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 Adaptive and data-driven forecasting and inventory control allows companies to recognize changing demand conditions and mitigate the risk of costly demand estimation errors by (i) tracking the performance of forecasting methods available to the manager and (ii) relying on historical data as much as possible without making assumptions on the underlying probability distributions. It has emerged as an important tool to help companies satisfy customer demand in fast-changing environments. This technical report discusses the challenges related to traditional demand management techniques, motivates the relevance of the data-driven approach to address these issues, and provides a high-level description of the solution tools implemented in this project.
CELDi researchers at Virginia Tech will collaborate with Special Education teachers in the Montgomery County Public School (MCPS) System to redesign a task in an existing distribution center so that it can be effectively completed by people with special needs. The research team will include Dr. Kimberly Ellis, Dr. Tonya Smith-Jackson, and two special education teachers from middle schools in MCPS.
The research team will visit the Walgreens Distribution Center in Anderson, SC, to learn more about their inclusive environment. The research team will then visit another distribution center (either the UPS distribution center in Roanoke, VA, or the Target Distribution Center in Stuarts Draft, VA) to select a task for redesign.
The team will employ the Systems Engineering Design Process (Blanchard and Fabrycky, 2007) to determine the requirements for the selected task from both the operational perspective as well as the operator perspective. Based on the requirements, the team will conduct preliminary system design and detailed design while incorporating accessibility and universal design guidelines. The team will then implement the design (either in the laboratory or at the facility) and assess the effectiveness and accessibility of the redesign.