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General Motors Optimizes Vehicle Content for Customer Value and Profitability

Author

Listed:
  • Peiling Wu-Smith

    (General Motors, Warren, Michigan 48092)

  • Philip T. Keenan

    (General Motors, Warren, Michigan 48092)

  • Jonathan H. Owen

    (General Motors, Warren, Michigan 48092)

  • Andrew Norton

    (General Motors, Warren, Michigan 48092)

  • Kelly Kamm

    (General Motors, Warren, Michigan 48092)

  • Kathryn M. Schumacher

    (General Motors, Warren, Michigan 48092)

  • Peter Fenyes

    (General Motors, Warren, Michigan 48092)

  • Don Kiggins

    (General Motors, Warren, Michigan 48092)

  • Philip W. Konkel

    (General Motors, Warren, Michigan 48092)

  • William Rosen

    (General Motors, Warren, Michigan 48092)

  • Kurt Schmitter

    (General Motors, Warren, Michigan 48092)

  • Sharon Sheremet

    (General Motors, Warren, Michigan 48092)

  • Laura Yochim

    (General Motors, Warren, Michigan 48092)

Abstract

General Motors (GM) vehicles have more than 100 customer-facing features, known as vehicle content. Decisions about how to package and price these features have a significant impact on our customers’ experiences and on GM’s business results. Vehicle features are assigned as standard, optional, or unavailable on different trim levels, resulting in an enormous combinatorial solution space. Vehicle content optimization (VCO) combines customer market research, discrete choice models, and custom multiobjective nonlinear optimization algorithms to optimize vehicle contenting and pricing decisions. VCO comprehends complex dynamics and tradeoffs and allows GM to optimally balance customer preferences and profitability. After six years of development and multiple proof-of-concept and pilot studies, VCO was officially integrated into GM’s Global Vehicle Development Process in 2014. As of 2021, VCO has been used on more than 85 vehicle programs globally. It has enabled customer-centric product development and more efficient engineering, sourcing, and manufacturing. GM Finance verified that VCO enabled $4.4 billion of incremental profit over the average product life cycle (i.e., six years on average) since 2018, making it a vastly impactful example of operations research and applied analytics.

Suggested Citation

  • Peiling Wu-Smith & Philip T. Keenan & Jonathan H. Owen & Andrew Norton & Kelly Kamm & Kathryn M. Schumacher & Peter Fenyes & Don Kiggins & Philip W. Konkel & William Rosen & Kurt Schmitter & Sharon Sh, 2023. "General Motors Optimizes Vehicle Content for Customer Value and Profitability," Interfaces, INFORMS, vol. 53(1), pages 59-69, January.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:1:p:59-69
    DOI: 10.1287/inte.2022.1144
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    References listed on IDEAS

    as
    1. Eunhye Song & Peiling Wu-Smith & Barry L. Nelson, 2020. "Uncertainty Quantification in Vehicle Content Optimization for General Motors," Interfaces, INFORMS, vol. 50(4), pages 225-238, July.
    2. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
    3. Kenneth E. Train & Clifford Winston, 2007. "Vehicle Choice Behavior And The Declining Market Share Of U.S. Automakers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1469-1496, November.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    Full references (including those not matched with items on IDEAS)

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