Inferring Missing Event Log Data from IoT Sensor Data - A Case Study in Manufacturing

Alexander Seeliger, Markus Schreiber, Florian Giger, Joachim Metternich, Max Mühlhäuser

Abstract: Market forces such as rising amounts of product variants and decreasing batch sizes lead to higher complexity in manufacturing processes. Therefore, production management’s demand for data-based process transparency is growing continuously as well as the number of companies turning to process mining to address these challenges. Information systems in production usually do not provide readily available event log data for the analysis. This paper investigates several techniques for inferring missing event log data in production processes by extracting events with timestamps from sensor data from machines and link them to process instances. We demonstrate the effectiveness of our approach in a real-world manufacturing environment. The evaluation of the resulting event logs revealed that the quality of the timestamps and the assignment of the actual process instances is sufficient to apply process mining techniques that would have required both greater effort and higher cost intensity if a traceability system had been implemented.