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Managing high-end ex-demonstration product returns

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  • Muyldermans, L.
  • Van Wassenhove, L.N.
  • Guide, V.D.R.

Abstract

Some manufacturers demonstrate their products so that customers can gain experience before making a purchase. We present a novel application of a closed-loop supply chain where product returns from demonstrations of high-end IT equipment are substantial and the major delay in the system is due to the long demonstration time at the client sites. In addition, the product lifecycle is short and the value erodes rapidly over time, with steep drops in the resale revenue when new product generations are introduced. We present a finite lifecycle model that captures the key trade-offs in this environment, that is, either to reuse a collected ex-demo product for a next demonstration or to salvage its residual value in the secondary market and use a new product to satisfy the next demo request. We derive two cost/revenue signals that enable us to distinguish between fast and slow value erosion. We show that the fast/slow erosion decision is dynamic and depends on the rate of value erosion and the length of the demonstration time. We analyze the optimal demo pool strategies and show that in the case of fast erosion it may be better to postpone reuse activities until later in the lifecycle. We illustrate our model using empirical data from a large IT manufacturer and formulate several guidelines so as to better manage high value ex-demonstration product returns.

Suggested Citation

  • Muyldermans, L. & Van Wassenhove, L.N. & Guide, V.D.R., 2019. "Managing high-end ex-demonstration product returns," European Journal of Operational Research, Elsevier, vol. 277(1), pages 195-214.
  • Handle: RePEc:eee:ejores:v:277:y:2019:i:1:p:195-214
    DOI: 10.1016/j.ejor.2019.02.031
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    References listed on IDEAS

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    Cited by:

    1. Quanxi Li & Haowei Zhang & Kailing Liu, 2021. "Research on Closed-Loop Supply Chain Decision-Making in Different Cooperation Modes with Government’s Reward-Penalty Mechanism," Sustainability, MDPI, vol. 13(11), pages 1-22, June.
    2. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).

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