Date and Time:
Inventory Control in Assemble-to-Order Systems with Identical Lead Times: Lower bound, Control Policies, and Asymptotic Analysis
Assemble-to-order (ATO) is a widely-adopted supply-chain strategy to facilitate product variety, mitigate demand forecasting error, and enhance the overall efficiency of a manufacturing process. A general ATO inventory system serves demands for multiple products, which are assembled from different and overlapping components according to a fixed Bill of Material. Inventories are kept at component level. Component supplies are not subject to capacity constraints but involve positive replenishment lead times. The inventory manager controls the system by deciding how many components of each type to order and which product demands to serve. The two decisions are intertwined with each other and are made continuously (or periodically) over an infinite time horizon. The objective is to minimize the long-run average expected inventory cost, which includes both the cost of backlogging demands and the cost of holding component inventory. Developing an optimal control policy for such systems is known to be difficult, and past works have focused on particular, sub-optimal policy types and/or systems with special structures and restrictive parameter values. In this talk, I will present a new approach that uses stochastic program (SP) as a proxy model to set a lower bound on the inventory cost and to define dynamic inventory control policies. I will describe the application of this approach to an important special case, ATO inventory systems with identical component lead times, and present an asymptotic analysis that proves that our approach is optimal on the diffusion scale, i.e., as the lead time extends, the percentage difference of the long-run average inventory cost under our policies from its lower bound converges to zero.
Qiong Wang is an associate professor at Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign. Before joining UIUC, he had worked at Alcatel-Lucent Bell Labs as a Member of Technical Staff for more than ten years. His research focuses on analyzing manufacturing, service, and networking systems, and develops inventory control, revenue management, and pricing strategies. His work has appeared in Management Science, Operations Research, and other top journals. Professor Wang received his Ph. D in Engineering and Public Policy from Carnegie-Mellon University.