Detecting Changes and Avoiding Unwanted Behavior in Supply Chains

Full Citation
Rabelo, L., Helal, M., Lertpattarapong, C. 2004. Detecting changes and avoiding unwanted behavior in supply chains. PICMET’04 Symposium ; Portland International Center for Management of Engineering and Technology, July 31 - August. 4, 2004, Seoul, Korea

Abstract
The new forces of globalization, advances in web technologies, new methodologies/tools to integrate engineering and business functions, the developments in e-commerce and their complex interrelationships are shaping the competitive landscape of business. Organizations are looking for strategies to meet these challenges and take advantage of new opportunities. One of such strategies is supply chain management. This paper introduces a methodology to detect changes in supply chain behaviors due to endogenous and/or exogenous influences, and predict the impact of these changes in the short and long term horizons. System Dynamics is used to model the dynamic supply chain behavior. Neural Networks, which can be encapsulated in software agents, are utilized to detect the changes at a very early stage so that enterprises have enough time to respond and counter-effect any unwanted situations. Then, principles of stability and controllability are used to apply and make modifications to the information and materials flows to adapt to the changes and avoid the undesirable behavior. A case study of an actual electronics manufacturing company is used to demonstrate the methodology.
Keywords: Supply chain, syytem dynamics, eigenvalue analysis