To Identify Technology Frontier for Mass-Customized Production Service Converged with Artificial Intelligence Based on Patent Data Mining

Abstract

This research aims to study the technology frontier of mass-customized (MC) production service based on technology development trajectory analysis. MC production service has been put forward for about 50 years, and the development of artificial intelligence (AI) raises the intriguing possibility of using MC production mode to satisfy the more and more diverse needs of customers. In this research, the author utilizes both expert interviews and patent analysis to explore the technology development trajectory of information technology and customized production technology to realize MC production, and to identify the technology frontier. The paper purposes a method based on patent analysis integrated the expert opinions to identify the technology frontier. Based on the analysis, the technology related with customized production has been divided into five parts – production process algorithm, production scheduling technology, product storage and transportation technology, customers' demands analysis technology, and system optimization technology. Latent Dirichlet Allocation (LDA) and social network analysis based on patent data are utilized to the fusion development trajectory of information technology and the five parts of customized production technology, respectively. As the results, Derwent Innovation database is utilized to obtain the patent data and the citation network data, and the analysis indicates that AI technology provides more opportunities on the development of MC production service. The fusion of AI technology and customers' demands analysis, and the fusion of AI technology and system optimization are more likely to be achieved, while the fusion of AI technology and production scheduling technology and product storage and transportation technology are still to be developed, one of which is the core process of manufacturing - production scheduling. Finally, this research suggests to support the development of MC production service converged with AI technology, especially on the development of production scheduling technology.