Lower bounds for multi-echelon stochastic inventory systems
Management Science
Inventory Management of Remanufacturable Products
Management Science
A Series System with Returns: Stationary Analysis
Operations Research
Optimal Policy for a Multiechelon Inventory System with Remanufacturing
Operations Research
Optimal Policy for a Multiechelon Inventory System with Remanufacturing
Operations Research
Managing an Assemble-to-Order System with Returns
Manufacturing & Service Operations Management
OR FORUM---The Evolution of Closed-Loop Supply Chain Research
Operations Research
Consumer Returns Policies and Supply Chain Performance
Manufacturing & Service Operations Management
Research on inventory management for muti-species systems with returns
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Optimal Control of Inventory Systems with Multiple Types of Remanufacturable Products
Manufacturing & Service Operations Management
Computers and Industrial Engineering
Component commonality in closed-loop manufacturing systems
Journal of Intelligent Manufacturing
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This paper considers an inventory system with an assembly structure. In addition to uncertain customer demands, the system experiences uncertain returns from customers. Some of the components in the returned products can be recovered and reused, and these units are returned to inventory. Returns complicate the structure of the system, so that the standard approach (based on reduction to an equivalent series system) no longer applies in general. We identify conditions on the item-recovery pattern and restrictions on the inventory policy under which an equivalent series system does exist. For the special case where only the end product (or all items used to assemble the end product) is recovered, we show that the system is equivalent to a series system with no policy restrictions. For the general case, we explain how and why the system becomes more problematic and propose two heuristic policies. The heuristics are easy to compute and practical to implement, and they perform well in numerical trials. Based on these numerical trials, we obtain insights into the impact of various factors on system performance. For example, we find that holding and backorder costs tend to increase when the average return rate, the variability of returns, or the number of components recovered increases. However, neither the product architecture nor the specific set of components being recovered seems to have a significant impact on these costs. Whether product recovery reduces total system costs depends on the magnitude of the additional holding and backorder costs relative to potential procurement cost savings.