Using Big Data to Minimize Uncertainty Effects in Adaptable Product Design

Abstract

One of the major concerns for adaptable products is to ensure the products to meet customer preferences. As customers may update their preferences over the product lifetime, designers need methods to measure those preferences. Lack of knowledge (uncertainty) in customer preferences could endanger the product success. If designers can update their views for customer requirements, a product can be designed to follow the user requirements. Huge data are generated continuously in product user behavior, product usage, manufacturing cost etc., now called as Big Data. Collecting, managing and applying such huge set of data in an innovative method can reduce uncertainties. In this paper, a method is discussed to minimize uncertainty effects on products to improve the product adaptability. Uncertainty is considered as changes of the customer preference. The proposed method uses Big Data (BD) in the analysis of uncertainty. The effect of quantified uncertainties on product adaptability is investigated. The method is concluded with the most affected parts and functional requirements to be updated to meet changing requirements. The proposed method is compared to a developed agent-based modeling (ABM) method in a case study. Although there are some differences between both methods in the uncertainty effect evaluation, The BD method provides more confidence for the design solution. The paper also proposes some future research directions for design of adaptable products using Big Data.