Service-oriented knowledge recommender system and performance evaluation in industrial product development

Manufacturing firms today co-exist in a complex collaboration network with their partners, who often seek help from external knowledge services to co-develop more competitive products. Thus, knowing how to accurately and rapidly acquire such knowledge to sustainably improve the quality of knowledge services are critical to maintaining the competitiveness of such firms. There is a need to develop reliable frameworks and tools to support the knowledge services of these firms. This paper proposes a service-oriented knowledge recommender framework focusing on the mechanism of knowledge service transfer and the knowledge recommendation model, which comprises the knowledge service demanders, knowledge service providers, and platform operators. The matching processes and algorithm of the knowledge service demanders and knowledge service providers are designed based on the product development tasks. A fuzzy synthetic evaluation method is proposed, using multi-factor, fuzzy decision making techniques, to improve the quality of the knowledge service. To validate the approach, a case study on gantry crane development is provided to show how to apply the proposed framework and system. An experimental evaluation is designed to demonstrate the benefit and performance of the framework.