Within the increasing digitalization, the widespread application of modern information and communication technologies and the technological ability to systematically and comprehensively capture and store data allow to build data storages of unprecedented size and quality. The evaluation and efficient use of the implicit knowledge in the data to support decision-making processes is becoming increasingly significant in manufacturing companies. Thus, new requirements arise for the qualification and competence development to efficiently solve engineering applications and issues in manufacturing and assembly with advanced data-driven methods. This paper presents the contribution of a qualification concept for Machine Learning in industrial production that has been realised within a recent research project funded by the Federal Ministry of Education and Research. This concept has been designed and validated within the university curriculum for graduate students in mechanical and industrial engineering, computer science and statistics. Taking into account the current challenges in manufacturing and assembly, the contribution of this enhanced interdisciplinary competence development can be considered quite significant. The results, findings, and future enhancements are presented within this paper.
Knowing the bottleneck is one of the keys to improving a production system. The active period method is one approach to detect shifting bottlenecks that most other bottleneck detection methods have problems with. Yet, like many other methods, these detections are limited to detecting the past and present bottlenecks. In this paper, we combined the active period method with the buffer inventories and free buffer spaces of the adjacent inventories to statistically predict not only an upcoming change of the bottleneck, but also where the bottleneck will move to.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.