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
(2023): When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders, European Journal of Operational Research: .
Abstract: Slow-moving goods are common in many retail settings and occupy a vast part of retail shelves. Since stores sell these products irregularly and in small quantities, the replenishing distribution center may only place batched orders with manufacturers every few weeks. While order quantities are often fixed, the challenge for manufacturers facing such intermittent demand is to forecast the order timing. In this paper, we explore the value of Point-of-Sales (PoS) data to improve a food manufacturer’s order timing forecast for slow-moving goods. We propose an inventory modeling approach that uses the last order, PoS data from retail stores, and the expected lead time demand to estimate the retailer’s channel inventory. With this dynamic estimate, we can ‘nowcast’ the retailer’s inventory and predict his next order. To illustrate our methodology, we first conduct an experimental simulation and compare our results to a Croston variant and a moving average model. Next, we validate our approach with empirical data from a small German food manufacturer that serves a grocery retailer with a central distribution center and 53 hypermarkets. We find that, on average, our approach improves the accuracy of order-timing predictions by 10–20 percent points. We overcome a shrinkage-induced bias by incorporating an inventory correction factor. Our approach describes a new way of utilizing PoS data in multi-layered distribution networks and can complement established forecasting methods such as Croston. Particular applications arise when the order history is short (e.g., product launch) or represents a bad predictor for future demand (e.g., during COVID-19).
(2023): Digital transformation in operations management: Fundamental change through agency reversal, Journal of Operations Management, 69 (6): 876-889.
Abstract: The emergence of digital technologies across all aspects of operations management has enabled shifts in decision making, shaping new operational dynamics and business opportunities. The associated scholarly discussions in information systems and operations management span digital manufacturing, the digitalization of operations management and supply chain management, platform outcomes, and economies of collaboration. For such changes to be successful, however, there is a need for organizations to go beyond the mere adoption of digital technologies. Instead, successful changes are transformational, delving into digital transformation endeavors, which in turn can enable operational improvements in organizational performance, lead to structural changes in operations processes, and may result in new business models being deployed. Our aim here, thus, is to provide an epistemic platform to advance our understanding of how such endeavors, including the adoption of digital technologies, business model innovations, and innovations in collaboration mechanisms and methods of operations improvement, can affect various aspects of operations management.
(2023): Adherence to standard operating procedures for improving data quality: An empirical analysis in the postal service industry, Transportation Research Part E: Logistics and Transportation Review, 176: .
Abstract: Complete and accurate data is an important enabler of effective supply chain decision making. Despite the increasing efforts to fully automate data collection processes using advanced sensors and scanners, human operators are still in charge of data entry tasks in most industries. Unfortunately, operators do not often comply with the standard operating procedures (SOPs) and do not always exhibit the consistency and commitment required to collect high-quality data. In fact, data collection is often perceived as a non-value-adding activity that increases workloads and lowers productivity. We aim to empirically study the extent to which compliance with SOPs for data collection is affected by some of the key factors. Using a large dataset obtained from a leading postal service provider in Australia, we find that an operator’s workload, fatigue, and related work experience directly impact the compliance levels. We also find that a company’s compliance reinforcement intervention to improve compliance behavior can moderate these impacts.
(2023): On the Drivers of Drug Shortages: Empirical Evidence from Germany, SSRN Electronic Journal: .
Abstract: Purpose - Disruptions and shortages of drugs have become severe problems in recent years, which has triggered strong media and public interest in the topic. However, little is known about the factors that can be associated with the increased frequency of shortages. In this paper, we analyze the drivers of drug shortages using empirical data for Germany, the fourth largest pharmaceutical market. Design/methodology/approach - We use a dataset provided by the German Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte [BfArM]) with 425 reported shortages for drug substances (DSs) in the 24-month period between May 2017 and April 2019 and enrich the data with information from additional sources. Using logistic and negative binomial regression models, we analyze the impact of (1) market characteristics, (2) drug substance characteristics and (3) regulatory characteristics on the likelihood of a shortage. Findings - We find that factors like market concentration, patent situation, manufacturing processes or dosage form are significantly associated with the odds of a shortage. We discuss the implications of these findings to reduce the frequency and severity of shortages. Originality/value – We contribute to the empirical research on drug shortages by analyzing the impact of market characteristics, DS characteristics and regulatory characteristics on the reported shortages. Our analysis provides a starting point for better prioritizing efforts to strengthen drug supply as it is currently intensely discussed by healthcare authorities.
(2023): Realizing supply chain agility under time pressure: Ad hoc supply chains during the Covid-19 pandemic, Journal of Operations Management, 69 (3): 426-449.
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.
|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.