In this research, we use two different theoretical models for customized modular product design to establish the relationship of product evaluation between the status of consumer demands and the features of the product. Furthermore, by using different theories and the database of the product modules that built through the inputs of the experts we build the criteria for recommending the most suitable product by its modulated functions or components. Then, such mechanism is used to provide customer recommendation system for two different companies with their modulated products. The company can use the system to recommend suitable modulated product according to the needs of different customers. The customer can also use the system to search the desired products by inputting the requirement information. The model build by the customized modular product design in this research, product design model I: we use fuzzy information axiom as the evaluation and decision principle of the product design model. Product design model I: the Analytical Hierarchy Process, Fuzzy Set Theory, Back-Propagation neural network, and Gray Relational Analysis are also used for the evaluation and decision principle of the product design model. With the maturity of current network technologies and e-commerce practices, a suitable recommendation service system to guide customer’s needs is needed for marketing. The manufacturers can use this system to extract the information of the needs for their customers as well as the choices of the products the made. Such information should provide valuable inputs for the sales and future improvement of the product to the company.
關聯:
The International Journal of Organizational Innovation vol. 4, no. 2 pp.123-147
