In this article, I describe the application of advanced analytics techniques to supply chain management. The applications are categorized in terms of descriptive, predictive, and prescriptive analytics and along the supply chain operations reference (SCOR) model domains plan, source, make, deliver, and return. Descriptive analytics applications center on the use of data from global positioning systems (GPSs), radio frequency identification (RFID) chips, and data-visualization tools to provide managers with real-time information regarding location and quantities of goods in the supply chain. Predictive analytics centers on demand forecasting at strategic, tactical, and operational levels, all of which drive the planning process in supply chains in terms of network design, capacity planning, production planning, and inventory management. Finally, prescriptive analytics focuses on the use of mathematical optimization and simulation techniques to provide decision-support tools built upon descriptive and predictive analytics models.

The flow of product returns is becoming a significant concern for manufacturers. Typically, these returns have been viewed as a nuisance, resulting in reverse supply chains that are designed to minimize costs. These minimum cost reverse supply chains often do not consider product return speed. The longer it takes to retrieve a returned product, the lower the chances that there are financially attractive reuse options. Unlike forward supply chains, design strategies for reverse supply chains are unexplored and largely undocumented. The most influential product characteristic for reverse supply chain design is the marginal value of time. Responsive reverse supply chains are the appropriate choice when the marginal value of time for products is high, and efficient reverse supply chains are the proper choice when the marginal value of time for products is low. Product returns and their reverse supply chains represent a potential value stream and deserve as much attention as forward supply chains.