Factors considered in product design have a significant impact on future cost and sustainability. Some factors such as materials, dimensions, and tolerances are crucial in the design process of many products. At the macro scale, this can give rise to environmental effects and influence the reliability and sustainability of the product. In this research, a hybrid system dynamics simulation-optimization approach has been proposed for tolerance design. In this approach, first, the optimal values of tolerance and price are determined by conjoint analysis. These values are entered into a system dynamics model to evaluate the relationships among sustainability, reliability, and customer satisfaction. Then, the impact of tolerance and other factors of product design on sustainability, reliability, and customer satisfaction are investigated. This approach can help decision-makers better understand the trade-off between design factors, resilience, sustainability, and customer satisfaction and make more informed decisions. The proposed method is illustrated for computer assembly step by step. The results show that lower sustainability and higher customer satisfaction are directly associated with tighter tolerances. Besides, lower sustainability, higher reliability, and higher customer satisfaction are associated with lower quality of materials. The validity of the model was examined using a boundary-adequacy test.
This study aims at understanding factors of an industrial product driving the sustainability and long-term survival. On the whole, sustainability in product design is an important issue for attracting and retaining customers around the world. Based on a printer case, this study attempted to determine sustainability factors in the product design based on a hybrid method of the conjoint analysis (CA), the fuzzy cognitive mapping (FCM) and the system dynamics (SD). In this approach, first, the optimal values of initial data are determined by CA. These values are entered into a FCM-SD model to evaluate the cause-and-effect relationships between factors of sustainability and long term scenarios. The results provide insights into which factors most strongly influence sustainability, namely, environmental, social, and economical factors. Hence, diverse scenarios at inter and intra-cluster levels were created to clarify the effects of variable changes on the model described.
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