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.
Maglaras, Constantinos and Joern Meissner (2006): Dynamic Pricing Strategies for Multi-Product Revenue Management Problems, Manufacturing & Service Operations Management, 8 (2): 136-148.
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.
Federgruen, Awi, Joern Meissner and Michal Tzur (2007): Progressive Interval Heuristics for the Multi-Item Capacitated Lot Sizing Problems, Operations Research, 55 (3): 490-502.
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.
Meissner, Joern and Arne K. Strauss (2012): Network revenue management with inventory-sensitive bid prices and customer choice, European Journal of Operational Research, 216 (2): 459-468.
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.
Meissner, Joern, Arne K. Strauss and Kalyan Talluri (2013): An enhanced concave program relaxation for choice network revenue management, Production and Operations Management, 22 (1): 71-87.
Abstract: The network choice revenue management problem models customers as choosing from an offer set, and the firm decides the best subset to offer at any given moment to maximize expected revenue. The resulting dynamic program for the firm is intractable and approximated by a deterministic linear program called the CDLP which has an exponential number of columns. However, under the choice-set paradigm when the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has been proposed but finding an entering column has been shown to be NP-hard. In this study, starting with a concave program formulation called SDCP that is based on segment-level consideration sets, we add a class of constraints called product constraints (σPC), that project onto subsets of intersections. In addition, we propose a natural direct tightening of the SDCP called inline image, and compare the performance of both methods on the benchmark data sets in the literature. In our computational testing on the data sets, 2PC achieves the CDLP value at a fraction of the CPU time taken by column generation. For a large network our 2PC procedure runs under 70 seconds to come within 0.02% of the CDLP value, while column generation takes around 1 hour; for an even larger network with 68 legs, column generation does not converge even in 10 hours for most of the scenarios while 2PC runs under 9 minutes. Thus we believe our approach is very promising for quickly approximating CDLP when segment consideration sets overlap and the consideration sets themselves are relatively small.
Koenig, Matthias and Joern Meissner (2010): List pricing versus dynamic pricing: Impact on the revenue risk, European Journal of Operational Research, 204 (3): 505-512.
Abstract: We consider the problem of a firm selling multiple products that consume a single resource over a finite time period. The amount of the resource is exogenously fixed. We analyze the difference between a dynamic pricing policy and a list-price capacity control policy. The dynamic pricing policy adjusts prices steadily resolving the underlying problem every time step, whereas the list pricing policy sets static prices once but controls the capacity by allowing or preventing product sales.As steady price changes are often costly or unachievable in practice, we investigate the question of how much riskier it is to apply a list pricing policy rather than a dynamic pricing policy. We conduct several numerical experiments and compare expected revenue, standard deviation, and conditional-value-at-risk between the pricing policies. The differences between the policies show that list pricing can be a useful strategy when dynamic pricing is costly or impractical.
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Professor of Supply Chain Management & Pricing Strategy at Kühne Logistics University
|2005 - 2011|
Lecturer in Management Science, Lancaster University Management School.
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.
Master of Philosophy, Columbia University, Graduate School of Business, New York.
Diplom-Kaufmann (Diploma in Business), University of Hamburg.
Best paper award of the POMS 26th Annual Conference for the Humanitarian Operations and Crisis Management track (2015)
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".