Product evolution analysis for design adaptation using big sales data

Abstract

Products are constantly evolving through design adaptations to satisfy market requirements. Efficient design adaptation via maximal reusing of existing design can improve product quality, reduce manufacturing cost, and time to market. Understanding of product evolution mechanism is required for design adaptations through rationalized modularity and interfaces. Emerging big sales data provide rich resources for product evolution analysis. To support efficient design adaptations, a framework and associated methods are developed in this work using big sales data for product evolution analysis. Reproduction, mutation, and crossover of product specifications are introduced as specification adaptation operations. Methods are proposed for identification of specification adaptations and estimation of the adaptation probability. Based on modeling and analyzing relationships among product specifications and components, different types of modules and interfaces are proposed through components clustering and their potential operation (mutation, reproduction, and crossover) probabilities. Design recommendations of adaptable product architecture with modules and interfaces are made for facilitating design adaptations. A case study of consumer Unmanned Aerial Vehicles (UVAs) illustrates the proposed method. Limitations and potential extensions of the newly developed method are discussed. Further investigations of the product evolution mechanism using game theory to support competitive product development are included.