Longard, Lukas ; Bardy, Sebastian ; Metternich, Joachim
Hrsg.: Herberger, D. ; Hübner, M. (2022)
Towards a Data-driven Performance Management in Digital Shop Floor Management.
3rd Conference on Production Systems and Logistics. Vancouver, Canada (17th-20th May 2022)
doi: 10.15488/12185
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Key performance indicators (KPIs) are crucial for measuring and managing the performance of industrial processes. They are used to detect deviations in processes, enabling opportunities to improve manufacturing processes within the three dimensions time, quality, and cost. In this context, the timeliness of information plays a decisive role in the success of measures since delayed information availability can leave decision makers with no time to react. With the introduction of digitization and industry 4.0, increasing amounts of data become available. They can be used to accelerate problem detection and shortening reaction times to define appropriate actions. This paper presents a data-driven performance management approach integrated in digital shop floor management (dSFM). If a deviation is detected in one process, KPIs of subsequent processes (horizontal level) as well as subordinate levels (vertical level) are checked for correlations and, if present, the associated team is notified by an automatic warning through the dSFM system. Based on the identified correlations, the team discusses the deviations and defines suitable countermeasures. The aim of this approach is to identify deviations more quickly and to quantify their impacts, thus giving shop floor managers the ability to react in time.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Herausgeber: | Herberger, D. ; Hübner, M. |
| Autor(en): | Longard, Lukas ; Bardy, Sebastian ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Towards a Data-driven Performance Management in Digital Shop Floor Management |
| 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 |
| Jahrgang/Volume einer Zeitschrift: | 3rd Conference on Product |
| Buchtitel: | Proceedings of the Conference on Production Systems and Logistics: CPSL 2022 |
| Veranstaltungstitel: | 3rd Conference on Production Systems and Logistics |
| Veranstaltungsort: | Vancouver, Canada |
| Veranstaltungsdatum: | 17th-20th May 2022 |
| DOI: | 10.15488/12185 |
| Kurzbeschreibung (Abstract): | Key performance indicators (KPIs) are crucial for measuring and managing the performance of industrial processes. They are used to detect deviations in processes, enabling opportunities to improve manufacturing processes within the three dimensions time, quality, and cost. In this context, the timeliness of information plays a decisive role in the success of measures since delayed information availability can leave decision makers with no time to react. With the introduction of digitization and industry 4.0, increasing amounts of data become available. They can be used to accelerate problem detection and shortening reaction times to define appropriate actions. This paper presents a data-driven performance management approach integrated in digital shop floor management (dSFM). If a deviation is detected in one process, KPIs of subsequent processes (horizontal level) as well as subordinate levels (vertical level) are checked for correlations and, if present, the associated team is notified by an automatic warning through the dSFM system. Based on the identified correlations, the team discusses the deviations and defines suitable countermeasures. The aim of this approach is to identify deviations more quickly and to quantify their impacts, thus giving shop floor managers the ability to react in time. |
| Freie Schlagworte: | Data mining, key performance indicators, machine learning, Performance management, Shop floor management |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) |
| Hinterlegungsdatum: | 29 Jul 2022 07:46 |
| Letzte Änderung: | 06 Okt 2022 08:33 |
| PPN: | 497713519 |
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| Suche nach Titel in: | TUfind oder in Google |
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