Making Better Decisions in the Supply Chain

What is the potential of supply chain analytics for logistics and supply chain management and how can data driven approaches improve decision making? Professors Kai Hoberg (KLU) and Wolfgang Kersten (TU Hamburg), both members of the scientific advisory council of BVL – The Global Supply Chain Network, dedicated a workshop to answering these questions. Invited by BVL’s Hamburg chapter, 85 participants enjoyed a mix of best-practice presentations and working discussions at KLU on February 21, 2019.

“It was a great event!” one of the participants remarked. “The way it was presented really made you think further about the topic for yourself.” In the first part of the event, experts from both research and practice shared their insights: Professor Sandra Transchel (KLU) provided an overview of supply chain analytics methodologies. She explained the differences between the different approaches and outlined the journey from descriptive analytics to prescriptive analytics. Christoph Lieth (German Federal Association of Artifical Intelligence) highlighted how data can be used for optimizing operations and highlighted why classic AI and machine learning approaches which require a lot of data cannot easily be deployed in manufacturing settings. Lennart Kirstein (Otto GmbH & Co KG) explained how retailers can use big data and AI to improve their services to customers. In a case study, he outlined how Otto used predictions to order goods in anticipation of customer demand, thereby cutting the efficient lead times to the customer.

Folllowing the presentations, participants discussed how supply chain analytics can be used in their companies in four breakout sessions. Professor Kai Hoberg was very excited by the depth of the discussions: “In my breakout session, we had very lively discussions about data quality and how to setup processes and incentives to ensure high data quality. This is an important pre-requisite to implement supply chain analytics”.