Prof. Çerağ Pinçe, PhD

Assistant Professor of Operations and Supply Chain Management

Çerağ Pinçe specializes in supply chain management with emphasis on sustainable operations and after-sales services. He addresses real-world business problems by closely collaborating with managers in consumer electronics, hi-tech, and aerospace industries. Driven by the questions faced by those managers, his research has examined topics including dynamic value recovery from returned consumer electronics products, proactive management of critical service parts, and value of installed base information in service-centric supply chains. His work has been appeared in leading academic journals and provided input for companies such as IBM. His other research interests include speculative stocking under procurement cost fluctuations and efficient supply allocation in the agribusiness context.

He holds a PhD from Erasmus University and held a postdoctoral appointment at Georgia Tech Scheller College of Business prior to joining to Kühne Logistics University.

Pince has previously worked as a researcher at the National Research Institute of Electronics and Cryptology in Turkey.


Tel: +49 40 328707-251
Fax: +49 40 328707-209


Selected Publications

Copy reference link   DOI: 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.

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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.

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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.

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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.

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Academic Positions

Since 2011

Assistant Professor of Operations and Supply Chain Management, Kühne Logistics University, Hamburg

2010 - 2011

Postdoctoral Research Fellow, College of Management, Georgia Institute of Technology, Atlanta

2005 - 2010

Ph.D. Candidate in Management, Erasmus University, Rotterdam



Ph.D., Management, Erasmus University, Rotterdam. 
Specialization: Operations Management and Logistics. 
Dissertation: Advances in Inventory Management: Dynamic Models.
Advisors: Rommert Dekker and Hans Frenk


MS, Industrial Engineering, Bilkent University, Ankara


BS, Statistics, Hacettepe University, Ankara