Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation

Aslan, Ayse; El-Raoui, Hanane; Hanson, Jack; Vasantha, Gokula; Quigley, John; Corney, Jonathan

Abstract

In complex manufacturing industries that are not fully automated and involve human workers it is important to identify deviations from the planned production schedule and locate bottlenecks for improved efficiency. This is not an easy task as it requires data on how workers are actually performing the manufacturing activities. Ultra-wideband (UWB) tags, which are sensors that track movement, can be used to collect this data. Previous research has mostly focused on using these sensors to detect faults and anomalies and to ensure worker safety. However, this paper presents a method for using UWB data to discover process models of manufacturing activities using process mining techniques. We applied our method to a real assembly line with UWB data and found deviations from the prescribed process steps and bottlenecks in the assembly line, which indicated that the first assembly step can take twice as much time compared to other steps.

Citation

Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., & Corney, J. (2023, June). Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation. Presented at 32nd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2023), Porto, Portugal

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 32nd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2023)
• Start Date: Jun 18, 2023
• End Date: Jun 22, 2023
• Acceptance Date: Jan 29, 2023
• Online Publication Date: Aug 24, 2023
• Publication Date: 2024
• Deposit Date: Mar 21, 2023
• Publicly Available Date: Aug 25, 2024
• Publisher: Springer
• Pages: 592-602
• Series Title: Lecture Notes in Mechanical Engineering
• Series ISSN: 2195-4356
• Book Title: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems: Proceedings of FAIM 2023, June 18–22, 2023, Porto, Portugal, Volume 1: Modern Manufacturing
• ISBN: 9783031382406
• DOI: https://doi.org/10.1007/978-3-031-38241-3_67
• Keywords: manufacturing process optimisation, industrial productivity, process mining, indoor positioning systems
• Related Public URLs: https://www.isep.ipp.pt/Page/ViewPage/FAIM