| This thesis proposes a multi-agent information system (MAIS) approach improve the order fulfillment process (OFP) in supply chain networks (SCNs). An order fulfillment process starts with receiving orders from customers and ends with delivery of finished goods. The order fulfillment process is complex because it is composed of several activities, executed by different functional entities, and is heavily interdependent between tasks, resources and agents involved in the process. A supply chain network is a network of autonomous or semi-autonomous business entities that are involved, through upstream and downstream linkages, in the different process and activities that produce goods or services to customers. As manufacturing practice is shifting toward the out-sourcing paradigm, the OFP is more likely to be executed throughout SCNs. It becomes imperative to integrate the OFP into SCNs to improve the OFP. Generalizing from the variety and complexity of SCNs, this study identifies several main types of SCN structures, and addresses OFP issues based on these SCNs.; I propose a multi-agent information system (MAIS) methodology for reengineering the OFP in SCNs. Agents in the MAIS are distributed, autonomous, self-organized, interdependent, and adaptive. These properties make it suitable for modeling the OFP in a SCN-based enterprise. The MAIS serves two purposes: (1) to model the OFP in SCNs, and (2) to evaluate OFP performance by applying the proposed strategies. The objective of reengineering the OFP is to achieve agility of the process in terms of efficiency, flexibility, robustness and adaptability. A multi-agent simulation platform, called Swarm, is enhanced and applied for modeling the MAIS, and experiments are conducted to simulate the OFP in SCNs in a multi-agent environment.; The OFP in SCNs can be improved in various dimensions, such as OFP operations, SCN structures, the information infrastructure, and other related processes. Based on the Swarm simulation platform, I model the OFP in SCNs, simulate the OFP, and then evaluate the potential OFP improvement strategies to identify useful strategies for improving the OFP. The strategies I evaluated include (1) coordinating demand management policies, (2) information sharing strategies, (3) synchronizing material and capacity availability, (4) dynamic resource allocation, and (5) the combination of various strategies. These strategies are applied to a generic SCN to understand dynamic of the OFP affected by different strategies under various business environments. The results also shed light on identifying the main effects of various strategies on OFP performance. I also evaluated OFP performance using various strategies in three different SCNs representing three typical SCNs. The insights of utilizing various strategies in different SCNs help reengineer the OFP in SCNs. |
