In view of the dynamic change of customer requirements (CRs) during the process of product use, in this paper we propose a Bayesian Nash equilibrium configuration model for product variant design driven by CRs. By analyzing CRs, the complete variant requirements of the products can be obtained. Combined with modularization and parameterization variant design methods, a parametric variant instance is proposed. Since cost and delivery time are affected by the product variant design, firms and customers are established as two decision-making bodies, and Bayesian Nash theory is introduced to the product configuration. The theory takes the product cost and customer satisfaction as the payoff function of the game, and based on the threshold value search of the customer satisfaction it determines the strategy set of the two parties. The Nash equilibrium solution equation is established and solved by a simulated annealing algorithm. The optimal product configuration scheme satisfying the interests of both sides of the game is obtained. Finally, the automatic guided vehicle (AGV) is taken as an example to illustrate the effectiveness and practicability of the method.
Customer requirement preference is an important part of customer satisfaction. In view of similar case retrieval technology for existing product level, in the process of solving similar cases, there is no consideration for customer requirement preference. This article proposes a similar case solution method considering customer requirement preference. First, we deal with the expression of customer requirements and transform them into operable parameter forms according to the mapping model. Second, the preference graph is used to analyze the customer's requirement preference, to determine the preference weight, and to weigh the final weight of the requirement node with the initial weight determined by the fuzzy analytic hierarchy process. Finally, the similarity degree solving model of requirement node and product case attribute parameters is established. By integrating the weights of the above-mentioned nodes, the similarity of the product case is obtained, and a more satisfied case of the customer is obtained. Taking the automated guided vehicle car product as an example, the effectiveness of the proposed method is verified.
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