通用汽车为客户价值和盈利能力优化车辆内容

IF 1.1 4区 管理学 Q4 MANAGEMENT
Peiling Wu-Smith, P. Keenan, Jonathan H. Owen, Andrew Norton, Kelly Kamm, Kathryn M. Schumacher, P. Fenyes, Don Kiggins, Philip W. Konkel, W. Rosen, Kurt Schmitter, Sharon Sheremet, Laura Yochim
{"title":"通用汽车为客户价值和盈利能力优化车辆内容","authors":"Peiling Wu-Smith, P. Keenan, Jonathan H. Owen, Andrew Norton, Kelly Kamm, Kathryn M. Schumacher, P. Fenyes, Don Kiggins, Philip W. Konkel, W. Rosen, Kurt Schmitter, Sharon Sheremet, Laura Yochim","doi":"10.1287/inte.2022.1144","DOIUrl":null,"url":null,"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.","PeriodicalId":53206,"journal":{"name":"Informs Journal on Applied Analytics","volume":"72 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"General Motors Optimizes Vehicle Content for Customer Value and Profitability\",\"authors\":\"Peiling Wu-Smith, P. Keenan, Jonathan H. Owen, Andrew Norton, Kelly Kamm, Kathryn M. Schumacher, P. Fenyes, Don Kiggins, Philip W. Konkel, W. Rosen, Kurt Schmitter, Sharon Sheremet, Laura Yochim\",\"doi\":\"10.1287/inte.2022.1144\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":53206,\"journal\":{\"name\":\"Informs Journal on Applied Analytics\",\"volume\":\"72 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informs Journal on Applied Analytics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1287/inte.2022.1144\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informs Journal on Applied Analytics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1287/inte.2022.1144","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

摘要

通用汽车(GM)的汽车有100多种面向客户的功能,被称为车辆内容。如何包装和定价这些功能的决定对我们的客户体验和通用汽车的业务成果有重大影响。在不同的装饰水平上,车辆功能被分配为标准、可选或不可用,从而产生了巨大的组合解决方案空间。车辆内容优化(VCO)结合客户市场研究、离散选择模型和自定义多目标非线性优化算法来优化车辆内容和定价决策。VCO理解复杂的动态和权衡,并允许通用汽车最佳地平衡客户偏好和盈利能力。经过六年的开发和多次概念验证和试点研究,VCO于2014年正式纳入通用汽车全球车辆开发流程。截至2021年,VCO已在全球超过85个车辆项目中得到应用。它使以客户为中心的产品开发和更有效的工程、采购和制造成为可能。通用汽车金融证实,自2018年以来,VCO在平均产品生命周期(即平均六年)内实现了44亿美元的增量利润,使其成为运营研究和应用分析的一个极具影响力的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
21.40%
发文量
51
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信