A Theory of Supply Chains

This lecture will discuss the stability supply chains, focusing on (autonomous) control policies that use only customer information from downstream neighbors. An important property of autonomous policies/algorithms is the "gain", which relates marginal changes in the average steady state inventory to small changes in the steady state demand rate. The lecture will show that any autonomous algorithm with positive gain is unstable if it does not use information from the future. This is the reason for the "bullwhip" effect. The lecture will also discuss a family of autonomous algorithms that are stable under any type of demand. The algorithms handle future demand in a special way, and can dynamically maintain any desired inventory level for any demand rate even if the gain is variable and the demand is heterogeneous. Just-in-time strategies are a special case. If time permits we will also present cost-performance results, and extensions to multi-commodity supply networks.

Carlos F. Daganzo is professor of civil and environmental engineering at the University of California, Berkeley. A past Associate Editor of Transportation Science, he is now Convenor-Elect of the ISTTT International Advisory Committee and an Associate Editor of Transportation Research (part B, methodological). Noted for his contributions to econometrics, logistics, port operations, network theory and traffic flow, Daganzo has authored three books, "Multinomial Probit: The Theory and its Application to Demand Forecasting" (Academic Press, 1979), "Logistics Systems Analysis" (1st and 2nd eds, Springer-Verlag, 1991, 1996), and "Fundamentals of Transportation and Traffic Operations" (Pergamon-Elsevier, 1997).