This PECASE award will show how to exploit real-time information with optimal dynamic control of pricing and production. Information technology is enabling firms to monitor consumer demand and production processes throughout the supply chain in real-time. The following, specific, pressing issues in estimation, dynamic control, and supply chain coordination will be addressed in this project: 1) How can a manufacturer use real-time information about orders and cancellations to estimate true demand and customers' sensitivity to delay, in spite of double bookings? 2) In an assemble-to-order system, what is an effective dynamic policy for pricing, leadtime quotation, sequencing orders for assembly, and control of component inventory? 3) Supply contracts are often renegotiated in response to changes in the business environment. How does this affect the structure of an optimal supply contract? 4) What is the effect of sharing real-time information on relational contracts (informal agreements sustained within a long-term relationship)? The analysis will involve a fusion of cooperative and non-cooperative game theory, statistics, stochastic processes, adaptive control, heavy traffic queueing theory, and dynamic programming. Results will be developed and disseminated through collaboration with managers in the Stanford Supply Chain Forum, Lucent, nthOrbit, Rapt, Lonza, etc., and tested with data from Cisco, Hewlett-Packard, and GM. PhD students in the Stanford Business School and Engineering School will be engaged in this process, and will become leaders in academia and industry. Research insights and learning about current business practice will be incorporated into teaching materials for MBA and PhD students, and published in academic journals. At present, very few firms are exercising real-time control of their business processes, but the potential for widespread productivity improvement is great (1). In a specific example, the automobile industry wastes $65-$80 billion per year building inventories of unwanted cars (2). Qualitative insights and quantitative methods for optimal dynamic control of an assemble-to-order system will help car companies to eliminate that waste. (1) Survey: Computers of the World Unite. The Economist, February 2002, S19-S20. (2) Agrawal, M., T.V. Kumaresh and G.A. Mercer. The False Promise of Mass Customization. McKinsey Quarterly 3, 2001, 62-71.

This project was originally funded as a CAREER award, and was converted to a Presidential Early Career Award for Engineers and Scientists (PECASE) award in September 2004.

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Stanford University
Palo Alto
United States
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