Record link: 
http://hdl.handle.net/20.500.12708/94772
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Title: 
A conceptual model for developing a smart process control system
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Citation: 
Weihrauch, D., Schindler, P. A., & Sihn, W. (2017). A conceptual model for developing a smart process control system. In Proceedings of 11th CIRP Conference on Conference on Intelligent Computation in Manufacturing Engineering (pp. 386–391). Elsevier. https://doi.org/10.1016/j.procir.2017.12.230
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Publisher DOI: 
10.1016/j.procir.2017.12.230
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Weihrauch, Dennis 
Schindler, Paul Anton 
Sihn, Wilfried 
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Organisational Unit: 
E330-02 - Forschungsbereich Betriebstechnik, Systemplanung und Facility Management 
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Published in: 
Proceedings of 11th CIRP Conference on Conference on Intelligent Computation in Manufacturing Engineering 
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Volume: 
67
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Date (published): 
2017
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Event name: 
11th CIRP Conference on Intelligent Computation in Manufacturing Engineering 
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Event date: 
19-Jul-2017 - 21-Jul-2017
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Event place: 
Naples, Italy
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Number of Pages: 
6
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Publisher: 
Elsevier
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Peer reviewed: 
Yes
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Keywords: 
General Materials Science; Decision Support; Manufacturing Execution System; Smart Process Control; Requirements Analysis
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Abstract: 
Current Manufacturing Execution Systems (MES) are not supporting a full integration into overall processes across the supply chain. Thus, optimization is limited to single areas. The SemI40 project is aimed at developing an integrated concept of Smart Process Control System (SPCS), which enhances the overall agility and productivity. The system, therefore, autonomously acquires and interprets process data to allow product individual optimization and enhancing logistics management. It also provides full traceability across the supply chain in real-time and allows model based process simulation and decision making support. The concept is developed based on requirements elicitation in cooperation with industry partners and combines state-of-the-art technologies with current trends, like vertical integration, big data and machine learning.
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Science Branch: 
Wirtschaftswissenschaften
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Appears in Collections:
Conference Paper

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