Personalization has received extensive attention as a new manufacturing paradigm to address increased market demand for personalized products. An open product architecture that assembling common, customized, and personalized modules is regarded as a key enabler for product personalization, which can deliver one-of-a-kind products for individual customers at near mass production efficiency. Offering the best product architecture should consider the variations in design variables and parameters that influence the performance of a product architecture. This is especially true when designing open architecture for personalized products as many uncertain design quantities need to be considered in early product design stage.

A robustness optimization method is proposed to simultaneously optimize product variety, module variant selection, and configuration of personalized module variants for a personalized assembly architecture. First, a profit model is developed to measure the performance of a product architecture, which incorporates individual customer preferences and manufacturing cost. A three-step process is proposed to model heterogeneous customer preferences: conjoint analysis of the preferences of a sample of customers from target market, market segmentation by a multi-variate normal mixture method, and simulation of personal preferences for a broader market by Monte-Carlo simulation. Thus, the simulated individual customer preferences are used to predict the sales and profit of product offerings. Second, the variation of profit associated with a product family architecture due to the uncertainty in customer preference and manufacturing cost estimates is formulated by a sensitivity analysis. A robustness index is defined by combining the objectives of maximizing profit and minimizing its variation. Lastly, a robustness optimization model is established to optimize product architecture by maximizing its robustness index. The proposed method is demonstrated with a personalized bicycle architecture design example.

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