This paper presents a multiagent approach for modeling supply chain dynamics. The goal is to develop a flexible and reusable framework that enables rapid development of customized decision support tools for supply chain management. The framework is based on the analysis of three distinct supply chain domains: a vertically integrated global computer manufacturer, a less tightly coupled Japanese automotive supply chain, and an interorganizational U.S. grocery supply chain. These supply chains differ in terms of decision-making centers, heterogeneity, and supplier relationships. However, they share common processes that are modeled using a library of software components. The library consists of two main categories: structural elements (e.g., retailers, manufacturers, suppliers, transportation vehicles) and control elements (e.g., inventory policies, demand control, supply control, flow control, and information control). These components are used to model the production and transportation of products, as well as the control policies that govern product flow within the supply chain. The framework allows for the development of models that address issues related to configuration, coordination, and contracts. Configuration deals with the network structure of a supply chain based on factors such as lead time, transportation cost, and currency fluctuations. Coordination deals with routine activities in a supply chain such as materials flow, distribution, inventory control, and information exchange. Contracts control material flow over a longer horizon based on factors such as supplier reliability, number of suppliers, quantity discounts, demand forecast mechanisms, and flexibility to change commitments. Multiagent computational environments are suitable for studying coordination issues involving multiple autonomous or semiautonomous agents. The framework uses a multiagent approach, where structural elements are modeled as heterogeneous agents that utilize control elements to communicate and control the flow of products within the supply chain. The framework emphasizes models that capture the locality of individual supply chain entities, promoting simultaneous analysis of supply chain performance from various organizational perspectives. The framework reduces the effort involved in modeling various alternatives and measuring their performance through simulation under different assumptions about uncertainties. This enables decision makers to quantitatively assess the risk and benefits associated with various supply chain reengineering alternatives. The paper describes the framework in its current state and provides examples to demonstrate how issues relevant to supply chain management can be analyzed using it. A software application using some of the concepts from this framework has been developed at IBM. The paper is organized as follows: a literature overview, a description of the multiagent framework, a section on the supply chain library, a cross-docking prototype from the grocery chain industry, a full-scale application developed for IBM asset managers, and a conclusion.This paper presents a multiagent approach for modeling supply chain dynamics. The goal is to develop a flexible and reusable framework that enables rapid development of customized decision support tools for supply chain management. The framework is based on the analysis of three distinct supply chain domains: a vertically integrated global computer manufacturer, a less tightly coupled Japanese automotive supply chain, and an interorganizational U.S. grocery supply chain. These supply chains differ in terms of decision-making centers, heterogeneity, and supplier relationships. However, they share common processes that are modeled using a library of software components. The library consists of two main categories: structural elements (e.g., retailers, manufacturers, suppliers, transportation vehicles) and control elements (e.g., inventory policies, demand control, supply control, flow control, and information control). These components are used to model the production and transportation of products, as well as the control policies that govern product flow within the supply chain. The framework allows for the development of models that address issues related to configuration, coordination, and contracts. Configuration deals with the network structure of a supply chain based on factors such as lead time, transportation cost, and currency fluctuations. Coordination deals with routine activities in a supply chain such as materials flow, distribution, inventory control, and information exchange. Contracts control material flow over a longer horizon based on factors such as supplier reliability, number of suppliers, quantity discounts, demand forecast mechanisms, and flexibility to change commitments. Multiagent computational environments are suitable for studying coordination issues involving multiple autonomous or semiautonomous agents. The framework uses a multiagent approach, where structural elements are modeled as heterogeneous agents that utilize control elements to communicate and control the flow of products within the supply chain. The framework emphasizes models that capture the locality of individual supply chain entities, promoting simultaneous analysis of supply chain performance from various organizational perspectives. The framework reduces the effort involved in modeling various alternatives and measuring their performance through simulation under different assumptions about uncertainties. This enables decision makers to quantitatively assess the risk and benefits associated with various supply chain reengineering alternatives. The paper describes the framework in its current state and provides examples to demonstrate how issues relevant to supply chain management can be analyzed using it. A software application using some of the concepts from this framework has been developed at IBM. The paper is organized as follows: a literature overview, a description of the multiagent framework, a section on the supply chain library, a cross-docking prototype from the grocery chain industry, a full-scale application developed for IBM asset managers, and a conclusion.