Improving Production Performance Through Multi-Plant Cross Learning

The advancement in Web-/Internet-based technologies and applications in manufacturing sector has increased the tilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to enhance the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the "best" feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increases of throughput are predicted.