Uncertainty Quantification in Vehicle Content Optimization for General Motors

Eunhye Song (), Peiling Wu-Smith () and Barry L. Nelson ()
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Eunhye Song: The Pennsylvania State University, University Park, Pennsylvania 16801;
Peiling Wu-Smith: General Motors, Warren, Michigan 48092;
Barry L. Nelson: Northwestern University, Evanston, Illinois 60201

Interfaces, 2020, vol. 50, issue 4, 225-238

Abstract: A vehicle content portfolio refers to a complete set of combinations of vehicle features offered while satisfying certain restrictions for the vehicle model. Vehicle Content Optimization (VCO) is a simulation-based decision support system at General Motors (GM) that helps to optimize a vehicle content portfolio to improve GM’s business performance and customers’ satisfaction. VCO has been applied to most major vehicle models at GM. VCO consists of several steps that demand intensive computing power, thus requiring trade-offs between the estimation error of the simulated performance measures and the computation time. Given VCO’s substantial influence on GM’s content decisions, questions were raised regarding the business risk caused by uncertainty in the simulation results. This paper shows how we successfully established an uncertainty quantification procedure for VCO that can be applied to any vehicle model at GM. With this capability, GM can not only quantify the overall uncertainty in its performance measure estimates but also identify the largest source of uncertainty and reduce it by allocating more targeted simulation effort. Moreover, we identified several opportunities to improve the efficiency of VCO by reducing its computational overhead, some of which were adopted in the development of the next generation of VCO.

Keywords: vehicle market simulation; design of experiments; discrete choice model; uncertainty quantification; sensitivity analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:50:y:2020:i:4:p:225-238

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