Monitoring and Automating Factories Using Semantic Models
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.
KeywordsSupply Chain Assembly Line SPARQL Query Enterprise Resource Planning System Manufacture Execution System
This work has been supported by the German Federal Ministry of Education and Research (BMBF) in the context of the projects LUCID (grant no. 01IS14019C), SDI-X (no. 01IS15035C) and Industrial Data Space (no. 01IS15054).
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