Search

• We study the impact of supply information on an inventory system under disruptions. • We analyze two advance information regimes: full and partial delivery information. • We provide exact long-run average cost models for both regimes under a multi-mode extensions of (S, T) policy. • For both regimes, we derive closed form optimal EOQ type solutions for the policy variables. • Comparisons show the importance of advance delivery information for effective planning. 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. [ABSTRACT FROM AUTHOR]

• An (r, Q) inventory policy with deterministic demand and defective items is proposed. • Two formulations for the percentage of defective items are considered. • Full inspection of lot size is conducted • The non-planned shortages during inspection period are modeled. • Properties are established for the optimal problem solution. In this paper an (r , Q) stock policy is used to control an inventory system with deterministic demand and defective items. The fraction of defective items in a batch is a random variable whose variance maybe dependent and independent of the order quantity. Considering the numerous benefits of selling high quality items (noting the vital importance for firms like medical device companies), a full inspection of the order quantity is conducted. Although full inspection reduces returns, ensures high quality products and improves customer satisfaction, it requires a substantial inspection time. Consequently, during this time a non-zero probability of undesirable (unplanned) shortages exists. The dominant suggestion in the literature, is to set the reorder level equal to the total demand during inpection period. This paper suggests an alternative reorder level to prevent shortages during this period and to formulate the backlogging cost in the case that unplanned shortages are permitted. Then, theoretical results ensure the determination of the reorder level and the order quantity that minimize the average total cost of the inventory system. Comparisons, through numerical examples and simulation, demonstrate that the proposed model leads to significant reduction both in cost and probability of shortages (if allowed), in relation to other existing models. [ABSTRACT FROM AUTHOR]