Prof. Jörn Meissner, PhD

Professor of Supply Chain Management and Pricing Strategy

Joern Meissner is Professor of Supply Chain Management & Pricing Strategy at Kühne Logistics University. Meissner holds a PhD and a Master’s Degree in Management Science from the Graduate School of Business at Columbia University (Columbia Business School) in New York City and a Diploma in Business from the University of Hamburg. His research spans a wide field of study, including the areas of Supply Chain Management (SCM), Pricing Strategy and Revenue Management. His work has been published in various prestigious journals including Operations Research, Manufacturing and Service Operation Management (MSOM), European Journal of Operational Research,  International Journal of Production Research, International Journal of Production Economics and Naval Research Logistics.

Meissner’s main research focus is the area of stochastic and dynamic decision-making, and in particular applications to logistics, manufacturing, supply chain management, and pricing strategy. The aim of his research is to develop and implement robust and efficient techniques to business problems in those domains. A common theme within his research is the use of mathematical optimization techniques such as dynamic programming to guide managers to make better business decisions. Currently, Meissner pursues the following research streams:

  • Global Supply Chain Optimization
    This research focuses on how to efficiently integrate suppliers, producers, and warehouses to produce and distribute the right quantity, at the right time and in the right place while maximizing the total supply chain profit and guaranteeing an appropriate service level. Research questions often focus on incentives for the business partners to align their individual behavior with the goals of the total supply chain.
  • Improvements in Inventory Control
    Control of inventory is a complex task, in particular if it involves a large number of SKUs (stock-keeping units) with intermittent demand patterns, which make forecasting and inventory control very difficult. Due to the large amount of spare part SKUs held in many companies, it is often not possible to configure an inventory system manually for each part separately. Our research evaluates, for example, a sub-grouping of intermittent demand patterns by a categorization scheme and the quality of the resulting inventory control policies.
  • Operations & Service Management
    Service management is integrated into supply chain management as the interface between sales/after-sales and the customer. The aim of high performance service management is to optimize the service-intensive supply chains, which are usually more complex than supply chains concerned exclusively with finished goods. Companies often must accommodate inconsistent and uncertain demand by establishing more advanced information. A typical research question within this context is staffing of call centers or teller stations, for example.
  • Pricing Strategy & Revenue Management
    Recent advances in pricing and revenue management have rapidly changed the environment in which firms operate. The Internet, the adoption of new information technologies, and other market forces are driving the need for more sophisticated pricing methodologies and techniques. Given the tremendous upside of better pricing strategies, more and more industries are adapting mathematical pricing models to their needs. Promising research directions include sophisticated models of consumer behavior, models including competition between different providers, and the evaluation of pricing mechanisms such as auctions. These are very important issues for today's managers and research in this area is promising from both a theoretical and empirical perspective.

As a leading expert in the development of optimization algorithms for a variety of logistics, supply chain management and pricing problems, Joern Meissner is often consulted by industry professionals and helps firms apply his cutting-edge mathematical techniques to their current business problems. He frequently advises companies, ranging from Fortune 500 companies to emerging start-ups, on issues such as supply chain planning and pricing strategy. His current and past clients include British Telecom (BT), British Airways, Apple Europe, Pernod Ricard, Promethean, TUI, The Co-Operative, Littlewoods, Virgin Trains, Virgin Cargo and SAP Germany, among others.

Meissner is a passionate and enthusiastic teacher. He believes that grasping an idea is only half of the fun; conveying it to others makes it whole. At his previous position at Lancaster University Management School, he taught the MBA Core course in Operations Management and originated three new MBA Electives: Advanced Decision Models, Supply Chain Management, and Revenue Management. He has also lectured at the University of Hamburg, the Leipzig Graduate School of Management (HHL), and the University of Mannheim. Meissner offers a variety of Executive Education courses aimed at business professionals, managers, leaders, and executives who strive for professional and personal growth.

In the past, Meissner has also been a successful entrepreneur. He founded Manhattan Review, a global test preparation and admissions consultancy, during his time at Columbia Business School. More recently, he established Lancaster Executive, a provider of executive education programs for senior managers.

For more details about Joern Meissner, including downloads of research articles and white papers, please visit his academic homepage at http://www.meiss.com.

Contact

Tel: +49 40 328707-236
Fax: +49 40 328707-209
joern.meissner@the-klu.org

Networks

Selected Publications

Copy reference link   DOI: 10.1016/j.ejor.2017.09.039

Abstract: Spare parts are necessary for ensuring the functioning of the critical equipment of many companies, and as such, they play a central role in these companies’ operations. Inventory control of spare parts is particularly challenging due to the nature of their demand, which is usually slow-moving, erratic and lumpy. As inventory policies rely on the forecasted lead-time demand distribution and this choice impacts the performance of the system, an ill-suited hypothesized distribution may result in high preventable costs. In this study, we contribute to the empirical literature by analyzing what distributions best fit spare parts demand. We use the Kolmogorov Smirnov (K–S) goodness-of-fit test to find the best-fitting distributions to our data and compare our results to those in the literature. Furthermore, we implement a slightly modified K–S test that places greater emphasis on differences in the right tail of the distribution, mirroring real-world inventory applications, and less emphasis on the left tail. Finally, we link the goodness-of-fit of the distributions to their inventory performance. Our first dataset comes from the German renewable energy industry and is composed of the weekly demand for more than 4000 items over the period 2011–2013. The second dataset comes from the Royal Air Force. It is composed of monthly demand for 5000 items over the period 1996–2002.

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Copy reference link   DOI: 10.1016/j.ejor.2017.06.049

Abstract: Companies commonly allocate their inventories across multiple locations based on their historical sales rates. However, random fluctuations in customer purchases, such as those caused by weather conditions and other external factors, might cause significant deviations from expected demand, leading to excess stock in some locations and stockouts in others. To fix this mismatch, companies often turn to lateral transshipments, e.g., the movement of stock between locations of the same echelon.In this paper, we examine multi-location inventory systems under periodic review with multiple opportunities for proactive transshipments within one order cycle. If stockouts occur, demand is lost with no opportunity to backorder. The objective of our model is to find an optimal policy that indicates the sources and the destinations of transshipments as well as the number of units, to maximise the profit of the network. We create a dynamic program that can, in principal, be solved to optimality using Bellman’s equation. However, the size of the state and decision spaces makes it impossible to find the optimal policy for real-world sized problem instances. Thereby, we use forward approximate dynamic programming to find a near-optimal transshipment policy.Finally, we conduct an extensive numerical study to gauge the performance of our transshipment policy. For small size instances, we compare our policy to the optimal one. For larger scale instances, we consider other practically oriented heuristics. Our numerical experiments show that our proposed algorithm performs very well compared to state-of-the-art methods in the literature.

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Copy reference link   DOI: 10.1016/j.ejor.2011.06.033

Abstract: We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.

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Copy reference link   DOI: 10.1287/opre.1070.0392

Abstract: We consider a family of N items which are produced in or obtained from the same production facility. Demands are deterministic for each item and each period within a given horizon of T periods. If in a given period an order is placed, setup costs are incurred. The aggregate order size is constrained by a capacity limit. The objective is to find a lot-sizing strategy that satisfies the demands for all items over the entire horizon without backlogging, and which minimizes the sum of inventory carrying, fixed and variable order costs. All demands, cost parameters and capacity limits may be time-dependent. In the basic (JS)-model, the setup cost of an order does not depend on the composition of the order. The (JIS)-model allows for item-dependent setup costs in addition to the joint setup costs.We develop and analyze a class of so-called progressive interval heuristics. A progessive interval heuristic solves a (JS) or (JIS) problem over a progressively larger time-interval, always starting with period 1, but fixing the setup variables of a progressively larger number of periods at their optimal values in earlier iterations. Different variants in this class of heuristics allow for different degrees of flexibility in adjusting continuous variables determined in earlier iterations of the algorithm.For the (JS)-model and the two basic implementations of the progressive interval heuristics, we show under some mild parameter conditions, that the heuristics can be designed to be epsilon-optimal for any desired value of epsilon > 0 with a running time that is polynomially bounded in the size of the problem. They can also be designed to be simultaneously asymptotically optimal and polynomially bounded.A numerical study covering both the (JS) and the (JIS) model, shows that a progressive interval heuristic generates close-to-optimal solutions with modest computational effort and that it can be effectively used to solve large-scale problems.

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Copy reference link   DOI: 10.1002/9780470400531.eorms0272

Abstract: This chapter reviews multi-product dynamic pricing models for a revenue maximizing monopolist firm. The baseline model studied in this chapter is of a seller that owns a fixed capacity of a resource that is consumed in the production or delivery of some type of product. The seller selects a dynamic pricing strategy for the offered product so as to maximize its total expected revenues over a finite time horizon. We then review how this model can be extended to settings where the firm is selling multiple products that consume this firm's capacity, and finally highlight a connection between these dynamic pricing models and the closely related model where prices are fixed, and the seller dynamically controls how to allocate capacity to requests for the different products. Methodologically, this chapter reviews the dynamic programming formulations of the above problems, as well as their associated deterministic (fluid) analogues. It highlights some of the key insights and pricing heuristics that are known for these problems, and briefly mentions possible extensions and areas of current interest.

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Research Projects

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Academic positions

Since 2011

Professor of Supply Chain Management & Pricing Strategy at Kühne Logistics University

2005 - 2011

Lecturer in Management Science, Lancaster University Management School.

Education

2005

Ph.D., Columbia University, Graduate School of Business, New York. Thesis: Multi-Item Supply Chain and Revenue Management Problems. Advisors: Professors Awi Federgruen and Costis Maglaras.

2005

Master of Philosophy, Columbia University, Graduate School of Business, New York.

1997

Diplom-Kaufmann (Diploma in Business), University of Hamburg.

2015 - Best paper award of the POMS 26th Annual Conference for the Humanitarian Operations and Crisis Management track

Joern Meissner received the best paper award of the POMS 26th Annual Conference for the Humanitarian Operations and Crisis Management track for her article (together with Maria Besiou and Laura Turrini) "Understanding Fundraising in Humanitarian Supply Chains".