Warranty Inventory Optimization with Advance Supply Information

In warranty inventory management, customers return allegedly malfunctioning products for replacement. Useful products may be recovered through testing and/or remanufacturing processes. The company must decide on the number of new units to purchase from a production line each period. This decision depends on an array of complex factors including stochastic demand rates, probabilistic yields from both the testing and remanufacturing processes, multiple sources of supply originating from both the stochastic reverse channel and the company's purchasing decisions, and varying levels of information regarding reverse pipeline inventory; we call this latter concept Advance Supply Information (ASI). In this paper we combine all of these elements to formulate a model that analyzes these tactical decisions and the value of ASI in this setting. We use dynamic programming to develop analytical models that determine the optimal ordering decisions under various levels of reverse channel visibility. The curse of dimensionality prohibits us from solving for optimal policies in all cases; thus, we develop heuristic dynamic programs using an aggregated state space that allow for tractable models while incorporating information gained from the pipeline visibility