BA 550: Supply Chain Analytics (Graduate level)

Course Description:

Supply chain analytics is one of the fastest-growing business intelligence application areas. Every day supply chains create a vast amount of data. Every transaction, every operation, every claim is often recorded and saved as data. This course showcases real-life applications of data analytics in various fields of supply chain management, such as forecasting and inventory management, sales and operations planning, production, transportation, logistics and fulfillment, purchasing and supply management, etc. in manufacturing, trade and service industries. Students will learn real-life examples of how analytics can be applied to these domains of a supply chain to generate a significant social/economic impact. Topics include demand forecasting for new products, supply chain design, transportation analytics, sales and operations analytics in production, inventory management, simulation, and applying analytics to big data in supply chain management. Software packages such as Excel/Excel Solver and/or R will be utilized to help in solving models.

Student Learning Outcomes:

  • Use several common models of analysis, both quantitative and qualitative, to address problems and challenges within supply chains.
  • Apply predictive models to forecast product demand.
  • Explain the strategic importance of the supply chain and issues and opportunities in the supply chain.
  • Explain the metrics used to evaluate the performance of a supply chain.
  • Explain the different types of inventory, the challenges of inventory management, and the reasons for the existence of inventory.
  • Evaluate inventory turns and days-of-supply for different time periods
  • Explain and use the EOQ model for independent inventory demand.
  • Compute a reorder point and explain safety stock.
  • List the advantages and disadvantages of modeling with simulation.
  • Perform the five steps in a Monte Carlo simulation.
  • Use Excel spreadsheets to create a simulation.
  • Understand why analytics is critical to supply chain management and its financial/economic impact.
  • Develop an ability to solve supply chain models using Excel/Excel Solver and/or R.
Yajun Lu
Yajun Lu
Assistant Professor of Analytics & Operations Management

My research interests are in Network Optimization, Graph-based Data Mining, Data Analytics of Complex Networks with applications in Healthcare, and Social Network Analysis.