Managing an Available-to-Promise Assembly System with Dynamic Short-Term Pseudo-Order Forecast

Long Gao · Susan H. Xu · Michael O. Ball

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Supply Chain and Inventory Management Management and Optimization Techniques Scheduling and Optimization Algorithms

Abstract

We study an order promising problem in a multiclass, available-to-promise (ATP) assembly system in the presence of pseudo orders. A pseudo order refers to a tentative customer order whose attributes, such as the likelihood of an actual order, order quantity, and confirmation timing, can change dynamically over time. A unit demand from any class is assembled from one manufactured unit and one inventory unit, where the manufactured unit takes one unit of capacity and needs a single period to produce. An accepted order must be filled before a positive delivery lead time. The underlying order acceptance decisions involve trade-offs between committing resources (production capacity and component inventory) to low-reward firm orders and reserving resources for high-reward orders. We develop a Markov chain model that captures the key characteristics of pseudo orders, including demand lumpiness, nonstationarity, and volatility. We then formulate a stochastic dynamic program for the ATP assembly system that embeds the Markov chain model as a short-term forecast for pseudo orders. We show that the optimal order acceptance policy is characterized by class prioritization, resource-imbalance-based rationing, and capacity-inventory-demand matching. In particular, the rationing level for each class is determined by a critical value that depends on the resource imbalance level, defined as the net difference between the production capacity and component inventory levels. Extensive numerical tests underscore the importance of the key properties of the optimal policy and provide operational and managerial insights on the value of the short-term demand forecast and the robustness of the optimal policy. This paper was accepted by Martin Lariviere, operations management.

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References

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