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Establishing dynamic expiration dates for perishables: An application of rfid and sensor technology

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  • Gaukler, Gary
  • Ketzenberg, Michael
  • Salin, Victoria

Abstract

Our research addresses the value of information (voi) for the use of a product's time and temperature history (tth). Using tth information, the retailer can set expiration dates dynamically, based on known environmental conditions. This dynamically set expiration date corresponds to the maximum number of periods that inventory may remain available for sale before it must be removed from inventory and discarded (outdated). In current static practice, however, without the availability of tth, environmental conditions are not known and all units of inventory receive the same expiration date, generally predicated on worst case conditions. Our research demonstrates that information on the tth as a product flows through the supply chain can be very valuable. Using the example of a supply chain for fresh packaged tomatoes, we quantify the value of tth information when used for dynamic expiration date setting. We find that the voi is quite sensitive to environmental and parametric settings, ranging upwards to 90.5% with a mean of 41.2%. Our studies demonstrate that the cost savings that leads to the voi from tth and expiration dating stems from two major sources: eliminating the chance of selling perished product, and greatly decreasing the rate at which lost sales occur. In addition, we show that when dynamic expiration dating is used, average product freshness at the time of sale increases significantly. This indicates a win-win situation where costs to the retailer are reduced, and also additional value for the consumer is created. We also extend our analysis into the impact of imperfect information and find that the voi is fairly robust, up to error levels corresponding to a mean absolute percentage error (mape) of approximately 12%. Median voi at those error levels is 16.5%. The impact of errors, however, differs depending on the model parameterization and we find that under certain settings, the voi can remain significant for much larger values of mape.

Suggested Citation

  • Gaukler, Gary & Ketzenberg, Michael & Salin, Victoria, 2017. "Establishing dynamic expiration dates for perishables: An application of rfid and sensor technology," International Journal of Production Economics, Elsevier, vol. 193(C), pages 617-632.
  • Handle: RePEc:eee:proeco:v:193:y:2017:i:c:p:617-632
    DOI: 10.1016/j.ijpe.2017.07.019
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    1. Ketzenberg, Michael E. & Rosenzweig, Eve D. & Marucheck, Ann E. & Metters, Richard D., 2007. "A framework for the value of information in inventory replenishment," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1230-1250, November.
    2. Haijema, René, 2013. "A new class of stock-level dependent ordering policies for perishables with a short maximum shelf life," International Journal of Production Economics, Elsevier, vol. 143(2), pages 434-439.
    3. Tijskens, L. M. M. & Polderdijk, J. J., 1996. "A generic model for keeping quality of vegetable produce during storage and distribution," Agricultural Systems, Elsevier, vol. 51(4), pages 431-452, August.
    4. Steven Nahmias, 1982. "Perishable Inventory Theory: A Review," Operations Research, INFORMS, vol. 30(4), pages 680-708, August.
    5. Mark Ferguson & V. Daniel R. Guide , Jr. & Gilvan C. Souza, 2006. "Supply Chain Coordination for False Failure Returns," Manufacturing & Service Operations Management, INFORMS, vol. 8(4), pages 376-393, August.
    6. Michael Ketzenberg & Jacqueline Bloemhof & Gary Gaukler, 2015. "Managing Perishables with Time and Temperature History," Production and Operations Management, Production and Operations Management Society, vol. 24(1), pages 54-70, January.
    7. Aiello, Giuseppe & Enea, Mario & Muriana, Cinzia, 2015. "The expected value of the traceability information," European Journal of Operational Research, Elsevier, vol. 244(1), pages 176-186.
    8. Goyal, S. K. & Giri, B. C., 2001. "Recent trends in modeling of deteriorating inventory," European Journal of Operational Research, Elsevier, vol. 134(1), pages 1-16, October.
    9. Kouki, Chaaben & Sahin, Evren & Jemaï, Zied & Dallery, Yves, 2013. "Assessing the impact of perishability and the use of time temperature technologies on inventory management," International Journal of Production Economics, Elsevier, vol. 143(1), pages 72-85.
    10. Fangruo Chen, 1998. "Echelon Reorder Points, Installation Reorder Points, and the Value of Centralized Demand Information," Management Science, INFORMS, vol. 44(12-Part-2), pages 221-234, December.
    11. Muriana, Cinzia, 2016. "An EOQ model for perishable products with fixed shelf life under stochastic demand conditions," European Journal of Operational Research, Elsevier, vol. 255(2), pages 388-396.
    12. 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.
    13. Kamran Moinzadeh, 2002. "A Multi-Echelon Inventory System with Information Exchange," Management Science, INFORMS, vol. 48(3), pages 414-426, March.
    14. Steven Nahmias, 1977. "On Ordering Perishable Inventory when Both Demand and Lifetime are Random," Management Science, INFORMS, vol. 24(1), pages 82-90, September.
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    Cited by:

    1. Voelkel, Michael A. & Sachs, Anna-Lena & Thonemann, Ulrich W., 2020. "An aggregation-based approximate dynamic programming approach for the periodic review model with random yield," European Journal of Operational Research, Elsevier, vol. 281(2), pages 286-298.
    2. Yang, Ya & Chi, Huihui & Tang, Ou & Zhou, Wei & Fan, Tijun, 2019. "Cross perishable effect on optimal inventory preservation control," European Journal of Operational Research, Elsevier, vol. 276(3), pages 998-1012.
    3. Ketzenberg, Michael & Gaukler, Gary & Salin, Victoria, 2018. "Expiration dates and order quantities for perishables," European Journal of Operational Research, Elsevier, vol. 266(2), pages 569-584.

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