Monitoring and automating factories using semantic models

Abstract

Keeping factories running at any time is a critical task for every manufacturing enterprise. Optimizing the flows of goods and services inside and between factories is a challenge that attracts much attention in research and business. The idea to fully describe a factory in a digital form to improve decision making is called a virtual factory. While promising virtual factory frameworks have been proposed, their semantic models lack depth and suffer from limited expressiveness. We propose an enhanced semantic model of a factory, which enables views spanning from the high level of supply chains to the low level of machines on the shop floor. The model includes a mapping to relational production databases to support federated queries on different legacy systems in use. We evaluate the model in a production line use case, demonstrating that it can be used for typical factory tasks, such as assembly line identification or machine availability checks.