The Expansion of Value Engineering Theory and Its Application in the Intelligent Automotive Industry

Value engineering (VE), as a conceptual approach and management technique, has allowed enterprises to capture value through mass production and market expansion during the industrial economic era. The VE method has enabled companies to produce products that meet user needs at a lower cost, leading to success. However, as the complexity of society and industry development increases, the lack of theoretical expansion in VE has limited its application in today’s more complex and macro management systems. With the development and evolution of vehicle–road collaborative intelligence, the intelligent automotive industry has become a complex system with multiple entities and interwoven values across different dimensions. Intelligent connected vehicles (ICVs), along with the external intelligent environment, will jointly participate in the realization of system functions. It is no longer sufficient to apply VE methods to analyze ICVs from a single product perspective. The pursuit of “maximizing value” is always the core driving force of industrial development. This study, building on the fundamental ideas of VE, expands and extends the connotation and theory of VE in three aspects: research objects, value dimensions, and associated entities, to adapt to the current situation. It also provides a new analysis process for the VE theory to better address systemic and complex issues. Taking the intelligent automotive industry as a case study, this study analyzes it based on the expanded VE theory. It considers not only the cost of system function realization and the product value of ICVs but also the external benefits of the system across different dimensions. The social value, user value, enterprise value are introduced in entity value analysis, and the relevant indicators are organized. This approach can better guide the collaboration and division of labor among multiple participating entities such as governments, enterprises, and users, achieving overall value maximization.