Global product family design is the problem in which product variants and supply chain configuration are simultaneously designed. It has become a significant concern of manufacturing industries under globalization. Its context is not only complicated under various factors and their interactions but also vague under strategic decision making. In this paper, first, a multi-objective mixed-integer formulation of simultaneous design of module commonalization and supply chain configuration is developed under the criteria on quality, cost and delivery, and an optimization algorithm for obtaining Pareto optimal solutions is configured by using a neighborhood cultivation genetic algorithm and simplex method. Then, this paper investigates into design concept exploration on the optimality and compromise in global product family design with data-mining techniques, a principal component analysis technique and a self-organizing map technique. This paper demonstrates some numerical case studies for ascertaining the validity and promise of the proposed mathematical model and computational techniques for supporting the designer’s decision making toward the excellence in global product family design.

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