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

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.