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Towards a Data-oriented Optimization of Manufacturing Processes – A Real-Time Architecture for the Order Processing as a Basis for Data Analytics Methods

  • Real-time data analytics methods are key elements to overcome the currently rigid planning and improve manufacturing processes by analysing historical data, detecting patterns and deriving measures to counteract the issues. The key element to improve, assist and optimize the process flow builds a virtual representation of a product on the shop-floor - called the digital twin or digital shadow. Using the collected data requires a high data quality, therefore measures to verify the correctness of the data are needed. Based on the described issues the paper presents a real-time reference architecture for the order processing. This reference architecture consists of different layers and integrates real-time data from different sources as well as measures to improve the data quality. Based on this reference architecture, deviations between plan data and feedback data can be measured in real-time and countermeasures to reschedule operations can be applied.

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Metadaten
Verfasserangaben:Günther SchuhORCiDGND, Matthias BlumGND
DOI:https://doi.org/10.5220/0006326002570264
ISBN:978-989-758-247-9
Titel des übergeordneten Werkes (Englisch):Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017); Bd. 1
Verlag:SCITEPRESS - Science and Technology Publications
Ort:Setúbal, Portugal
Herausgeber*in:Slimane Hammoudi, Michal Smialek, Olivier Camp, Joaquim Filipe
Dokumentart:Konferenzveröffentlichung
Sprache:Englisch
Datum der Veröffentlichung (online):24.07.2023
Datum der Erstveröffentlichung:31.10.2017
Datum der Freischaltung:31.08.2023
Freies Schlagwort / Tag:Digitaler Schatten; Echtzeit
data analytics; digital shadow; digital twin; real-time architecture
GND-Schlagwort:Industrie 4.0GND
Erste Seite:257
Letzte Seite:264
FIR-Nummer:SV6864
Konferenzname:International Conference on Enterprise Information Systems
Konferenzort:Porto, Portugal
Konferenzzeitraum:26.04.2017-29.04.2017
Institut / Bereiche des FIR:FIR e. V. an der RWTH Aachen
Produktionsmanagement
DDC-Klassifikation:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften