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Pricing Perishables

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Author Info

  • Weaver, Robert D.
  • Moon, Yongma

Abstract

A key feature of food products is their perishability. Within the short marketing window that characterizes most food and ag products, demand is typically highly stochastic and difficult to predict. This combination of features poses substantial challenges to retailers when pricing products and has implications for performance that ripples through vertical food chains. For many food products, processing to forms that can be preserved and held in inventory has traditionally been used as a means of coping with these conditions, despite its high costs and ancillary risks introduced such as change in product attributes and deterioration. This paper presents an alternative ERM strategy that focuses on dynamic pricing to control the rate of sale for perishable products. The paper considers a retailer that has market power to price and supplies perishable products to a market with substitute products and demand originating from heterogeneous consumers. Perishability implies a finite horizon for the marketing of the products over which demand across market segments of consumers is both dynamic and stochastic. Faced with uncertainty, we suppose the firm has limited information about the stochastic properties of demand and must choose a pricing strategy that projects over the market horizon. This price trajectory represents a key control mechanism to cope with uncertainty of both the perishability of the product and of demand. A variety of mechanisms for setting prices has been pursued in the past and can be imagined. Given substantial waste associated with food retailing, it seems evident that retailers may not incorporate the social or food supply network interests in optimal performance. Uniform pricing within the marketing horizon is typical for most food retailers. At the horizon, or shelf-life, the product is often removed from the shelves and either disposed of, or diverted into a secondary market. Despite such practice, lessons exist from other industries where dynamic pricing approaches have been pursued. Here, we consider pricing rules derived from robust optimization that sets price trajectories over the market horizon that explicitly consider two features that appear to be of particular interest for food: 1) sales of all available supply (i.e. eliminate disposal) and 2) existence of close substitutes (i.e. fresher product). An important innovation in many industries for dynamic is the concept of price assurance. We consider two types of price assurance. Under ex-post price assurance, prices are set subject to the constraint that refunds will be paid if future prices are reduced below levels paid by consumers. This is an intriguing variation on “everyday low pricing”. Next, we introduce a novel alternative that we label as ex-ante price assurance where the firm sets the dynamic price trajectory subject to the Robert. D. Weaver and Yongma Moon 213 constraint that prices will not decrease. Though perhaps counter-intuitive in perishable good context, we show this novel approach has merit under particular demand conditions. Thus, we compare three dynamic pricing strategies to manage uncertain demand given perishability: i) robust dynamic pricing, ii) ex-post price assurance, and iii) ex-ante price assurance. Numerical experiments show that our robust optimization model prevents loss when a firm encounters the worst case demand and outperforms a deterministic pricing model. Comparison across different pricing strategies identifies conditions under which particular strategies are superior to the others.

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Bibliographic Info

Paper provided by International European Forum on Innovation and System Dynamics in Food Networks in its series 2011 International European Forum, February 14-18, 2011, Innsbruck-Igls, Austria with number 122007.

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Date of creation: Nov 2011
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Handle: RePEc:ags:iefi11:122007

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Web page: http://www.fooddynamics.org/

Related research

Keywords: Dynamic pricing; demand substitution; consumer choice model; robust optimization; price assurance.; Agribusiness; Agricultural and Food Policy; Food Consumption/Nutrition/Food Safety; Productivity Analysis; Research Methods/ Statistical Methods;

References

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  1. Xuanming Su, 2007. "Intertemporal Pricing with Strategic Customer Behavior," Management Science, INFORMS, vol. 53(5), pages 726-741, May.
  2. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
  3. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
  4. Constantinos Maglaras & Joern Meissner, 2006. "Dynamic Pricing Strategies for Multiproduct Revenue Management Problems," Manufacturing & Service Operations Management, INFORMS, vol. 8(2), pages 136-148, July.
  5. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
  6. Yossi Aviv & Amit Pazgal, 2008. "Optimal Pricing of Seasonal Products in the Presence of Forward-Looking Consumers," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 339-359, December.
  7. Zhang, Dan & Cooper, William L., 2009. "Pricing substitutable flights in airline revenue management," European Journal of Operational Research, Elsevier, vol. 197(3), pages 848-861, September.
  8. Yingjie Lan & Huina Gao & Michael O. Ball & Itir Karaesmen, 2008. "Revenue Management with Limited Demand Information," Management Science, INFORMS, vol. 54(9), pages 1594-1609, September.
  9. Yuri Levin & Jeff McGill & Mikhail Nediak, 2009. "Dynamic Pricing in the Presence of Strategic Consumers and Oligopolistic Competition," Management Science, INFORMS, vol. 55(1), pages 32-46, January.
  10. \c{S}. \.Ilker Birbil & J. B. G. Frenk & Joaquim A. S. Gromicho & Shuzhong Zhang, 2009. "The Role of Robust Optimization in Single-Leg Airline Revenue Management," Management Science, INFORMS, vol. 55(1), pages 148-163, January.
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