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Managing Perishable and Aging Inventories: Review and Future Research Directions

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Planning Production and Inventories in the Extended Enterprise

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 151))

Abstract

Over the years, several companies have emerged as exemplary of “best practices” in supply chain management; for example, Wal-Mart is frequently cited as using unique strategies to lead its market. One significant challenge for Wal-Mart is managing inventories of products that frequently outdate: A significant portion of Wal-Mart’s product portfolio consists of perishable products such as food items (varying from fresh produce to dairy to bakery products), pharmaceuticals (e.g., drugs, vitamins, cosmetics), chemicals (e.g., household cleaning products), and cut flowers. Wal-Mart’s supply chain is not alone in its exposure to outdating risks – to better appreciate the impact of perishability and outdating in society at large, consider these figures: In a 2003 survey, overall unsalable costs at distributors to supermarkets and drug stores in consumer packaged goods alone were estimated at $2.57 billion, and 22% of these costs, over 500 million dollars, were due to expiration in only the branded segment (Grocery Manufacturers of America 2004). In the produce sector, the $1.7 billion US apple industry is estimated to lose $300 million annually to spoilage (Webb 2006). Note also that perishability and outdating are a concern not only for these consumer goods, but for industrial products (for instance, Chen (2006), mentions that adhesive materials used for plywood lose strength within 7 days of production), military ordnance, and blood – one of the most critical resources in health care supply chains. According to a nationwide survey on blood collection and utilization, 5.8% of all components of blood processed for transfusion were outdated in 2004 in the USA (AABB 2005).

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Notes

  1. 1.

    Note that multi-location models we review here are different from the two-warehouse problem described in Section 10 of Goyal and Giri (2001); that model considers a decision-maker who has the option of renting a second storage facility if he/she uses up the capacity of his/her own storage.

  2. 2.

    The model of Lin and Chen (2003) has three echelons in its design: A cross-docking facility (central decision maker) orders from multiple suppliers according to the demand at the retailers and the system constraints, and allocates the perishable goods to retailers. However, the replenishment decisions are made for a single echelon: The authors propose a genetic algorithm to solve for the single-period optimal decisions that minimize the total system cost.

  3. 3.

    One common practice in managing blood inventories is cross-matching, which is assigning units of blood from inventory to particular patients. Jagannathan and Sen (1991) report that more than 50% of blood products held for patients are not eventually transfused (i.e., used by the patient). The release of products that are cross-matched enable re-distribution of inventories in a blood supply chain. See Prastacos (1984), Pierskalla (2004), and Jagannathan and Sen (1991) for more information on cross-matching.

  4. 4.

    There is research on coordination issues in supply chains with deteriorating goods: A permissible delay in payment agreement between a retailer and a supplier is proposed in the deterministic model of Yang and Wee (2006) to coordinate the supply chain. Chen and Chen (2005) study centralized and decentralized planning for the joint replenishment problem with multiple deteriorating goods.

  5. 5.

    We refer the reader to Prastacos (1984) for earlier, simulation-based research on the effect of freezing blood products on inventory management.

  6. 6.

    Another paper that considers prices of perishable products is by Adachi et al. (1999). Items of each age generate a different revenue in this model, demand is independent of the price, and the inventory is issued in a FIFO manner. The work entails obtaining a replenishment policy via computation of a profit function given a price vector.

  7. 7.

    We thank Feryal Erhun from Stanford University for bringing this practical issue, which she has witnessed in blood supply chains, to our attention.

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Authors and Affiliations

  1. Kogod School of Business, American University, 4400 Massachusetts Avenue NW, DC 20016, Washington, USA

    Itir Z. Karaesmen

Authors
  1. Itir Z. Karaesmen
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  2. Alan Scheller–Wolf
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  3. Borga Deniz
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Correspondence to Itir Z. Karaesmen .

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Editors and Affiliations

  1. Decision Technologies, Intel Corporation, W. Chandler Blvd. 5000, Chandler, 85226, Arizona, USA

    Karl G. Kempf

  2. School of Industrial &, Systems Engineering, Georgia Institute of Technology, Ferst Drive NW., 765, Atlanta, 30332-0205, Georgia, USA

    Pınar Keskinocak

  3. , Edward P. Fitts Department of Industrial, North Carolina State University, Daniels Hall 300, Raleigh, 27695, North Carolina, USA

    Reha Uzsoy

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© 2011 Springer Science+Business Media, LLC

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Karaesmen, I.Z., Scheller–Wolf, A., Deniz, B. (2011). Managing Perishable and Aging Inventories: Review and Future Research Directions. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 151. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6485-4_15

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