Authors: Kai Hoberg (Kühne Logistics University), Michael Becker-Peth (Rotterdam School of Management, Erasmus University Rotterdam)
The quality of any operational decision is utterly dependent on the quality of the input data that is available. However, data used for supply chain decision making is often inaccurate. In our study we focus on inventory management decisions that are subject to inaccurate inventory data. Due to shrinkage and loss, the available physical inventory can be lower than recorded system inventory. To handle this inventory inaccuracy a decision maker can decide to invest into cleaning the inventory data before placing an ordering decision. Alternatively, decision makers can deliberately decide to not clean the data, but to account for the inaccuracy by compensating the actual order decision. Depending on the cost of cleaning and the efficiency loss due to the compensation, decision makers have an incentive to either clean or compensate. We present a set of hypotheses on the cleaning-compensation trade-off and test these hypothesis in a laboratory setting.
Kai Hoberg is Head of Department of Operations and Technology and Professor of Supply Chain and Operations Strategy at Kühne Logistics University since November 2017. He joined the KLU as an Associate Professor in May 2012. From 2010 to 2012 he was Assistant Professor of Supply Chain Management at the University of Cologne. Kai Hoberg received his PhD in 2006 from Münster University, Germany under supervision of Prof. Dr. Ulrich W. Thonemann. In his academic career he was a visiting scholar at different top universities such as S.C. Johnson Graduate School of Management at Cornell University, Israel Institute of Technology, NUS Business School at National University of Singapore, Saïd Business School at the University of Oxford and the University of Stellenbosch. Kai Hoberg´s current research topics include supply chain analytics, the role of technology in supply chains, inventory modeling, and the link between operations and finance. In particular, he explores the fundamental drivers of supply chain performance and strategies applying real-world data.