OIM 7507: Supply Chain Analytics
3 credits
Traditionally, supply chains were seen as sources of cost that were necessary to achieve the goals of buying, selling, manufacturing, assembling, warehousing, transporting, and delivering goods. More recently, managers have come to understand the potential of the supply chain as a source of competitive advantage and expand efforts beyond cost minimization and into the realm of profit maximization and expanding market share. At the same time, supply chains have become increasingly large and complex with the potential for thousands of suppliers, manufacturers, distributms, and retailers to be included in the supply chain for a single product. This increase in supply chain size has coincided with exponential increases in the amount of data collected and maintained by firms and the computing power we have available to analyze it. Problems which were entirely intractable decades ago can now be solved at scale in a matter of minutes. As technology has increased the number of problems that can be solved at industrial scale, firms have shifted from primarily focusing on descriptive analytics to utilizing the power of predictive and prescriptive analytics to solve supply chain problems.
This course is designed to provide you with a broad introduction to the uses of prescriptive analytics to optimize common supply chain decisions associated with purchasing, manufacturing, distributing, and retailing goods and services. The focus will be on identifying areas of Supply Chain Management where optimization and simulation are helpful tools, selecting and implementing appropriate models given the context, and interpreting those models and their limitations. We will discuss a variety of decisions across the different functions of the supply chain and the role of optimization and data in making those decisions. Students will gain a foundational understanding of how analytics can be applied to Supply Chains, the currently available tools and software, and how to recognize common pitfalls or issues.
Prerequisites: None