Kai Hoberg is Associate Professor of Supply Chain and Operations Strategy at Kühne Logistics University since 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, e.g. S.C. Johnson Graduate School of Management at Cornell University, Israel Institute of Technology, and NUS Business School at National University of Singapore. Kai Hoberg earned a Diplom Degree in Industrial Engineering at Paderborn University, Germany and Monash University, Melbourne.
Kai Hoberg’s current research topics include empirical supply chain management, 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. His research findings have been published in academic journals like IIE Transactions or European Journal of Operational Research. Besides research, Kai Hoberg is very enthusiastic about teaching supply chain management applying new teaching concepts.
Before returning to academia, Kai Hoberg worked as a strategy consultant and project manager for Booz & Company from 2006 to 2010. He conducted supply chain and operations management projects for numerous clients, in particular in consumer, chemicals and discrete manufacturing industries. Currently, he is active as faculty for executive supply chain education for global firms.
Steinker, Sebastian, Mario Pesch and Kai Hoberg (2016):Inventory Management under Financial Distress: An Empirical Analysis, International Journal of Production Research, 54(17): 5182-5207.
Abstract: This study analyses inventory reductions as a means of short-term financing of firms under financial distress. We use quarterly panel data of U.S. manufacturing firms for the period from 1995 to 2007. We identify a sample of 198 distressed firms for which we analyse changes in relative inventory. Approximately 70% of distressed firms reduce their inventories until the end of their individual distress periods. This decrease corresponds to a mean reduction of 18.7 inventory days or 9.4%. Additional regression analyses show that differences in inventory adjustments depend on pre-distress inventory performance, firm size, and turnaround strategy. We also compile a sample of 142 firms that defaulted to analyse inventory actions of unsuccessful turnarounds. Our findings indicate that defaulting firms also reduce their inventories but that the reductions are lower than those of firms that resolve their financial distress. We conclude that distressed firms use short-term inventory adjustments to free up cash and to achieve long-term efficiency gains from inventory optimisation. Our findings suggest that inventory optimisation is an essential part of a complete and successful turnaround strategy and financially distressed firms should always consider this action as a means to prevent bankruptcy.
Hoberg, Kai and Ulrich W. Thonemann (2015):Analyzing Variability, Cost, and Responsiveness of Base-Stock Inventory Policies with Linear Control Theory, IIE Transactions, 47(8): 865-879.
Abstract: The effect of inventory policies on order variability has been analyzed extensively. Two popular means of reducing order variability are demand smoothing and order smoothing. If the objective is minimizing demand variability, demands and orders can be heavily smoothed, resulting in an inventory policy that orders equal amounts in each time period. Such a policy obviously minimizes order variability, but it leads to high cost and low responsiveness of the inventory system. To optimize the overall performance of an inventory system, the effect of the inventory policy on all relevant dimensions of operational performance must be analyzed. We address this issue and analyze the effect of the parameter values of an inventory policy on three main dimensions of operational performance: Order variability, expected cost, and responsiveness. The inventory policy we use is the partial correction policy, a policy that can be used to smooth demand and to smooth orders. To analyze this policy, we use linear control theory. We derive the transfer function of the policy and prove the stability of the inventory system under this policy. Then, we determine the effect of the policy parameters on order variability, cost, and responsiveness and discuss how good parameter values can be chosen.
Hoberg, Kai and Ulrich W. Thonemann (2014):Modeling and analyzing information delays in supply chains using transfer functions, International Journal of Production Economics, 156: 132-145.
Abstract: Advanced inventory policies require timely system-wide information on inventories and customer demand to accurately control the entire supply chain. However, the presence of unsynchronized processes, processing lags or inadequate communication structures hinder the widespread availability of real-time information. Therefore, inventory systems often have to deal with obsolete data which can seriously harm the overall supply chain performance. In this paper, we apply transfer function methods to analyze the effect of information delays on the performance of supply chains. We expose the common echelon-stock policy to information delays and determine to what extent a delay in inventory information and point-of-sale data deteriorates the inventory policies׳ performance. We compare the performance of this policy with the performance of an installation-stock policy that is independent of information delays since it only requires local information. We find that this simple policy should be preferred in certain settings compared to relying on a complex policy with outdated system-wide information. We derive an echelon-stock policy that compensates for information delays and show that its performance improves significantly in their presence. We note potential applications of the approach in service parts supply chains, the hardwood supply chain, and in fast moving consumer goods settings.
Steinker, Sebastian and Kai Hoberg (2013):The impact of inventory dynamics on long-term stock returns – An empirical investigation of U.S. manufacturing companies, Journal of Operations Management, 31(5): 250-261.
Abstract: This paper investigates the relationship between the inventory dynamics and long-term stock returns of a large panel of U.S. manufacturing firms over the time period from 1991 to 2010. We propose two measures of inventory dynamics: one metric to assess the fluctuations of quarterly inventories within the year and a second metric to quantify relative year-over-year inventory growth. Our results indicate that within-year inventory volatility (IV) and abnormal year-over-year inventory growth (ABI) are associated with abnormal stock returns. Both metrics cannot be entirely explained by common risk factors. We find that firms with high IV and low ABI have the best long-term stock returns, and that stock performance decreases monotonically with higher ABI values. Our results are robust to various control variables including size, book-to-market value, industry and prior performance. We therefore conclude that changes in inventory levels provide valuable insights into the risks and opportunities faced by a company.
Hoberg, Kai, James R. Bradley and Ulrich W. Thonemann (2007):Analyzing the effect of the inventory policy on order and inventory variability with linear control theory, European Journal of Operational Research, 176(3): 1620-1642.
Teaching at KLU
Associate Professor of Supply Chain and Operations Strategy at Kühne Logistics University, Hamburg, Germany
Visiting scholar at the NUS Business School, National University of Singapore, Singapore (Host: Professor Chung Piaw Teo)
|2010 - 2012|
Assistant Professor for Supply Chain Management at University of Cologne, Germany
|2006 - 2010|
Project manager and strategy consultant at Booz & Company (formerly Booz Allen Hamilton) in the European Operations team with functional focus on supply chain and operations management
|2005 - 2006|
Research and teaching assistant at the Seminar for Supply Chain Management and Management Science, University of Cologne (Professor Ulrich W. Thonemann)
Visiting scholar at the School of Industrial Engineering and Management, Israel Institute of Technology, Haifa, Israel (Host: Professor Yale T. Herer)
Visiting scholar at the S. C. Johnson Graduate School of Management, Cornell University, Ithaca, New York (Host: Professor James R. Bradley)
|2001 - 2005|
Research and teaching assistant at the Institute of Supply Chain Management, Westfälische Wilhelms-Universität Münster (Professor Ulrich W. Thonemann)
|2000 - 2001|
Students research assistant at the Institute for Production Management, Paderborn University (Professor Otto Rosenberg)
Dr. rer. pol. at Westfälische Wilhelms-Universität Münster „Analyzing the Fundamental Performance of Supply Chains: A Linear Control Theoretic Approach”, Co-Chairs: Professor Ulrich Thonemann and Professor Jörg Becker
Dipl.-Wirt. Ing. in Industrial Engineering at Paderborn University with majors in operations research, production management and electrical engineering, Diplom thesis “Practical Model Formulations and Solutions in Detailed Facility Layout Planning“”
Project Experience (Selection)
- Supply chain strategy definition for consumer goods division in the chemical industry
- Supply chain strategy definition for aircraft component manufacturer
- Operations strategy definition for recycling machine manufacturer
- Working capital reduction for global steel manufacturer
- Operations segmentation for pharmaceutical custom manufacturer
- Setup of European logistics footprint for consumer goods manufacturer
- Organizational re-alignment of supply-side departments for consumer goods manufacturer
- Operations model for sourcing joint venture of two global consumer goods companies
- Optimization of promotion-related supply chain processes for department store
- Development of a supply chain analysis tool for a global technology enterprise
Supply Chain Learning
What we have to learn to do, we learn by doing – as Aristotle pointed out almost 2,400 years ago, learning is about gaining experience. To manage future supply chains, students need to acquire knowledge in numerous fields from mathematical modeling to negotiation skills. However, students need to learn fast in order to keep pace with the constantly accelerating complexity of our supply chains. Different learning styles are available to teach students those supply chain concepts that can make the difference between failure and success. A teaching method that has proven very effective is experiential learning: students learn directly from their own experience.
A classic experiential learning in supply chain management has been around for many years: MIT’s beer game. In a fascinating simple and concise way, generations of students have played the beer game to understand the supply chain dynamics that trigger the bullwhip effect. Departing from the classic beer game many extensions in experiential learning for supply chain management have been made. However, the simplicity and frugality of the beer game has often been lost when students were required to read through thick manuals and spend days to prepare and conduct games.
At Kühne Logistics University and University of Cologne, Prof. Dr. Kai Hoberg has worked on developing experiential learning games for teaching supply chain management. He focuses on simplicity while carving out the core learning objective. Certain games are played by the entire class whereas other games are performed by a group of students that is observed and evaluated by the class. In other settings, students conduct role plays to highlight problems that are further analyzed. The range of topics spans from very strategic issues around supply chain design or supply chain finance to very operational issues in warehousing. Here is a selection of games that provides an overview on different experiential learning approaches:
- Postponement: Students manage a fashion supply chain and learn how postponement and design-for-supply-chain can be beneficial in settings with long lead times and high demand uncertainty.
- Warehouse Picking: Students observe warehouse operations of few students who are picking parts for distribution. Different picking schemas are compared, performance is observed and aligned picking schemas are developed.
- Service Level Alignment: Students observe discussions between sales managers and supply chain planners and analyze data to realize that the service level definition that is applied in the firm does not reflect customer requirements.
We are happy to provide you with more information as required. Please feel free to contact Kai Hoberg for materials or discussions on experiential supply chain management learning.