Scalable Enterprise Systems: Designing and Managing Dynamic Supply Chains Using Model-on-Demand Predictive Control

Arizona State University, Scottsdale AZ

Investigators

Abstract

This grant provides funding for the development and evaluation of model predictive control methods for integrating entities in supply chains where business and production conditions vary significantly over time. Increasingly, companies find that they must reconfigure their supply chain structure, policies, and operating conditions to maintain or improve their performance in dynamic, real-time environments. In this research, a Model-on-Demand Predictive Control approach will provide a closed-loop methodology for integrating supply chain decisions that generates desirable system behaviors for the enterprise using both feedback and feedforward control action on the dynamical system. In addition, the model predictive control approach will provide the ability to evaluate the economic benefits of heterogeneous supply structures and varying degrees of centralization and information sharing, and the impact of time-varying inputs and outputs on system performance. Using a series of simulation experiments, the Model-on-Demand Predictive Control approach will be compared with the performance of Materials Requirement Planning logic with a rolling horizon implementation. Measures of performance evaluated in this study include inventory, customer service, responsiveness, and schedule stability. If successful, the model predictive control approaches developed through this research will provide benefits that include: (1) improved understanding of the value of real-time, feedback controlled information across heterogeneous organizations in supply chains, (2) demonstration of the value of an integrated modeling approach that uses operations research, data warehousing, estimation procedures, and process control in a distributed decision making environment, (3) managerial insights on designing and operating dynamic supply chains for improved performance through increased coordination and information sharing, (4) advancement of the Internet as the enabling technology for closed-loop decision making across organizational boundaries, and (5) foundation for future research on supply chain integration using dynamic control approaches in real-time environments such as those found on the Internet including supply web enterprises and business-to-business marketplaces.