Professor Jörn Meissner, PhD

Publications

Professor of Supply Chain Management and Pricing Strategy

Publications

Journal Articles (Peer-Reviewed)

Copy reference link   DOI: 10.1287/msom.2016.0584

Abstract: The high cost of lenient return policies force consumer electronics original equipment manufacturers (OEMs) to look for ways to recover value from lightly used consumer returns, which constitute a substantial fraction of sales and cannot be resold as new products. Refurbishing to remarket or to fulfill warranty claims are the two common disposition options considered to unlock the value in consumer returns, which present the OEM with a challenging problem: How should an OEM dynamically allocate consumer returns between fulfilling warranty claims and remarketing refurbished products over the product’s life cycle? We analyze this dynamic allocation problem and find that when warranty claims and consumer returns are jointly taken into account, the remarketing option is generally dominated by the option of refurbishing and earmarking consumer returns to fulfill warranty claims. Over the product’s life cycle, the OEM should strategically emphasize earmarking of consumer returns at the early stages of the life cycle to build up earmarked inventory for the future warranty demand, whereas it should consider remarketing at the later stages of the life cycle after enough earmarked inventory is accumulated or most of the warranty demand uncertainty is resolved. These findings show that, for product categories with significant warranty coverage and refund costs, remarketing may not be the most profitable disposition option even if the product has strong remarketing potential and the OEM has the pricing leverage to tap into this market. We also show that the optimal dynamic disposition policy is a price-dependent base-stock policy where the earmarked quantity is capacitated by the new and refurbished product sales quantities. We compare with the myopic policy and show that it is a good heuristic for the optimal dynamic disposition policy.

Export record: Citavi Endnote RIS ISI BibTeX WordXML

Copy reference link   DOI: 10.1111/poms.12358

Abstract: The majority of after-sales service providers manage their service parts inventory by focusing on the availability of service parts. This approach, combined with automatic replenishment systems, leads to reactive inventory control policies where base stock levels are adjusted only after a service contract expires. Consequently, service providers often face excess stock of critical service parts that are difficult to dispose due to their specificity. In this study, we address this problem by developing inventory control policies taking into account contract expirations. Our key idea is to reduce the base stock level of the one-for-one policy before obsolescence (a full or partial drop in demand rate) occurs and let demand take away excess stock. We refer to this policy as the single-adjustment policy. We benchmark the single-adjustment policy with the multiple-adjustment policy (allowing multiple base stock adjustments) formulated as a dynamic program and verify that for a wide range of instances the single-adjustment policy is an effective heuristic for the multiple-adjustment policy. We also compare the single-adjustment policy with the world-dependent base stock policy offered by Song and Zipkin (1993) and identify the parameter combinations where both policies yield similar costs. We consider two special cases of the single-adjustment policy where the base stock level is kept fixed or the base stock adjustment is postponed to the contract expiration time. We find that the initial demand rate, contract expiration time, and size of the drop in demand rate are the three key parameters driving the choice between the single-adjustment policy and its special cases.

Export record: Citavi Endnote RIS ISI BibTeX WordXML

Copy reference link   DOI: 10.1016/j.ijpe.2011.11.025

Abstract: Demand for spare parts is often difficult to forecast using historical data only. In this paper, we give an overview of installed based management and provide several ways in which installed base information can be used to support forecasting. We discuss cases where installed base information is used in forecasting at four companies and list the issues involved. Moreover, we review some models to illustrate the potential value of the installed base information and conclude that forecasts of spare parts demand and return can be made considerably more timely and accurate using installed base information.

Export record: Citavi Endnote RIS ISI BibTeX WordXML

Copy reference link   DOI: 10.1016/j.ejor.2011.02.013

Abstract: In this paper, we consider a continuous review inventory system of a slow moving item for which the demand rate drops to a lower level at a known future time instance. The inventory system is controlled according to a one-for-one replenishment policy with a fixed lead time. Adapting to lower demand is achieved by changing the control policy in advance and letting the demand take away the excess stocks. We show that the timing of the control policy change primarily determines the tradeoff between backordering penalties and obsolescence costs. We propose an approximate solution for the optimal time to shift to the new control policy minimizing the expected total cost during the transient period. We find that the advance policy change results in significant cost savings and the approximation yields near optimal expected total costs.

Export record: Citavi Endnote RIS ISI BibTeX WordXML

Copy reference link   DOI: 10.1016/j.ejor.2007.04.055

Abstract: In this study, we analyze an inventory system facing stochastic external demands and an autonomous supply (independent return flow) in the presence of fixed disposal costs and positive lead times under a continuous review replenishment–disposal policy. We derive the analytical expressions of the operating characteristics of the system; and, construct the objective function to minimize the total expected costs of ordering, holding, purchasing and disposal per unit time subject to a fill rate constraint. An extensive numerical analysis is conducted to study the sensitivity of the policy parameters and the benefit of employing a policy which allows for disposal of excess stock in this setting. We model the net demand process as the superposition of normally distributed external demand and inflows, which is expressed as a Brownian motion process. Our findings indicate that the disposal option results in considerable savings even (i) in the presence of non-zero fixed disposal costs, (ii) large actual demand rates with high return ratios (resulting in small net demands) and (iii) for moderate return ratios with high demand variability.

Export record: Citavi Endnote RIS ISI BibTeX WordXML

Journal Articles (Professional)

Books

Copy reference link  

Abstract: In this study, we develop and analyze models incorporating some of the dynamic aspects of inventory systems. In particular, we focus on two major themes to be analyzed separately: nonstationarity in demand rate and unfixed purchasing prices.In the first part of the study, we consider an inventory system with a nonstationary demand rate. In particular, we consider critical service parts subject to obsolescence. Inventory management of such items is notoriously difficult due to their slow moving character and the high risks involved when they are not available or no more needed.In practice, there is a need for policies tailored for service parts taking these aspects into account and easy to implement. We propose an obsolescence based control policy and investigate its performance and impact on costs. We find that ignoring obsolescence in the control policy increases costs significantly and early adaptation of base stock levels can lead to important savings.In the second part of the study, we consider an inventory system where the supplier offers price discounts at random points in time. We extend the literature by assuming a more general backordering structure. That is, when the system is out of stock, an arriving customer either decides to be backlogged with a certain probability or leaves the system and becomes a lost sale. We derive equations to calculate optimal policy parameters and demonstrate that allowing backorders in face of random deal offerings can result in considerable savings.

Export record: Citavi Endnote RIS ISI BibTeX WordXML

Working Papers