a 10-year period of 1997-2006. The literature reviews for the selected sectors were based on a common structured review process developed in-house for dealing with a vast body of literature. The search was undertaken within three of the most widely used academic electronic data-bases: JSTOR (www.jstor.org), Scopus (www.scopus.com), and the ISI Web of Knowledge (www.wok.mimas.ac.uk). The scale of the search results was so large - for instance, around 20,000 papers for the commerce sector alone - that screening mechanisms were essential to target the most relevant body of literature. Therefore, a series of filtering methods were adopted using a relatively new visualization software tool called CiteSpace. For example, we used CiteSpace to identify and exclude some of the irrelevant keywords. Another use of the CiteSpace was to carry out an efficient ‘Reference Chasing’ within the health-care domain. Figure 2 exhibits a snapshot of the CiteSpace results. Abstract and full-text reviews were also used to filter out the irrelevant papers. Table 1 shows the number of relevant papers ultimately found in the literature for each of the three domains.

Table 1: Search results corresponding to literature review process for the three domains

Health-care Commerce Defence

Initial result of Database searching ~ 9,700 ~ 20,000 ~ 9,000 Final no. of papers 87 281 104

The reviewers identified 4 key pieces of information from each paper, namely:

a) Simulation technique used: An open judgement was exercised to identify the simulation techniques. When there was no explicit reference, reviewers used their expertise to identify the technique.

Technology Suppliers/Manufacturers

Department of Health

Private Health Sector

Regulatory Agencies

Patients

CliniciansCarers

National Institute for Clinical Excellence (NICE)

Universities/ Professional communities

(e.g. MaSHNet) NHS Managers

Commissioners Strategic Health Authorities

UK National Health

Services (NHS)

Consulting Companies

UK Public health-care

Output User

Information Provider

Figure 2: A snapshot of the CiteSpace result from reference chasing in health-care simulation

b) Application area: While general scope of all three reviews in terms of application areas were defined to be management and process-related aspects, yet there were variants of application areas for each sector. Therefore a consolidated mapping of application areas consisting of eight categories was worked out for comparative purposes. These categories are: 1) Policy, 2) High-Level Planning, 3) Detailed Planning, 4) Human Behaviour, 5) Workforce Management, 6) Quality Management, 7) Risk Management, 8) Research & Development (R&D).

c) Type of industry: Apart from defence and health-care each of which represents, to some extent, one industry, a list of various industries was drawn for the Commerce sector.

d) Level of stakeholder engagement: To illustrate and analyse the level of user/stakeholder engagement in simulation projects, we classified the literature into three groups, defined as follows:

i. Class A or Real Problem-Solving papers: where simulation was applied on a real problem with real data. This class demonstrates a project with a significant level of user/stakeholder engagement in the simulation part.

ii. Class B or Hypothetical Problem-Solving papers: where simulation was applied for the purpose of solving a real-life problem, but using artificial data rather than real data. This is usually aimed at providing generic solutions.

iii. Class C or Methodological papers: where research was conducted to enhance the methodology of simulation itself regardless of any specific application area. No experimental study was carried out in such a case. This class and class B do not usually involve real user/stakeholders.

5 RESULTS OF THE LITERATURE REVIEWS

Four kinds of analyses by level of user engagement, simulation technique, application area and industry were conducted, all with a stress on the user engagement.

5.1 By level of user engagement

Figure 3 exhibits the number and percentage of class A papers in each sector. It can be seen that health-care lags far behind the other two sectors in terms of stakeholder engagement. Proportions of Class A publications for commerce, defence, and health-care were 49%, 39% and 8%, respectively. An attempt to explain these results would be useful. Thus the next step of analysis considered different perspectives of the results and provides meaningful insights on cross-sector practice with a focus on the low user/stakeholder engagement in the health-care sector.

Note: the figures in brackets are the no. o f class A papers

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Commerce Defence Health-Care

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Figure 3: Percentage of real problem solving (class A) papers in commerce, defence and health-Care

sectors

5.2 By simulation technique

In order to see if there is any correlation between a simulation technique and the level of stakeholder engagement, we calculated the percentage of real problem-solving papers (class A) for each simulation technique. Figure 4 shows top six simulation techniques in Commerce and Defence with highest proportion of class A papers. A high level of user engagement associated with Simulation Gaming in Commerce could be explained by the high level of interaction and competition involved. Traffic simulation benefits from the nature of traffic control problems, for which it is perhaps easier to gather data, while it carries more relevance to society and everyday life [6].

Distributed Simulation is found to be effective for distributed organisational structures, and its high degree of user engagement in defence could represent such compatibility within the sector where the resources are very much dispersed. Apart from their suitability to the user organization’s characteristics and requirements, the availability of distributed infrastructures such as the Web and High Level Architecture (HLA) could also explain such a high impact.

Commerce Defence

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Note: Figures in the brackets are no. of class A papers

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Figure 4: The percentage of real problem-solving (class A) papers for top six simulation techniques in

Commerce and Defence

System Dynamics also shows a relatively high rate of user engagement in both sectors, which could be due to the fact that it offers a holistic picture with users and helps them to understand their system as a whole. Furthermore, it may be claimed, based on the results of our field study as well as our previous experience, that a large community of users are involved and interested in the organization’s strategic issues, which are the set of problems appropriately addressed by System Dynamics.

5.3 By application area

We also conducted an analysis on the correlation between application areas and level of user engagement within the commerce and defence sectors. The results can be seen in Figure 5. It implies that simulation has benefitted from a relatively high level of user engagement within the two sectors with respect to a few areas of applications, such as ‘Detailed Planning’, ‘Policy’, and ‘Work-force Management’. This finding could represent a higher success rate of simulation projects in those application areas, maybe as a result of a good match between nature of the application and simulation capabilities. For instance, we know that simulation is very good at resource allocation and efficiency enhancement that are sub-categories of detailed planning. ‘Policy-level matters’ is another area that benefits from modelling and simulation in a sense that it can be translated into a simplified, user-friendly, holistic picture of the whole system, making problem solving more understandable and manageable by the user community. While work-force management has some similarities with detailed-planning, it also includes a ‘Training’ element, which can well be accomplished by means of interactive simulations.

Commerce Defence

(5)

(13)

(20)(78)

(16)(5) (10) (4)

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Note: figures in the brackets are the no. o f class A papers.

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Figure 5: Top 4 application areas in commerce and defence in terms of the number of real problem-

solving papers

5.4 By industry

Figure 6 shows top four commerce industries in terms of the number of class A papers, namely a) Electronic Parts Manufacturing (including semi-conductors, computer hardware, and telecommunications), b) Traffic and Transportation, c) Automotive, and d) Construction. These four industries are characterized by either a high degree of ‘automation’ involved in the process, or a high level of ‘investment’ made, or both. Therefore, what can be implied from this observation is that industries with high degree of automation and/or high level of investment (i.e. large-scale applications) could make the best use of simulation.

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Electronic PartsManufacturing

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Automotive Construction

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Figure 6: Top four commerce industries in terms of the number of real problem-solving papers

6 DISCUSSION

The findings of our cross-sector review highlight a number of factors that influence the level of user/stakeholder inclusion and engagement in simulation projects. These factors can be grouped into three categories:

a) Technical factors: 1) Interactivity: Literature on simulation has long recognized the value of interaction since the inception of

Visual Interactive Simulation (VIS) in the late 1970s [3]. “By watching a simulation model progress through time and having the ability to interact with it, the user can improve his analysis and understanding of the original problem situation … The decision maker can watch the progress of the model and can himself contribute to the validation of the model. Hence, he will have more confidence in its use, because of his participation” [5]. Findings of our study with respect to the user engagement aspect of Gaming Simulation in the Commerce have also confirmed this. In addition to ‘Real-time Interactions’ and ‘Visual Representations’ of the conventional VISs, Gaming Simulations also benefit from two more user-friendly features, namely ‘Real-life Simulation’, and ‘Competition-based Interactions’.

2) Simulators: Simulators are a category of simulation packages that are generally menu-driven, with Graphical User Interfaces (GUI) and visualization tools, enabling users to input data and adopt basic logic commands while little or no programming skills are required, therefore enhancing user involvement. A good number of simulators in industrial domain, such as SIMFACTORY, WITNESS, ProModel, AutoMod, and Taylor ED with built-in templates for manufacturing, material handling systems and storage facilities were introduced perhaps since late 1980’s. Over the past 20 years, they have played a major role in facilitating the user involvement within simulation projects. It was probably till 1993 when the first health-care simulator - called MedModel - came into market and since then a few other efforts, more towards bringing some health-care add-ins into simulation packages, were made in this direction. Hospital Navigator (Rockwell Automation) and Scenario Generator (Simul8 Corp.) are two examples.

3) Data issues: Simulation projects are usually projects that depend on the availability of a relatively large set of data about the system. The accuracy of data seems to be equally important [15]. Robinson explains how these issues could be so frustrating for a simulation expert. Data issues in health-care are even more challenging, firstly because of the high sensitivity of the data (which is discussed later under ‘industry-specific factors’) and secondly due to the fact that data should be provided mostly by the clinicians and carers (see Stakeholder model in Figure 1), who are usually quite busy in their jobs. Therefore, any attempt to facilitate the provision of this very crucial requirement will result into an extremely valuable improvement.

b) Organisational factors: 1) User leaders: It can be seen from the results of the commerce sector (esp. manufacturing industries) that

there are leaders in the sector who have used and implemented simulation and have provided a valuable momentum for further promotion of simulation in the arena. These user communities are often amongst the large-size industries with a huge investment involved such as Automotive, Traffic & Transportation, and Construction. Jahangirian et al. [6] demonstrate the dominance of simulation applications in such industries. In a study to compare simulation practices in the UK and the US, Murphy and Perera [10] also cite the importance of ‘Industrial Leaders’, which enable other companies to recognise the practical capabilities and benefits of simulation.

2) User participation: A project will receive higher user engagement if there is an element of user participation, which means helping users to understand the process and the final picture as well as facilitating their participation. Field studies conducted by the RIGHT project within a number of UK health-care units suggests that users are keen to be informed about the changes in the organization and to play a part in it. Therefore, a simulation project should be able to communicate with the user community through various modes of contact.

3) Organisational structure: A large, hierarchical organization, such as a national health-care system, is less agile and more resistant to changes. Big players of the commerce sector, especially those with dispersed facilities around the world, have established a long history of achievements in structural improvements as a crucial requirement for change projects, e.g. simulation projects, in the organization.

c) Industry-specific factors: 1) Complexity and dynamics: Although simulation techniques are assumed to address complexity and dynamic

features of a system, such as stochastic parameters, simulation projects will face difficulties if systems of high complexity are going to be modelled. Manufacturing industries in general and such industries as Electronic Parts Manufacturing and Automotive in particular, benefit from a high level of automation and standardization which might not be found in other sectors. On the other hand, health-care systems are generally characterized by a high degree of complexity in handling resources, which is not frequently found in the manufacturing sector [8]. The RIGHT field studies within a number of health-care systems in the UK, including a simulation pilot project in an A&E department of a London hospital, specified a number of distinctive types of complexity involved, such as i) uncertainties with process and activity times (no two identical care cases exist, as opposed to identical products in a manufacturing setting), ii) expert human beings as the main resources of the health-care system, as opposed to machines or workers in the manufacturing contexts, and iii) ethical issues involved in the health-care systems.

2) Level of competition: Competition is seen as a main driver of change, contributing a lot towards the development of a well-motivated environment for improvements and thus the involvement of the stakeholders. We believe this has played a significant role in the success rate of simulation projects in the Commerce sector. Electronic parts manufacturing industry and Automotive industry with a high rate of simulation applications, as shown earlier in Figure 6, are actually amongst the most competitive industries in the world.

3) Sensitivity of service/product and data: Health-care sector is characterized by a high level of data sensitivity and confidentiality, which imposes a great challenge for information-intensive techniques such as simulation. This is in contrast to the commerce sector, where a very limited and marginal amount of personal data is involved.

7 CONCLUSIONS

The purpose of this study was to offer some cross-sector insight with regard to the implementation of simulation projects. The focus of our study was specifically on the level of user engagement. Three extensive literature reviews

on the application of simulation in commerce, defence, and health-care sectors, were carried out to provide a research platform. The findings from the comparative analysis of the literature reviews indicated that:

a) Health-care lags far behind the other two sectors in terms of stakeholder engagement.

b) There are a number of simulation techniques with higher rate of stakeholder engagement, such as Simulation Gaming, System Dynamics, Distributed Simulation, Traffic Simulation, Hybrid Simulation and Discrete-Event Simulation.

c) In terms of application areas, ‘Detailed Planning’, ‘Policy’, and ‘Work-force Management’ seem more fitting with the simulation capabilities.

d) Simulation has the highest penetration rate in such commerce industries as ‘Electronic Parts Manufacturing’, ‘Traffic and Transportation’, ‘Automotive’, and ‘Construction’.

Based upon these analyses, three sets of factors were found to influence the level of user engagement in simulation projects, namely technical, organisational and industry-specific factors. Based on these factors, one can put forward a number of recommendations for the health care sector in order to get the most out of simulation techniques, as follows:

1) Developing Health-Care simulators: Developing advanced simulators with capabilities that handle specific challenges of health-care systems, such as ‘behavioural modelling’, ‘complex system modelling’, and ‘modelling with insufficient or poor data’, will improve the implementation rate of simulation in the health care sector.

2) Adopting Simulation Gaming as a training tool to promote simulation in the health-care: Simulation Gaming technique can be used as one of the most effective tools to build up momentum for user engagement through workshops in which users receive a game-based training using bespoke educational simulation games.

3) Promoting the concept of ‘User Leaders’ and their success stories: This basically means the identification of user leaders in the health care sector, whose role could be to establish a momentum, and then to promote their success stories afterwards. Those leaders should represent a less complex system, with a well-funded support for improvements. One example of a user leader is an outpatient department in a newly-funded hospital, where simulation will address planned care with a lower level of complexity.

4) Adopting ABS and Intelligent Simulation to manage complexities of health-care and modelling human behaviour: Agent-Based Simulation (ABS) and Intelligent Simulation techniques, which focus on more complex systems with lots of interactions and human behaviour involvements, could be among the more appropriate simulation techniques for health-care systems.

5) Communicating with users through user-friendly tools and techniques: Some simulation techniques lend themselves to a better communication with the user community; for instance SD is a powerful technique that makes use of simple causal-loop diagrams, which are more understandable by new users. Simulation Gaming is another example that is able to pass a key message to the users very quickly.

8 ACKNOWLEDGEMENTS This work was supported in part by the EPSRC, UK (RIGHT project, Grant No: EP/E019900/1; < http://www.right.org.uk>). We would also like to thank Prof. Terry Young and Prof. Sally Brailsford for their constructive inputs and comments throughout the research.

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