An article recommender system for smes in digital transformation

In the last decade, the industrial environment has witnessed a mode shift driven by the fourth industrial revolution (Industry 4.0). This revolution is especially characterized by the integration of digital technologies into various fields of the industry, ranging from business administration to manufacturing processes. Nevertheless, this transformation presents significant challenges, especially for Small and Medium-sized Enterprises (SMEs). These challenges range from a lack of specialty in the field to cultural and organizational resistance toward digital change. The aim of this thesis is to address these challenges in the field of digital transformation by creating a bridge between industrial practice and academic research. While academia is extensively exploring the theoretical attributes and capacities of Industry 4.0, there is a disconnection when it comes to practical implementations by SMEs. The main reason behind this is that accessibility to theoretical research is limited for the companies. To tackle this incompatibility, this thesis adopts a two-pronged approach: Firstly, it involves a comprehensive systematic mapping study of the Industry 4.0 literature. Secondly, it suggests a tailored recommender engine for the use of SMEs. This engine is capable of presenting inspiring paper suggestions to SMEs throughout their digital transformation journey. Throughout our research, we utilize advanced Natural Language Processing (NLP) approaches for analyzing and interpreting large volumes of text data.