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This paper presents a methodology for developing simulation conceptual models that incorporate the SCOR Process Reference Model. It outlines the importance of conceptual modelling, requirements, design, and guidelines for building effective models. The paper also discusses the uniqueness of simulation conceptual models and their application in supply chain management. The proposed methodology includes incorporating existing guidance, domain knowledge, and a general process, as well as providing inputs and standard descriptions for the modelling process.
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A Methodology for Simulation Conceptual Modelling that Embeds the SCOR Process Reference Model Dr. Miles Weaver, School of Management, Edinburgh Napier University m.weaver@napier.ac.uk@DrMilesWeaver #SimCM
Outline • Importance of conceptual modelling and need for a SCM2 • Requirements (Specification) - (what do we need the conceptual model to do) • Outline design - (what procedure is needed to arrive at this conceptual model) • Illustrationof the SCM2 and the incorporatedkey concepts • General guidelines for building an ‘effective’ conceptual modelling • Embedding the utility of a process reference model (e.g. SCOR)
What is a simulation conceptual model? A simulation conceptual model (SimCM): • Documents and details the explicit statement of assumptions and relationships to be included in the simulation model in accordance with the problem statement(Manuj et al., 2009) • A non-softwarespecific description of the simulation model that is to be developed, describing the objectives, inputs, outputs, content, assumptions and simplification of the model (Robinson, 2004; 2008) Uniqueness for simulationpurposes: • ‘process’to be followed – at the heart of this is setting the model boundary & level of detail (model content) • ‘output’– the description of the computer model to be built is as ‘simpleas possible’ (by drawing assumptions & simplifications) and is both credible & valid
Simulation conceptual modelling for SCM applications • Evaluating supply chain problems is important (Stewart, 1997); difficulty is that they are inherently complex and dynamic systems (e.g. Davies, 1993; Levy, 1994; Beamon, 1998) • Simulation is an approach that is often used for evaluating SC problems; extent of research is great (Weaver, 2010) • Creating a conceptual model is often regarded as the most important stage of a simulation project (Law, 1991); but little is written on the subject (Robinson, 2004b). • SimCM is a ripeareafor research (Robinson, 2006, 2010). Even in the SCM domain, Manuj et al., (2009) noted that further development in this area can improve the rigourof simulation studies • No methodologies existthat could guide a user through the creation of a conceptual model (Weaver, 2010).
The ‘idea’ behind the SCM2 Domain-specific SimCMprocedure for SCM applications Incorporate existing SimCM guidance from the literature Embed domain-knowledge in the form of a process reference model + = Provide inputs to the process of SimCM (i.e. setting the model boundary & detail) and standard descriptions? Incorporate a general process, general principles, methods for validation, advice on simplification?
Relationship between the requirements and outline design for SCM2
Phase 2: Determine how each objective is to be measured Output: Description of the processes that provide data used to calculate each objective Phase 1: Describe the supply problem Output: Description of the improvement(s) to be evaluated, for a given objective(s) within its supply setting Point of entry A formal problem formulation and structuring methodology or unstructured problem from client Phase 3: Determine how each improvement is to be represented Output: Description of the processes that represent each improvement Phase 6: Design the level of detail necessary to implement the model Output: Description of the model components and interconnections that represent the actual practices included in the model Phase 7: Document and validate the conceptual model Output: A valid description of the computer model to be developed Phase 4: Determine how the inputs and their sources interconnect within the model and with its immediate supply setting Output: List of inputs and candidate processes for possible inclusion in the model boundary Phase 5: Formulate the modelboundary Output: List of processes and inputs included in the model Build a prototype and use sensitivity analysis to extend the model boundary and level of detail Output: Refinement of the model boundary and level of detail Iterate for each PROMOTED process decided in phase five
Experimental situation:CoffeePot Case • Q: Where and how to cost effectively manufacture products in a global and complex supply setting? • Efficientmanufacturing scenario in a low-cost area with either shipments made in: • (S1) small(by air) • (S2) largequantities (by road and ship). Further detail on the CoffeePot case: Taylor, G. D., Love, D. M., Weaver, M. W., & Stone, J. (2009). Determining inventory service support levels in multinational companies. International Journal of Production Economics, Vol. 116 No. 1, pp. 1–11.
Phase one:Describe the supply problem Output: The supply problem is described from the perspective of the client Key Concept 1:Embedding SCOR in a generic procedure for simulation conceptual modelling can aid in the description of a problem from the perspective of the client using standard terminology and domain-specific process detail
Phase two:Determine how each objective is to be measured Output: The objective is described in terms of how it will be measured The supply chain performance metric is described using the hierarchy of metrics presented by SCOR at three different levels. Key concept 2:Embedding SCOR in a generic procedure for simulation conceptual modelling can aid in determining how an objective can be measured using standard descriptions of typical performance attributes and metrics; plus data collection needs from associated business processes at different levels of detail
Phase three:Determine how each improvement is to be represented Output: The improvement is described in terms of how it is to be represented Key Concept 3:Embedding SCOR in a generic procedure for simulation conceptual modelling can aid in determining how each improvement can be represented by business processes to implement each improvement at different levels of detail
Phase four:Determine how the inputs and their sources interconnect within the model and with its immediate supply setting Output: Provide a list of model inputs and candidate process elements (NB supplies information only to formulate the model boundary) Key Concepts 4 and 5: Embedding SCOR in a generic procedure for simulation conceptual modelling can aid in determining the model boundary by providing information on the relationshipsbetween business processes (i.e. interconnections between inputs and outputs germane to each process element)
Key concept 4:Identification of core process elements and their inputs generated from a source process element Description of the supply problem Description of how each objective is to be measured e.g.S1.4 (WH), D1.8 (WH), D1.3 (WH), D1.12 (WH), D1.13 (WH)) Example of SCOR inputs and outputs to a decomposed business process Source: SCOR V.9 (2008) Description of each improvement to be represented e.g. D2.10 (F), D2.11 (F), D2.12 (F), D2.13 (F)
Phase four:Determine how the inputs and their sources interconnect within the model and with its immediate supply setting Output: Provide a list of model inputs and candidate process elements (NB supplies information only to formulate the model boundary) Key concept 5:Process elements that have yet to be included in the model can be classed as ‘candidates’ for possible inclusion Does the source process element (that generates each input to be fed) exist as a CORE or PROMOTEDprocess element?
Phase five:Formulate the model boundary Output: Provide a list of processes and inputs included in the model Key concept 6: Decision rules can be used to consider which business processes to include within the model boundary from identifying the critical relationships between (core processes) and within the setting (real world) of the processes that are associated with each objective and improvement Simplify – Promote – Test - Exclude Rule 1: Will the input to be generated from the candidate process element effect model behaviour by significantly impacting on the objectives of study?
Phase five:Formulate the model boundary Output: Provide a list of processes and inputs included in the model Decision rules can be embedded in a generic procedure to simplify inputs to the model and to determine when no further processes should be included in the scope of the model (i.e. model boundary is set) Simplify – Promote – Test - Exclude Key Concept 7:Included process elements are considered in turn to identify those that could be simplified Rule 2: Can the input be generated in a simplifiedform(i.e. a random distribution or fixed value), so that there are no further inputs to the process?
Embedding SCOR in a generic procedure for SimCM can (not in the scope of presentation): • Key concepts 1 – 5:Aid in providing clear domain-specific guidelines for extracting information from a pre-defined process reference model and when necessary focus consultation with people who are knowledgeable about the system being represented • Key concept 8 & 9:Aid in focusing consultation with people who are knowledgeable about the system being represented to determine the detail of the actual practice that needs to be included from the descriptions provided for each process element included in the model boundary and simplified inputs Example of SCOR inputs and outputs to a decomposed business process Source: SCOR V.9 (2008)
Summary & implications for further work Domain-specific SimCMprocedure for SCM applications Incorporate existing SimCM guidance in the literature Embed domain-knowledge in the form of a process reference model + = • Develop a web-based application that can automate a number of the steps • Further refinement and validationof the SCM2 • Feasibility & utility with a range of process reference models in different industrial contexts