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A delayed product customization cost model with supplier delivery performance

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  • Ngniatedema, Thomas
  • Fono, Louis Aimé
  • Mbondo, Georges Dieudonné

Abstract

The concept of delayed product differentiation has received considerable attention in the research literature in recent years. However, few analytical models explain and quantify the benefits of delayed product differentiation strategy with additional consideration of supplier delivery performance. This paper proposes a delayed product differentiation model in which a supply of raw materials is integrated at the beginning of the production process to match uncertain demand in a cost-effective way given the constraint of lead time delivery window. It develops insights regarding a delayed product differentiation strategy and shows that with respect to delivery windows, supplier delivery performance plays an important role in the determination of the optimal point of differentiation. This study also shows that when the “on-time” and the “late” portions of the delivery window are constant, the proposed cost function coincides with similar models found in the literature. An extension of this work also reveals that when the customer service level varies across various production stages, its choice affects the decision to delay or postpone the customization point. A mini industrial case involving the customization of a personal desktop computer is used to illustrate the applicability of the resulting framework.

Suggested Citation

  • Ngniatedema, Thomas & Fono, Louis Aimé & Mbondo, Georges Dieudonné, 2015. "A delayed product customization cost model with supplier delivery performance," European Journal of Operational Research, Elsevier, vol. 243(1), pages 109-119.
    • Handle: RePEc:eee:ejores:v:243:y:2015:i:1:p:109-119
    DOI: 10.1016/j.ejor.2014.11.017
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    References listed on IDEAS

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    Cited by:

    1. Ngniatedema, Thomas & Shanker, Murali & Hu, Michael Y. & Guiffrida, Alfred L. & Eddy Patuwo, B., 2015. "Late customization strategy with service levels requirements," International Journal of Production Economics, Elsevier, vol. 166(C), pages 72-84.
    2. Yilun Zhang & Jianghang Chen & Zhibin Jiang, 2023. "Optimal product service system configuration considering pairing utility and uncertain customer behavior," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 343-375, June.
    3. Wu, Jun & Du, Gang & Jiao, Roger J., 2021. "Optimal postponement contracting decisions in crowdsourced manufacturing: A three-level game-theoretic model for product family architecting considering subcontracting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 722-737.
    4. Singa Wang Chiu & Liang-Wei You & Tsu-Ming Yeh & Tiffany Chiu, 2020. "The Collective Influence of Component Commonality, Adjustable-Rate, Postponement, and Rework on Multi-Item Manufacturing Decision," Mathematics, MDPI, vol. 8(9), pages 1-18, September.
    5. Tao, Liangyan & Liu, Sifeng & Xie, Naiming & Javed, Saad Ahmed, 2021. "Optimal position of supply chain delivery window with risk-averse suppliers: A CVaR optimization approach," International Journal of Production Economics, Elsevier, vol. 232(C).
    6. Heradio, Ruben & Perez-Morago, Hector & Alférez, Mauricio & Fernandez-Amoros, David & Alférez, Germán H., 2016. "Augmenting measure sensitivity to detect essential, dispensable and highly incompatible features in mass customization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1066-1077.

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