Design Analytics in Consumer Product Design: A Simulated Study

Abstract

A growing area of research in the engineering community is the use of data and analytics for transforming information into knowledge to design better systems, products, and processes. Data-driven decisions can be made in the early, middle, and late stages in a design process where customer needs are identified and understood, a final concept for a design is chosen, and usage data from the deployed product is captured, respectively. Design Analytics (DA) is a paradigm for improving the core information-to-knowledge transformations in these stages of a design process resulting in better performing and functioning products that reflect both explicit and implicit customer needs. In this paper, a simulator is used to model usage of a hypothetical refrigerator and generate artificial data driven by four different customer behavior profiles with variation. The population of customers is randomly divided among the four behavior profiles so that the underlying customer preferences are unknown to the experimenter prior to data analysis. The purpose of the simulation is to illustrate the use of DA in the late stage of a design process to improve the transition from an existing product to the next generation product. Metrics are developed to analyze the product usage data, and both prevailing and subtle usage trends are identified. After conclusions are made, the study proceeds to the early and middle stages of a subsequent design process where a hypothetical next-generation refrigerator is conceptualized.