Data-driven optimized vehicle-level engineering specifications

The purpose of this paper is to propose a methodology that determines vehicle-level specifications for new-car program by balancing market environments and engineering feasibility in the early stages of the vehicle development processes using statistical analysis of historical data.

The proposed methodology effectively captures the interplay among key factors in preliminary vehicle planning: engineering feasibility constraints, market demands, and economic conditions. Engineering design constraints, derived by statistical analysis of historical data, define the strategic feasible space. Within the defined design space, the methodology determines a set of specifications that maximize the customer utility which is built as a function of preferences on each attribute of a vehicle.

The present paper develops an “extrapolation” approach using historical vehicle data, rather than attempt to model a complex system with limited information. In doing so, the proposed approach avoids the difficulties of understanding an entire complex system by determining only the moderate level of specifications. Moreover, its quantification of revealed customer preferences as expressed in sales data resolves the confusions in vehicle planning arising from the translation of customer requirements to engineering specification.

The proposed methodology can provide feasible prediction values with a new, historical-data-based statistical approach that effectively surmounts the difficulty of mechanically understanding complex systems. Moreover, through quantification of the target market's customer requirements as well as effects of market-environmental changes, the methodology enables designers to plan complex products for new concept in objective and reasonable manner.