Small and medium-sized enterprise dedicated knowledge exploitation mechanism: A recommender system based on knowledge relatedness

Abstract

Knowledge is a vital asset for organizations, especially in today’s Industry 4.0 context with the ever-increasing amount of information being produced. Organizations must consider knowledge management (KM) to create a sustainable competitive advantage. Currently, KM is applied relatively well in large organizations; however, small and medium-sized enterprises (SMEs) encounter various constraints. Knowledge exploitation is a key phase in KM for the retrieval of relevant knowledge. Therefore, a recommender system (RS), which is a promising and widely used information technology (IT) tool, is proposed in this study, for SMEs to enable effective knowledge exploitation. The RS can be adapted to SME KM specificities and a dedicated RS based on knowledge relatedness derived from different information sources is proposed herein. The proposed RS enables the recommendation of knowledge item balancing: i) historical application data, that is, information regarding how items were related during past projects, and ii) initial relatedness knowledge, which represents the relationships between knowledge items defined by knowledge experts. The proposed RS was developed in collaboration with the Axsens-bte SME, who specialize in consultancy and training in the supply chain, Industry 4.0, and quality requirements management. The proposed RS improved SME KM processes and increased efficiency in terms of exploiting knowledge assets. This demonstrated the ability of the proposed RS to assist SMEs in efficiently and effectively navigating complex information environments.