Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 18-23 |
| Seitenumfang | 6 |
| Fachzeitschrift | Procedia Manufacturing |
| Jahrgang | 28 |
| Frühes Online-Datum | 25 Jan. 2019 |
| Publikationsstatus | Veröffentlicht - 2019 |
| Veranstaltung | 7th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2018) - Nantes, Frankreich Dauer: 8 Okt. 2018 → 10 Okt. 2018 Konferenznummer: 7 |
Abstract
Higher customer's expectations and new technological developments have resulted in an increased complexity of manufacturing systems. Consequently, work planning and production control have become more crucial than ever to the success of companies. Adaptive work planning and production control approaches offer high potential with respect to flexibility and complexity management. However, the approaches find little use in practice, since the automatic acquisition of all potentially relevant information with additional sensors is cost-intensive and not feasible in many cases. At the same time, the potential of already existing production data, that is stored in Manufacturing Execution Systems (MES), remains untapped. This paper presents a method for automated production data feedback that guarantees a systematic update of production plan data only based on MES data. The functionality of the method is validated exemplarily at a thread manufacturer. The results reveal that an MES can provide a sufficient database for adaptive work planning and production control approaches. Moreover, the developed method can be applied to identify suitable workstations and measurement categories for additional sensor implementation.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Procedia Manufacturing, Jahrgang 28, 2019, S. 18-23.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Automated production data feedback for adaptive work planning and production control
AU - Denkena, Berend
AU - Dittrich, Marc-andré
AU - Wilmsmeier, Sören
N1 - Conference code: 7
PY - 2019
Y1 - 2019
N2 - Higher customer's expectations and new technological developments have resulted in an increased complexity of manufacturing systems. Consequently, work planning and production control have become more crucial than ever to the success of companies. Adaptive work planning and production control approaches offer high potential with respect to flexibility and complexity management. However, the approaches find little use in practice, since the automatic acquisition of all potentially relevant information with additional sensors is cost-intensive and not feasible in many cases. At the same time, the potential of already existing production data, that is stored in Manufacturing Execution Systems (MES), remains untapped. This paper presents a method for automated production data feedback that guarantees a systematic update of production plan data only based on MES data. The functionality of the method is validated exemplarily at a thread manufacturer. The results reveal that an MES can provide a sufficient database for adaptive work planning and production control approaches. Moreover, the developed method can be applied to identify suitable workstations and measurement categories for additional sensor implementation.
AB - Higher customer's expectations and new technological developments have resulted in an increased complexity of manufacturing systems. Consequently, work planning and production control have become more crucial than ever to the success of companies. Adaptive work planning and production control approaches offer high potential with respect to flexibility and complexity management. However, the approaches find little use in practice, since the automatic acquisition of all potentially relevant information with additional sensors is cost-intensive and not feasible in many cases. At the same time, the potential of already existing production data, that is stored in Manufacturing Execution Systems (MES), remains untapped. This paper presents a method for automated production data feedback that guarantees a systematic update of production plan data only based on MES data. The functionality of the method is validated exemplarily at a thread manufacturer. The results reveal that an MES can provide a sufficient database for adaptive work planning and production control approaches. Moreover, the developed method can be applied to identify suitable workstations and measurement categories for additional sensor implementation.
KW - Adaptive production planning
KW - Digital manufacturing system
KW - Production data
UR - http://www.scopus.com/inward/record.url?scp=85072562849&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2018.12.004
DO - 10.1016/j.promfg.2018.12.004
M3 - Conference article
VL - 28
SP - 18
EP - 23
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
T2 - 7th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2018)
Y2 - 8 October 2018 through 10 October 2018
ER -