A National Science Foundation Industry/University Cooperative Research Center (I/UCRC)
The overall project objective of this research is to investigate approaches to forecasting and inventory control that are (i) data-driven, i.e., dynamically integrate the experimental measurements in the decision-making framework, and (ii) adaptive, i.e., exploit information revealed over time, to reduce part stoc-kouts and provide CELDi partners with a framework well-suited to the information available in real-life logistics problems. Forecasting and optimization have traditionally been approached as two sequential components of inventory management: the random demand is first estimated based on historical data, and the forecast is then used as input to the optimization module. This method suffers from several limitations: (i) the best forecasting method might not be known a priori, and may vary depending on the product considered, (ii) the forecasting module does not take into account the over and underage costs of the inventory model, and instead penalizes over and under-predicting demand equally, although backorder costs are typically much higher than holding costs. This provides motivation for devising an operating strategy that builds upon the information revealed over time and remains flexible to integrate forecasting and inventory control in a framework that can be applied to large numbers of products. In preliminary work, the PI and Co-PI have demonstrated the potential of adaptive forecasting, and achieved cost reductions of up to 5% on test problems. To establish the relevance of the approach to industry, this project will be performed in collaboration with Lockheed Martin and FLSmidth, and is expected to benefit all CELDi members faced with inventory management challenges.