Modeling supply chain dynamics: A multiagent approach

Decision Sciences, Summer 1998 by Swaminathan, Jayashankar M, Smith, Stephen F, Sadeh, Norman M

Modeling Supply Chain Dynamics: A Multiagent Approach*

ABSTRACT

A global economy and increase in customer expectations in terms of cost and services have put a premium on effective supply chain reengineering. It is essential to perform risk-benefit analysis of reengineering alternatives before making a final decision. Simulation provides an effective pragmatic approach to detailed analysis and evaluation of supply chain design and management alternatives. However, the utility of this methodology is hampered by the time and effort required to develop models with sufficient fidelity to the actual supply chain of interest. In this paper, we describe a supply chain modeling framework designed to overcome this difficulty. Using our approach, supply chain models are composed from software components that represent types of supply chain agents (e.g., retailers, manufacturers, transporters), their constituent control elements (e.g., inventory policy), and their interaction protocols (e.g., message types). The underlying library of supply chain modeling components has been derived from analysis of several different supply chains. It provides a reusable base of domain-specific primitives that enables rapid development of customized decision support tools.

Subject Areas: Artificial Intelligence, Decision Support System, Simulation, and Supply Chain Management.

INTRODUCTION

A supply chain can be defined as a network of autonomous or semiautonomous business entities collectively responsible for procurement, manufacturing and distribution activities associated with one or more families of related products (see Figure 1). Different entities in a supply chain operate subject to different sets of constraints and objectives. However, these entities are highly interdependent when it comes to improving performance of the supply chain in terms of objectives such as on-time delivery, quality assurance, and cost minimization. As a result, performance of any entity in a supply chain depends on the performance of others, and their willingness and ability to coordinate activities within the supply chain. A global economy and increase in customer expectations regarding cost and service have influenced manufacturers to strive to improve processes within their supply chains, often referred to as supply chain reengineering (Swaminathan, 1996). For example, Hewlett Packard's Vancouver division reduced inventory costs by approximately 18% for HP Deskjet printers through delayed product differentiation (Billington, 1994). Similarly, National Semiconductor has managed to reduce delivery time, increase sales, and reduce distribution cost through effective supply chain reengineering (Henkoff, 1994).

Supply chain reengineering efforts have the potential to impact performance in a big way. Often they are undertaken with only a probabilistic view of the future, and it is essential to perform a detailed risk analysis before adopting a new process. In addition, many times these reengineering efforts are made under politically and emotionally charged circumstances. As a result, decision support tools that can analyze various alternatives can be very useful in impartially quantifying gains and helping the organization make the right decision (Feigin, An, Connors, & Crawford, 1996). In most organizations, reengineering decisions are generally based on either qualitative analysis (such as benchmarking) or customized simulation analysis. This is because complex interactions between different entities and the multitiered structure of supply chains make it difficult to utilize closed-form analytical solutions. Benchmarking solutions provide insights into current trends but are not prescriptive. This leaves simulation as the only viable platform for detailed analysis for alternative solutions. However, there are two major problems associated with building customized simulation models: (1) they take a long time to develop and, (2) they are very specific and have limited reuse. Our aim in this paper is to provide a flexible and reusable modeling and simulation framework that enables rapid development of customized decision support tools for supply chain management.

It is essential to understand important issues (decision trade-off) and common processes in different types of supply chains to develop a generic, modular, and reusable framework. Our framework is based on supply chain studies conducted in three distinct domains: (1) a vertically integrated supply chain of a global computer manufacturer (Swaminathan, 1994); (2) a Japanese automotive supply chain that is less tightly coupled (Sabel, Kern, & Herrigel, 1989); and (3) an interorganizational supply chain in the U.S. grocery industry (ECR, 1993). These supply chains differ in terms of centers of decision making, heterogeneity in the supply chain, and relationship with suppliers. In the supply chain for the computer manufacturer we found that the decision-making process was centralized to a great extent, few suppliers were extremely important whereas others were mainly controlled by the manufacturer, and a major part of the supply chain was owned by the manufacturer. In the Japanese automotive supply chain, the manufacturer had a greater control over external suppliers and in some cases partially owned them. However, suppliers made independent decisions many times and the supply chain involved different companies, though all worked according to the guidelines set by the manufacturer. In the grocery supply chain, manufacturers and retailers were equally powerful and sometimes had conflicting interests. The decision making was decentralized and different organizations (operating under different industrial environments) were part of the same supply chain.