An Analysis of the Optimal Customer Clusters Using Dynamic Multi-Objective Decision

Abstract

The M-type society has emerged in recent years, under which the Food and Beverage (F&B) industry is facing more intense competition. Product innovation and development capabilities, competition, increasing raw material costs, and the rising awareness of consumers pose even further challenges. This study used a project portfolio as the decision subject to identify customer clusters, with the research purposes: (1) to identify the optimal customer clusters by minimizing inter-cluster relationships and (2) to maximize the intra-cluster relationships. The findings help address the problem of a dispersed optimal intra-group structure caused by clustering in the inter-group relationships. Computerized management of that is conducive in accounting and information analysis. Thus, dynamic multi-objective service decisions can provide the related industries with development strategies when facing uncertainties.