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General Motors Optimizes Vehicle Content for Customer Value and Profitability

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.

Ketersediaan

Informasi Detail

Judul Seri

INFORMS JOURNAL ON APPLIED ANALYTICS; Vol.53 No.1 January-February 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 59-69

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONJOINT ANALYSIS
MIXED LOGIT MODEL
EDELMAN AWARD
MULTIOBJECTIVE NONLINEAR OPTIMIZATION

Info Detail Spesifik

https://doi.org/10.1287/inte.2022.1144

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Peiling Wu-Smith, Philip T. Keenan, Jonathan H. Owen, Andrew Norton, Kelly Kamm, et al.

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