Supply Chain Analytics

• Explain fundamental terminology and concepts in supply chain management and Supply Chain Analytics (SCA)
• Describe the data sources that provide the input needed for different SCA tools and interpret the output provided by SCA tools
• Analyze supply chain planning and design problems within an SCA framework
• Assess the applicability of SCA in different situations
• Apply and compare SCA tools to solve supply chain planning and design problems
• Develop unified solution approaches to given supply chain problems by appropriately combining different SCA tools
• Discuss the impact SCA can have on the performance of supply chains
• Describe how SCA can be used to gather important managerial insights about improving supply chain performance

This course will introduce students to complex networks consisting of suppliers, manufacturers, distributors, retailers, etc., that collaborate in bringing a product or service to the customer. It will be shown how activities within supply networks can be coordinated and integrated using SCA in order to reduce systemwide costs and improve customer service. An end-to-end view of supply chain management is therefore taken. On the customer-facing side, students will use SCA for demand forecasting and order fulfillment. On the internal side, SCA will be used for network design, inventory management and production planning. Finally, on the supply side, SCA will be used for supplier selection, supply contract design, outsourcing and vertical/horizontal collaboration. The SCA tools used will be drawn from a wide range of analytical domains, including probability theory, statistics, machine learning, mathematical programming and game theory.

42101/02402/02403/42402/42587, (or similar introductory courses in OR, statistics and SCM). It is expected that participants have some basic programming skills in languages such as R, Python, Matlab, Julia, or similar.

Lectures, exercises and project work