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.
|2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
|Udgivet - 2023
|28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 - Sinaia, Rumænien
Varighed: 12. sep. 2023 → 15. sep. 2023
|28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023
|12/09/2023 → 15/09/2023
|IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
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