Kai Hoberg is Professor of Supply Chain and Operations Strategy at Kühne Logistics University since November 2017. From 2017 to 2023 he served as Department Head of the Operations and Technology Department. 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 earned a Diplom Degree in Industrial Engineering at Paderborn University, Germany and Monash University, Melbourne.
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. His research findings have been published in academic journals like Journal of Operations Management, Production and Operations Management 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.
Up Close & Personal
“What really sets KLU apart for me is its small set-up. We know our students, we know our colleagues, and we can easily connect to them.”
– Prof. Dr. Kai Hoberg
(2022): Evaluating Human Behaviour in Response to AI Recommendations for Judgemental Forecasting, European Journal of Operational Research, 303 (3): 1151-1167.
Abstract: Various advanced systems deploy artificial intelligence (AI) and machine learning (ML) to improve demand forecasting. Supply chain planners need to become familiar with these systems and trust them, considering real-world complexities and challenges the systems are exposed to. However, planners have the opportunity to intervene based on their experience or information that the systems may not capture. In this context, we study planners’ adjustments to AI-generated demand forecasts. We collect a large amount of data from a leading AI provider and a large European retailer. Our dataset contains 30 million forecasts at the SKU-store-day level for 2019, plus variables related to products, weather, and holidays. In our two-phase analysis, we aim to understand the adjustments made by planners and the quality of these adjustments. Within each phase, we first identify the drivers of adjustments and their quality using random forest, a well-known ML algorithm. Next, we investigate the collective effects of the different drivers on the occurrence and the quality of the adjustments using a decision tree approach. We find that product characteristics such as price, freshness, and discounts are important factors when making adjustments. Large positive adjustments occur more frequently but are often inaccurate, while large negative adjustments are generally more accurate but fewer in number. Thus, planners do not contribute to accuracy on average. Our findings provide insights for the better use of human knowledge in judgmental forecasting.
(2022): Realizing supply chain agility under time pressure: Ad hoc supply chains during the Covid-19 pandemic, Journal of Operations Management: .
Abstract: When the COVID-19 pandemic began in 2020, the medical product industry faced an unusual demand shock for personal protective equipment (PPE), including face masks, face shields, disinfectants, and gowns. Companies from various industries responded to the urgent need for these potentially life-saving products by adopting ad hoc supply chains in an exceptionally short time: They found new suppliers, developed the products, ramped-up production, and distributed to new customers within weeks or even days. We define these supply chains as ad hoc supply chains that are built for a specific need, an immediate need, and a time-limited need. By leveraging a unique sampling, we examined how companies realize supply chain agility when building ad hoc supply chains. We develop an emergent theoretical model that proposes dynamic capabilities to enable companies building ad hoc supply chains in response to a specific need, moderated by an entrepreneurial orientation allowing firms to leverage dynamic capabilities at short notice and a temporary orientation that increases a company's focus on exploiting the short-term opportunity of ad hoc supply chains.
(2022): Supplier Inventory Leanness and Financial Performance, Journal of Operations Management, 68 (4): 385-407.
Abstract: Numerous studies have examined the relationship between inventory management and financial performance. However, the focus of such empirical work has primarily been on how a firm's own inventory characteristics affect its performance. Our objective is to extend this body of literature beyond the firm-level. We draw on inventory theory and resource-based theories to hypothesize about the effect of supplier inventory leanness on a focal firm's financial performance and how supplier and focal firm inventory leanness interact to affect such outcomes. We test our hypotheses using a large panel dataset of supplier-focal firm relationships obtained from Compustat's Customer Segment database and aggregated to the focal firm-quarter level, as well as firm financial information from Compustat's Fundamentals Quarterly database. The econometric analyses provide evidence that supplier inventory leanness influences focal firm financial performance indirectly through the interaction with the firm's own inventory leanness. In particular, our estimation results detail how supplier inventory leanness affects the non-linearity of the focal firm's inventory leanness-financial performance relationship and its optimal inventory leanness level. The findings broaden the scope of empirical inventory literature and highlight supplier inventory leanness as an important consideration in firm-level inventory decision making.
(2021): Designing smart replenishment systems: Internet-of-Things technology for vendor-managed inventory at end consumers, European Journal of Operational Research, 295 (3): 949-964.
Abstract: Motivated by recent advances in Internet-of-Things (IoT) technology for household appliances, we analyze a Smart Replenishment system that leverages point-of-consumption (POC) information at end consumers to decide on deliveries of consumables. As such, we extend the classic Vendor-Managed Inventory (VMI) concept to end consumers. We model the system for a single manufacturer who directly serves end consumers with uncertain demand. End consumers partially adopt the new Smart Replenishment mode, which results in a mix of VMI and non-VMI customers. We assume that unfulfilled demand is lost and that the manufacturer’s dispatch capacity is constrained. Customers compete for the same capacity while featuring different out-of-stock risks and service-level expectations, both of which are costly to the manufacturer. Considering various adoption levels, we decide on the design of such a system and focus on (i) inventory control, (ii) customer prioritization, and (iii) degree of smart, integrated decision-making. Using discrete-event simulation and a full-factorial experiment, we show that replenishment decisions can be significantly enhanced with POC information. It leads to substantial improvements in service levels and capacity utilization without loading customers with inventories. This improvement potential is highest for a low demand coverage of the replenishment quantity, a high gap in the ordering behavior of manufacturer and end consumers, and a long lead time. To realize this improvement potential, we propose a flexible reorder corridor to manage inventories at VMI customers that balances the trade-off between out-of-stock risk and service-level expectation inherent in the system.
(2020): Multiperiod Inventory Management with Budget Cycles: Rational and Behavioral Decision-Making, Production and Operations Management, 29 (3): 643-663.
Abstract: We examine inventory decisions in a multiperiod newsvendor model. In particular, we analyze the impact of budget cycles in a behavioral setting. We derive optimal rational decisions and characterize the behavioral decision‐making process using a short‐sightedness factor. We test the aforementioned effect in a laboratory environment. We find that subjects reduce order‐up‐to levels significantly at the end of the current budget cycle, which results in a cyclic pattern during the budget cycle. This indicates that the subjects are short‐sighted with respect to future budget cycles. To control for inventory that is carried over from one period to the next, we introduce a starting‐inventory factor and find that order‐up‐to levels increase in the starting inventory.
|since 2017||Professor of Supply Chain and Operations Strategy at Kühne Logistics University, Hamburg, Germany|
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
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.