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The Value of Information in Reverse Logistics

Author

Listed:
  • Ketzenberg, M.E.
  • van der Laan, E.A.
  • Teunter, R.H.

Abstract

We explore the value of information in the context of a remanufacturer that faces uncertainty with respect to demand, product return, and product recovery (yield loss). We assume a single period model in which the operational decision of interest is the quantity of new product to order. Our objective is to evaluate the absolute and relative value of the different types of information that such a firm may choose to invest in order to reduce the uncertainty it experiences in matching supply with demand. The different types of information include demand, return, and yield loss. Our results are extensive and reveal that the value for any specific type of information depends both on the overall level of uncertainty and the level of uncertainty that is attributed to the information for which it explains. We develop and test a theoretical model that is predictive of 1) the value of each type of information, 2) the conditions that give rise to the value for each type of information, and 3) the relative value for each type of information.

Suggested Citation

  • Ketzenberg, M.E. & van der Laan, E.A. & Teunter, R.H., 2004. "The Value of Information in Reverse Logistics," ERIM Report Series Research in Management ERS-2004-053-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:1447
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    References listed on IDEAS

    as
    1. Gérard P. Cachon & Marshall Fisher, 2000. "Supply Chain Inventory Management and the Value of Shared Information," Management Science, INFORMS, vol. 46(8), pages 1032-1048, August.
    2. Vlachos, Dimitrios & Dekker, Rommert, 2003. "Return handling options and order quantities for single period products," European Journal of Operational Research, Elsevier, vol. 151(1), pages 38-52, November.
    3. de Brito, M.P. & Flapper, S.D.P. & Dekker, R., 2002. "Reverse logistics," Econometric Institute Research Papers EI 2002-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Srinagesh Gavirneni & Roman Kapuscinski & Sridhar Tayur, 1999. "Value of Information in Capacitated Supply Chains," Management Science, INFORMS, vol. 45(1), pages 16-24, January.
    5. Mostard, J. & Teunter, R.H., 2002. "The Newsboy Problem with Resalable Returns," ERIM Report Series Research in Management ERS-2002-89-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Vincent P. Simpson, 1978. "Optimum Solution Structure for a Repairable Inventory Problem," Operations Research, INFORMS, vol. 26(2), pages 270-281, April.
    7. Bourland, Karla E. & Powell, Stephen G. & Pyke, David F., 1996. "Exploiting timely demand information to reduce inventories," European Journal of Operational Research, Elsevier, vol. 92(2), pages 239-253, July.
    8. de Brito, M.P. & van der Laan, E.A., 2002. "Inventory control with product returns: the impact of (mis)information," Econometric Institute Research Papers EI 2002-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    Cited by:

    1. Talay, Işılay & Özdemir-Akyıldırım, Öznur, 2019. "Optimal procurement and production planning for multi-product multi-stage production under yield uncertainty," European Journal of Operational Research, Elsevier, vol. 275(2), pages 536-551.
    2. Nenes, George & Panagiotidou, Sofia & Dekker, Rommert, 2010. "Inventory control policies for inspection and remanufacturing of returns: A case study," International Journal of Production Economics, Elsevier, vol. 125(2), pages 300-312, June.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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