Accelerate Business Value in Manufacturing with Advanced Analytics

Recent developments in smart manufacturing enable convergence between the digital and physical worlds of modern manufacturing facilities. This evolution is, however, far from trivial and thorough research and investigation needs to be conducted regarding dynamic connectivity of assets and implementation of data driven analytics, which provides deeper insight into the operational processes. Scania in Södertälje is the object for the case study, with the aim of presenting recommendations for future research projects within smart manufacturing. Also, PTC in Boston, Massachusetts, has contributed with expertise and knowledge in the matter. Addressing the problems regarding what future actions to pursue and what methodologies to invest research in, this thesis base its analysis and discussion on recent research papers and documentation from international standardization organizations. The analysis identified and categorized the present problems into company wide development architecture, information modeling, communication structures, computational modules and collaboration with other companies and organizations. Ultimately, four different project recommendations are presented. The first suggestion includes development of a framework for using Reference Architecture Model Industrie 4.0 (RAMI 4.0) in company wide development and research, as a way of categorizing systems and functions. Secondly, a suggestion for generic modeling of assets was presented. Assets could be anything from machining tools, to analytics software and even operational personell. Thirdly, the thesis recommends that Scania investigates dynamic communication structures, which breaks the traditional hierarchical view on information infrastructure across the company. Lastly, a project regarding non-intrusive, online computational modules was discussed. This suggestion was, however, not in particular detail, as the thesis concluded that the foundation for data driven methods is of the highest importance, rather than suggesting actual analytics algorithms