Mass Customization and the “Parts-Procurement Planning Problem”

Ali Fattahi (), Sriram Dasu () and Reza Ahmadi ()
Additional contact information
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

Management Science, 2022, vol. 68, issue 8, 5778-5797

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.

Keywords: production scheduling; hierarchical planning; programming; nonlinear; algorithms; integer; applications; MRP (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.4172 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:8:p:5778-5797

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().