Schreiber, Markus ; Metternich, Joachim (2022)
Traceability System's Impact On Process Mining in Production.
doi: 10.15488/12136
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
From the perspective of manufacturing companies, data handling is gaining more attention as it is becoming a strategic resource in digital ecosystems. 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. The increased use of process mining has uncovered many documented data quality issues that hamper output quality. In response to data usage and quality problems, research in the field of Big Data has turned to sophisticated data value chains as a promising approach to optimize data usage. This paper presents the application of the data value chain concept on a manufacturing use case, delivering an assessment of traceability systems and their effect on data quality issues. This assessment reviews commonly known quality issues and investigates how traceability systems can influence and facilitate better data quality. The results support manufacturing companies in their use of traceability systems to improve the reliability of their process mining input data and, hence, their output performance indicators to meet the demand for more data-based process transparency.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Schreiber, Markus ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Traceability System's Impact On Process Mining in Production |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Hannover |
| Verlag: | publish-Ing |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of the Conference on Production Systems and Logistics: CPSL 2022 |
| Buchtitel: | Proceedings of the Conference on Production Systems and Logistics: CPSL 2022 |
| DOI: | 10.15488/12136 |
| Kurzbeschreibung (Abstract): | From the perspective of manufacturing companies, data handling is gaining more attention as it is becoming a strategic resource in digital ecosystems. 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. The increased use of process mining has uncovered many documented data quality issues that hamper output quality. In response to data usage and quality problems, research in the field of Big Data has turned to sophisticated data value chains as a promising approach to optimize data usage. This paper presents the application of the data value chain concept on a manufacturing use case, delivering an assessment of traceability systems and their effect on data quality issues. This assessment reviews commonly known quality issues and investigates how traceability systems can influence and facilitate better data quality. The results support manufacturing companies in their use of traceability systems to improve the reliability of their process mining input data and, hence, their output performance indicators to meet the demand for more data-based process transparency. |
| Freie Schlagworte: | data quality, data value chain, process mining, data-based process transparency, traceability system |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Management industrieller Produktion |
| Hinterlegungsdatum: | 28 Jul 2022 05:40 |
| Letzte Änderung: | 28 Jul 2022 05:40 |
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