TY - JOUR
T1 - Uncertainty quantification in vehicle content optimization for general motors
AU - Song, Eunhye
AU - Wu-Smith, Peiling
AU - Nelson, Barry L.
N1 - Funding Information:
History: This paper was refereed. Funding: This work was funded by the General Motors Operations Research Department.
Publisher Copyright:
Copyright: © 2020 INFORMS
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - Design of experiments
KW - Discrete choice model
KW - Sensitivity analysis
KW - Uncertainty quantification
KW - Vehicle market simulation
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U2 - 10.1287/INTE.2020.1041
DO - 10.1287/INTE.2020.1041
M3 - Article
AN - SCOPUS:85093869717
VL - 50
SP - 225
EP - 238
JO - Interfaces
JF - Interfaces
SN - 0092-2102
IS - 4
ER -