Game analysis of product specifications for design optimisation using big sales data

AbstractProduct specifications that precisely describe customers’ preference for product comparison and purchase decision-making are critical to product competitiveness. Competitive specifications’ determination requires a better understanding of the comparison-oriented product competition mechanism. Big sales data that consist of comprehensive and multi-dimensional datasets of products are valuable sources for the investigation of the specification competition mechanism. In this work, the relative satisfaction hypothesis and rational comparison hypothesis are proposed on the basis of product competition analysis. A framework is proposed to support rational design decisions in the context of competitiveness-oriented design optimisation. This framework is based on game analysis of product specifications using big sales data. Initially, competing products in the market are collected and modelled for game analysis. To monitor the competition mechanism of specifications amongst competing products, the relative satisfaction is then defined to evaluate the comparison results of product specifications. By comparing different specifications and their combinations, four design scenarios (i.e. dominant, dominated, equivalent and mixed design) are identified for design evaluation. Similarities amongst specifications are considered for supporting both homogeneous and differentiated competition. Ultimately, a bi-level optimisation model is proposed for enhancing product competitiveness. A case study illustrates the implementation of the proposed method.KEYWORDS: Product designcompetition analysisgame theorydesign optimisationbig sales data AcknowledgementsThe authors thank the National Key Research and Development Program of China (No: 2018YFB1701701), and Special Grand from Tianjin City Government, China for providing financial support to this research.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key Research and Development Program of China [grant number 2018YFB1701701].