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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-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.

Original languageEnglish
Title of host publication2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
Number of pages8
PublisherIEEE
Publication date2023
ISBN (Electronic)9798350339918
DOIs
Publication statusPublished - 2023
Event28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 - Sinaia, Romania
Duration: 12. Sept 202315. Sept 2023

Conference

Conference28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023
Country/TerritoryRomania
CitySinaia
Period12/09/202315/09/2023
SeriesIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2023-September
ISSN1946-0740

Keywords

  • manufacturing systems
  • predictive process monitoring
  • process mining
  • remaining cycle time prediction

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