Abstract
AbstractTo reduce the vagueness and subjectivity of customer demand in the process of product–service system design, a fuzzy semantic calculation method is proposed to obtain the importance of service demand. In addition, according to the demand of the clustering of service modules, a new clustering method is proposed to analyse discrete data based on the improved K‐means algorithm that is based on the Kruskal algorithm. According to the criterion of the service module division and its weight, the correlation coefficient between any two service activities is judged to form the comprehensive correlation coefficient matrix, and the comprehensive dissimilarity matrix can be obtained by the additive model. Then, this method calculates the minimum cost spanning tree (MCST) using the Kruskal algorithm. The different clusters of service activities with different centres can be found based on the MCST, and the edge values can be calculated by the improved K‐means algorithm. This paper uses 28 service activities of excavators. These activities can be divided into K (K = 4, 5, 6, and 7) clusters by the improved K‐means algorithm. Finally, the service element configuration model is established based on the demand weight, which is optimized by using the maximum customer satisfaction of competition.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
