Supply Chain Simulation

Supply Chain Simulation

Nenad Stefanovic, Dusan Stefanovic
Copyright: © 2015 |Pages: 12
DOI: 10.4018/978-1-4666-5888-2.ch521
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The goal of simulation is to evaluate existing supply chain configurations, as well as to aid in design of the new supply chains. In other words, it helps resolve different supply chain management (SCM) problems which can be grouped into the following categories:

  • Infrastructure configuration that implies defining of the manufacturers, distribution centers, wholesalers, retailers and their locations (nodes).

  • Defining strategy related to processes at the nodes. For example, for ordering process – which policy will be used (depending on the demand characteristics models can be deterministic or stochastic), for reception process (i.e. devices for checking quantities, RFID or bar code readers for identification, inspection methods, etc.), or distribution process (successive or group delivery, instant or postponed order fulfillment, etc.)

  • Coordination between processes and activities with the purpose of their alignment and fulfillment of performance goals on global supply chain level.

  • Information integration so that processes can exchange all necessary information.

  • Supply chain validation through performance measurement which involves defining metrics at different supply chain levels.

There are different supply chain modeling methods and types of simulation. In this chapter, methodology and software solution based on the discrete-event simulation are presented. The background section gives definitions and explanations of key terms and concepts, as well as literature review with main contributions related to supply chain simulation. The main section of the chapter describes original supply chain modeling approach which enables flexible modeling of any supply chain configuration. It also describes the main components of the software solution such as model database, process library, knowledge base, and execution engine.

The main contributions and benefits of the presented simulation solution are presented. Finally, the main future research directions and opportunities are examined.



Simulation can be defined as the process of designing an abstract model of a real system (or subsystem) and conducting experiments with this model for the purpose of either understanding system behavior or evaluating various strategies within the limits imposed by a set of criteria for the operating of the system (Shannon, 1975).

By examining well designed simulation models, organizations can reinforce their decisions regarding supply chain processes. They can study and analyze effects of different supply chain initiatives and improvement programs through sensitivity analysis (such as what-if or goal seek) before investing huge amount of money or disrupting their operations.

Computer simulation and simulation models can be used to model intricate supply networks close to real systems, execute those models, and observe system behavior.

The main advantages of the supply network computer simulation are (Stefanovic et al., 2009):

  • The simulation is relatively clear and flexible.

  • It can be used for analysis of the complex real systems such as supply networks.

  • With the simulation, it is possible to include real-world influences, for example uncertainty factor in demand or lead time.

  • ‘‘Time compression” is possible. Effects of a certain business policy over a long period of time (months, years), can be obtained in a short time.

  • The simulation enables ‘‘what-if” analysis. Managers can test the results of different decisions.

  • The simulation does not interrupt real systems. For example, experimenting with different supply network configurations can be done without disruptions and significant investment.

  • With the simulation, the effects of the individual components, parameters and variables can be studied at the global level.

The main disadvantages of the supply network computer simulation can be summarized as:

Key Terms in this Chapter

Knowledge Base: A specialized database that serves as repository for information and knowledge management.

Metric: A measure of particular process characteristic.

Simulation: Simulation is defined as the process of creating a model of an existing or proposed system in order to identify and understand those factors which control the system and/or to predict the future behaviour of the system.

Supply Chain: Dynamic, interconnected and collaborative group of companies working jointly on planning, management and execution of cross-company business processes spanning from the first tier suppliers to the end-customers.

Business Process: A business process is a complete set of end-to-end activities that together create value for the customer.

Metamodel: A conceptual model which defines concepts, relationships, and semantics and enables creation of concrete models.

Performance Measurement: A process for collecting and reporting information regarding the performance of an individual, group or organizations. It can involve looking at process/strategies in place, as well as whether outcomes are in line with what was intended or should have been achieved.

Model: A model is a simplified representation of a system at some particular point in time or space intended to promote understanding of the real system.

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