Predictive Process Monitoring for Prediction of Remaining Cycle Time in Automated Manufacturing: A Case Study

Jonas Friederich*, Jonas Kristoffer Lindeløv, Sanja Lazarova-Molnar

*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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Abstract

Predicting remaining cycle times of products in manufacturing systems is critical to ensure on-time deliveries to customers, schedule resources and actions for expected order completions, and address excessive production stops proactively rather than retroactively. Recent advances in Predictive Process Monitoring (PPM), a sub-discipline of Process Mining, enable the use of machine learning to predict remaining cycle times based on event data. We apply PPM to the automated manufacturing domain and demonstrate the approach using a case study from a water meter manufacturer. For prediction of remaining cycle times, PPM relies on regression methods, such as Decision Trees, Random Forests, and Gradient Boosting Machines based on event data. We compare the prediction accuracy of these methods and show that PPM can deliver relevant insights for production lines without imposing extensive data requirements.

OriginalsprogEngelsk
Titel2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
Antal sider8
ForlagIEEE
Publikationsdato2023
ISBN (Elektronisk)9798350339918
DOI
StatusUdgivet - 2023
Begivenhed28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 - Sinaia, Rumænien
Varighed: 12. sep. 202315. sep. 2023

Konference

Konference28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023
Land/OmrådeRumænien
BySinaia
Periode12/09/202315/09/2023
NavnIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Vol/bind2023-September
ISSN1946-0740

Bibliografisk note

Publisher Copyright:
© 2023 IEEE.

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