EOQ with supply disruptions under different advance information regimes

Abstract

In this paper, we consider the impact of advanced delivery information on the performance of supply chains facing endogenous disruptions. We study a single-echelon EOQ-based inventory model with backorders where the supplier could fail to deliver shipments on time following a Bernoulli process, a measure of the supplier service quality. Two extreme advance information regimes are analyzed: (1) full delivery information (FDI), where the timing of the next delivery is always known, and (2) partial delivery information (PDI), where the timing of the next delivery is known if it coincides with the next planned delivery. For both regimes, we use a natural adaptation of the base-stock policy, having multiple order up-to levels, each corresponding to the level of available information when ordering. We then model the long-run average cost and determine the policy variables that minimize this cost. While the analysis of the FDI model proved straightforward, the PDI model involved the solution of a mixed-integer nonlinear optimization problem. We found closed-form solutions for both models. Numerical results show that, by reducing planning uncertainty, advanced information directly reduces operating costs. They also show that the cost of the case of full information is higher than that of the EOQ model, suggesting the importance of finding reliable suppliers for disruption-free operations.