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Mass Customization and the “Parts-Procurement Planning Problem”

Author

Listed:
  • Ali Fattahi

    (Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202)

  • Sriram Dasu

    (Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Reza Ahmadi

    (Anderson School of Management, University of California, Los Angeles, California 90095)

Abstract

We study a new parts-procurement planning problem that is motivated by a global auto manufacturer (GAM) that practices mass customization. Because of the astronomically large number of producible configurations, forecasting their demand is impossible. Instead, firms forecast demand for options that constitute a vehicle. Requirements for many parts (up to 60%) are based on the combinations of options in a fully configured vehicle. The options’ forecast, however, does not map into a unique configuration-level forecast. As a result, the options’ forecast translates into ranges for many parts’ requirements. The combined ranges of a set of parts are not always equal to the sum of the component ranges; they may be less. Determining parts ranges is a large-scale NP-hard problem. Large ranges and inaccurate calculation of these ranges can result in excess inventories, shortages in inventories, and suboptimal flexibility levels. We model and analyze the problem of allocating parts to suppliers and accurately computing the ranges to minimize procurement costs arising because of ranges. The range costs are assumed to be convex increasing. We perform extensive numerical analysis using a large set of randomly generated instances as well as eight industrial instances received from GAM to establish the quality of our approximation framework. Our proposed approach significantly reduces the error in range estimates relative to current industry practice. In addition, the proposed approach for allocations of parts to suppliers reduces joint-parts ranges by an average of 29.87% relative to that of current practice.

Suggested Citation

  • Ali Fattahi & Sriram Dasu & Reza Ahmadi, 2022. "Mass Customization and the “Parts-Procurement Planning Problem”," Management Science, INFORMS, vol. 68(8), pages 5778-5797, August.
  • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:8:p:5778-5797
    DOI: 10.1287/mnsc.2021.4172
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    References listed on IDEAS

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    1. Rodríguez, Betzabé & AydIn, Göker, 2011. "Assortment selection and pricing for configurable products under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 210(3), pages 635-646, May.
    2. Da Silveira, Giovani & Borenstein, Denis & Fogliatto, Flavio S., 2001. "Mass customization: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 72(1), pages 1-13, June.
    3. Yehuda Bassok & Ravi Anupindi, 2008. "Analysis of supply contracts with commitments and flexibility," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(5), pages 459-477, August.
    4. Andy A. Tsay, 1999. "The Quantity Flexibility Contract and Supplier-Customer Incentives," Management Science, INFORMS, vol. 45(10), pages 1339-1358, October.
    5. Atan, Zümbül & Ahmadi, Taher & Stegehuis, Clara & Kok, Ton de & Adan, Ivo, 2017. "Assemble-to-order systems: A review," European Journal of Operational Research, Elsevier, vol. 261(3), pages 866-879.
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