Example

1Simulation in the Supply Chain Domain : Evaluating modelling approachesMr. Chris OwenDr. Pavel AlboresDr. Doug Love

Operations and Information Management GroupAston Business SchoolAston University, Birmingham

1Supply chains are dynamic and complex, they involve the integration, coordination and synchronisation of activities between different business entities.

Supply chains require the transmission of both material and information between those business entities.

The characteristics of supply chains mean that simulation is a useful technique.their complexity obstructs analytic evaluation (Van der Zee and Van der Vorst, 2005)

There are many simulation approaches used in the supply chain, however There is little advice for practitioners on when to use each method.Much of the advice that does exist relies on custom and practice.There is little back to back modelling of the same problem using more than one technique.

Background and Context

VAN DER ZEE, D. J. & VAN DER VORST, J. G. A. J. 2005. A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*. Decision Sciences, 36, 65-95.2Research QuestionsWhat are the main methods of simulation used to improve supply chain performance?What are the theoretical building blocks and assumptions that lie behind these techniques? How does this illuminate the supply chain problem types for which certain techniques might be better suited than others? What are the relative strengths and weaknesses of different techniques in simulating certain supply chain problem types?What experiments can be done to test and compare alternative approaches?How can these conclusions be used to generate recommendations for practitioners on how they should deploy these tools in achieving their supply chain objectives?

3Research QuestionsWhat are the main methods of simulation used to improve supply chain performance?What are the theoretical building blocks and assumptions that lie behind these techniques? How does this illuminate the supply chain problem types for which certain techniques might be better suited than others? What are the relative strengths and weaknesses of different techniques in simulating certain supply chain problem types?What experiments can be done to test and compare alternative approaches?How can these conclusions be used to generate recommendations for practitioners on how they should deploy these tools in achieving their supply chain objectives?

4There are three main methods of simulation in the supply chain contextDiscrete Event SimulationSystem DynamicsAgent Based Modelling

Classification of 100 papers from literature search on simulation and the supply chainWhy not Mathematical Modelling?How did we arrive at these three?Add in diagram on papers5Proposition 1 : Discrete methods of simulation can be useful in investigating strategic problem types as well as System Dynamics;

Proposition 2 : Discrete methods of simulation can represent feedback effects in models;

Proposition 3 : System Dynamics can model supply chain problem types at the operational end of the spectrum as well as the strategic;

Proposition 4 : The nature and role of decision makers in the problem may influence the selection of simulation technique;

Proposition 5 : The purpose of the modelling (exploratory, problem solving or explanatory) may influence the selection of simulation technique.Propositions were formulated from the literature review and theoretical analysis6The case study method was selected to test the propositions

The key areas for investigation were :the level of the supply chain problem on a scale from strategic, tactical and operational;the importance of feedback as a feature of the problem;the role of decision makers in the problem and how they can be represented;the purpose of the modelling itself, whether exploratory, descriptive or explanatory.

Case Study ApproachHowever, there are times when little is known about a phenomenon, current perspectives seem inadequate because they have little empirical substantiation, or they conflict with each other or common sense. (Eisenhardt, 1989).EISENHARDT, K. M. 1989. Building Theories from Case Study Research. The Academy of Management Review, 14, 532-550.7A multiple case approach was usedDevelop theorySelect casesConduct 2nd case studyWrite individual case reportDraw cross-case conclusionsDesign data collection protocolConduct 1st case studyConduct remaining case studiesWrite individual case reportWrite individual case reportModify theoryDevelop policy implicationsWrite cross-case reportYIN, R. K. 2003. Case Study Research - Design and Methods, London, Sage.8Case StudiesCase StudyCase characteristicsSimulation Approaches UsedStrategic Purchasing CaseModelling the centralisation of procurement in a large construction companySystem DynamicsAgent Based ModellingBullwhip CaseDemand amplification System DynamicsAgent Based ModellingCoffee pot ProblemGlobal logistics problem balancing inventory with customer serviceDiscrete Event SimulationSystem DynamicsOperational Scheduling ProblemDetailed scheduling of job shopDiscrete Event SimulationSystem Dynamics9Case StudiesCase StudyCase characteristicsSimulation Approaches UsedStrategic Purchasing CaseModelling the centralisation of procurement in a large construction companySystem DynamicsAgent Based ModellingBullwhip CaseDemand amplification System DynamicsAgent Based ModellingCoffee pot ProblemGlobal logistics problem balancing inventory with customer serviceDiscrete Event SimulationSystem DynamicsOperational Scheduling ProblemDetailed scheduling of job shopDiscrete Event SimulationSystem Dynamics

Strategic Purchasing SD ModelStrategic Purchasing Agent Model10Case StudiesCase StudyCase characteristicsSimulation Approaches UsedStrategic Purchasing CaseModelling the centralisation of procurement in a large construction companySystem DynamicsAgent Based ModellingBullwhip CaseDemand amplification System DynamicsAgent Based ModellingCoffee pot ProblemGlobal logistics problem balancing inventory with customer serviceDiscrete Event SimulationSystem DynamicsOperational Scheduling ProblemDetailed scheduling of job shopDiscrete Event SimulationSystem Dynamics

Bullwhip SD ModelBullwhip SD ModelBullwhip Agent Based Model11Case StudiesCase StudyCase characteristicsSimulation Approaches UsedStrategic Purchasing CaseModelling the centralisation of procurement in a large construction companySystem DynamicsAgent Based ModellingBullwhip CaseDemand amplification System DynamicsAgent Based ModellingCoffee pot ProblemGlobal logistics problem balancing inventory with customer serviceDiscrete Event SimulationSystem DynamicsOperational Scheduling ProblemDetailed scheduling of job shopDiscrete Event SimulationSystem Dynamics

Coffee Pot DES ModelCoffee Pot SD Model12Case StudiesCase StudyCase characteristicsSimulation Approaches UsedStrategic Purchasing CaseModelling the centralisation of procurement in a large construction companySystem DynamicsAgent Based ModellingBullwhip CaseDemand amplification System DynamicsAgent Based ModellingCoffee pot ProblemGlobal logistics problem balancing inventory with customer serviceDiscrete Event SimulationSystem DynamicsOperational Scheduling ProblemDetailed scheduling of job shopDiscrete Event SimulationSystem Dynamics

Operational SD ModelOperational DES Model13FindingsCase Study 1 Strategic PurchasingCase Study 2 Bullwhip and SD ArchetypesCase Study 3 Coffee Pot ProblemCase Study 4 Operational SchedulingProposition 1 - Discrete methods of simulation can be useful in investigating strategic problem types as well as System Dynamics.SupportedThe purchasing case clearly demonstrates that ABM can be used very effectively to investigate a strategic problem type. In fact, it could be argued that it identified key decision making in the system which was not identified by the SD approach.SupportedThe Bullwhip case clearly demonstrates that discrete methods, and in this case Agent Based Modelling (ABM) can be used effectively to investigate a strategic problem type.SupportedDiscrete Event Simulation (DES) is found to be very effective in investigating this supply chain problem which spans strategic to operational issues.Not applicableProposition 2 - Discrete methods of simulation can represent feedback effects in models.SupportedThe agent based model does incorporate feedback effects.SupportedBoth the Bullwhip case and the SD archetypes demonstrated that discrete methods can represent feedback effects in models.SupportedThis case demonstrates that both feedback and feed forward of information can be effectively modelled by DES.Not applicableProposition 3 - System Dynamics can model supply chain problem types at the operational end of the spectrum as well as the strategic.Not applicableNot applicableContradictedThis case demonstrates that SD cannot model classes of supply chain problem where discrete behaviour or measures are important.ContradictedThis case demonstrates that there are practical limits to the applicability of SD to discrete problem types.Proposition 4 - The nature and role of decision makers in the problem may influence the selection of simulation technique.SupportedThis case demonstrates that ABM can be very effective in identifying the key decision makers in systems. It also provides some evidence that the SD approach may contain risks that it may ignore key decision makers embedded in the system.SupportedABM is found to be very effective at locating the decision making process in a given agent. This means that the representation of decision making by individuals is perhaps more accurate and easier to model than in SD, where the decision making process is detached from the individuals in the system. SupportedDES seems to be suited to problems where decision making is quite mechanistic. If more complex decision making processes are required, ABM may be needed.SupportedDES appears to be suited to modelling fairly simple, mechanistic decisions. Proposition 5 - The purpose of the modelling (exploratory , problem solving or explanatory ) may influence the selection of simulation technique.SupportedBoth SD and ABM appear to be well suited to exploratory or explanatory modelling.SupportedBoth SD and ABM are found to be useful in exploratory and explanatory modes. The transparency of SD models may lend the approach to a more interactive process than ABM.SupportedDES seems well suited to a problem solving approach.SupportedDES appears to be suited to problem solving.14An initial guidance framework for practitionersApproachAspectSystem DynamicsDiscrete Event SimulationAgent Based ModellingPurpose of the enquiryCharacteristics of the problemProblem Level StrategicProblem Level OperationalDiscreteness of measures, entities, resources, process is importantDecision making processPhysical SpaceCharacteristics of the approachPerspectiveFeedbackExtending ideas from LORENZ, T. A. J., A. 2006. Towards an orientation framework in multi-paradigm modelling. 23rd International Conference of the System Dynamics Society. Nijmegen.15An initial guidance framework for practitionersApproachAspectSystem DynamicsDiscrete Event SimulationAgent Based ModellingPurpose of the enquiryExploration and explanation of dynamic relationshipsInteractive investigation of policies with clientProblem solvingOptimisationCan be used for exploration and explanation, but transparency and client involvement become keyExplorationUnderstanding of agent behavioursInvestigation of unexpected consequencesCharacteristics of the problemProblem Level StrategicPolicy investigation and evaluationsCan be used at the strategic level but transparency of models and involvement of clients becomes keyUseful when understanding of individual agent behaviours are the focus of enquiry. Can be used at the strategic level but transparency of models and involvement of clients becomes keyProblem Level OperationalMay be vulnerable to missing important decision making at this levelCannot be used for certain discrete problems at this levelProcess level investigation into inventory levels, customer service, physical logisticsInvestigation into behaviour of individual agent behaviours at the operational level.Discreteness of measures, entities, resources, process is importantAggregation of measures , entities, resources and processes is acceptableDiscrete measures or behaviour of discrete entities, resources and processes is an important aspect of the problemDiscrete measures or behaviour of discrete entities, resources and processes is an important aspect of the problemDecision making processNot important to link decision process in the model directly to decision makers in the real worldNot important to link decision process in the model directly to decision makers in the real worldImportant to link decision process in the model directly to decision makers in the real worldPhysical SpaceNot important to the problemMay be important to the problemMay be important to the problemCharacteristics of the approachPerspectiveTop down, development of dynamic hypothesisProcess perspective (material and information flows)Agent perspective (essentially bottom up)FeedbackModelled explicitlyCan be modelled but is hiddenCan be modelled but is hiddenExtending ideas from LORENZ, T. A. J., A. 2006. Towards an orientation framework in multi-paradigm modelling. 23rd International Conference of the System Dynamics Society. Nijmegen.16Limitations and Further WorkThis research has used a limited number of cases for the analysis. One practical case has been used and three typifications or secondary cases. More cases would clearly lead to a higher level of confidence in the generalisability of the findings. Another potential limitation has been that the modelling has been carried out by this researcher. This means that there is some risk of bias, since this researcher may have conscious or unconscious preferences or attitudes that are leading to the focusing on certain issues and the biasing of results. More real life cases would lead to more confidence in the findings. 17Limitations and Further WorkThis research has used a limited number of cases for the analysis. One practical case has been used and three typifications or secondary cases. More cases would clearly lead to a higher level of confidence in the generalisability of the findings. Another potential limitation has been that the modelling has been carried out by this researcher. This means that there is some risk of bias, since this researcher may have conscious or unconscious preferences or attitudes that are leading to the focusing on certain issues and the biasing of results. More real life cases would lead to more confidence in the findings. 18Summary / ConclusionsThe three main methods of simulation (SD, DES and ABM) in the supply chain domain have been compared through back to back modelling of case studiesDiscrete methods (DES and ABM) have been found able to model all problem types and in particular to be useful in modelling strategic problem typesSystem Dynamics has been found to have hard limits to its application in more operational and discrete problemsSystem Dynamics (as a top down approach) may be vulnerable to missing decision making at the operational levelA framework to assist practitioners in the selection of the appropriate method has been presented19Summary / ConclusionsThe three main methods of simulation (SD, DES and ABM) in the supply chain domain have been compared through back to back modelling of case studiesDiscrete methods (DES and ABM) have been found able to model all problem types and in particular to be useful in modelling strategic problem typesSystem Dynamics has been found to have hard limits to its application in more operational and discrete problemsSystem Dynamics (as a top down approach) may be vulnerable to missing decision making at the operational levelA framework to assist practitioners in the selection of the appropriate method has been presented20Questions ?21FindingsPropositionFindingsProposition 1 : Discrete methods of simulation can be useful in investigating strategic problem types as well as System Dynamics;Supported both by case study findings and enfolding literature review.Proposition 2 : Discrete methods of simulation can represent feedback effects in models;Supported both by case study findings and enfolding literature review.Proposition 3 : System Dynamics can model supply chain problem types at the operational end of the spectrum as well as the strategic;Rejection of the original proposition supported by case study findings and refined by enfolding literature review. System Dynamics (SD) cannot model problems where the performance measure or the behaviour of discrete entities, resources or processes is the focus of interest.Proposition 4 : The nature and role of decision makers in the problem may influence the selection of simulation technique;Decision making is modelled differently by all three approaches. Each approach has limitations.Proposition 5 : The purpose of the modelling (exploratory, problem solving or explanatory) may influence the selection of simulation technique.Some insights into the potential uses for all three approaches in all different modes of enquiry depending on the philosophical stance of the modeller and the client.Proposition 6 : System Dynamics (SD) is vulnerable to the risk of overlooking important decision making processes at the operational level and of making a Type III error.The purchasing case has identified this potential risk. The literature appears to support the possibility of this error, although proposes a methodological solution (calibration).22