Seeliger, Alexander ; Schreiber, Markus ; Giger, Florian ; Metternich, Joachim ; Mühlhäuser, Max
Hrsg.: Marrella, Andrea ; Resinas, Manuel ; Jans, Mieke ; Rosemann, Michael (2024)
Inferring Missing Event Log Data from IoT Sensor Data - A Case Study in Manufacturing.
22nd International Conference on Business Process Management (BPM 2024. Krakow, Poland (01.09.2024-06.09.2024)
doi: 10.1007/978-3-031-70418-5_14
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Herausgeber: | Marrella, Andrea ; Resinas, Manuel ; Jans, Mieke ; Rosemann, Michael |
| Autor(en): | Seeliger, Alexander ; Schreiber, Markus ; Giger, Florian ; Metternich, Joachim ; Mühlhäuser, Max |
| Art des Eintrags: | Bibliographie |
| Titel: | Inferring Missing Event Log Data from IoT Sensor Data - A Case Study in Manufacturing |
| Sprache: | Englisch |
| Publikationsjahr: | 30 August 2024 |
| Verlag: | Springer |
| Buchtitel: | Business Process Management Forum |
| Reihe: | Lecture Notes in Business Information Processing |
| Band einer Reihe: | 526 |
| Veranstaltungstitel: | 22nd International Conference on Business Process Management (BPM 2024 |
| Veranstaltungsort: | Krakow, Poland |
| Veranstaltungsdatum: | 01.09.2024-06.09.2024 |
| DOI: | 10.1007/978-3-031-70418-5_14 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation |
| Hinterlegungsdatum: | 27 Nov 2024 12:57 |
| Letzte Änderung: | 27 Nov 2024 12:57 |
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