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A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

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dc.contributor.author Papapanagiotou, Petros
dc.contributor.author Vaughan, James
dc.contributor.author Smola, Filip
dc.contributor.author Fleuriot, Jacques
dc.date.accessioned 2020-12-24T19:10:55Z
dc.date.available 2020-12-24T19:10:55Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70734
dc.description.abstract We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Case Studies of Artificial Intelligence, Business Intelligence, Analytics Technologies for Industry Platforms
dc.subject analytics
dc.subject formal methods
dc.subject manufacturing
dc.subject simulation
dc.subject workflow
dc.title A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows
dc.identifier.doi 10.24251/HICSS.2021.122
prism.startingpage 1001
Appears in Collections: Case Studies of Artificial Intelligence, Business Intelligence, Analytics Technologies for Industry Platforms


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