Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 659-668 |
| Seitenumfang | 10 |
| Fachzeitschrift | Production Engineering |
| Jahrgang | 8 |
| Ausgabenummer | 5 |
| Publikationsstatus | Veröffentlicht - 27 Apr. 2014 |
Abstract
In this article, a method for a scalable autonomous data acquisition for an analysis and optimization of production systems based on interpretation of the material flow within small and medium-sized manufacturing enterprises is presented. The data is acquired locally and combined centrally to interpret the material flow as a basis for the optimization of the material flow as well as individual processes. When it is not completely observable for efficiency reasons or due to technical restrictions, one can also reconstruct relevant but unobservable system behavior based on system knowledge and actual measurements. A validation of the method is carried out in a company maintaining engines. The application of the model shows that with the presented method it is possible to reduce buffers in the production, optimize transportation routes and reduce waiting and therefore cycle times in job shop productions for an increasing productivity.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Maschinenbau
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Production Engineering, Jahrgang 8, Nr. 5, 27.04.2014, S. 659-668.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Interpretation and optimization of material flow via system behavior reconstruction
AU - Denkena, Berend
AU - Dengler, Barbara
AU - Doreth, Karl
AU - Krull, Claudia
AU - Horton, Graham
N1 - Funding information: Acknowledgments The results presented have been developed within the scope of the Federal Ministry for Education and Research (BMBF) funded project ‘‘OptiBox—Automatisierte Optimierung von Produktionssystemen durch mobile und selbstlernende Analys-eeinheiten’’ (Grant Number: 02PK3029).
PY - 2014/4/27
Y1 - 2014/4/27
N2 - In this article, a method for a scalable autonomous data acquisition for an analysis and optimization of production systems based on interpretation of the material flow within small and medium-sized manufacturing enterprises is presented. The data is acquired locally and combined centrally to interpret the material flow as a basis for the optimization of the material flow as well as individual processes. When it is not completely observable for efficiency reasons or due to technical restrictions, one can also reconstruct relevant but unobservable system behavior based on system knowledge and actual measurements. A validation of the method is carried out in a company maintaining engines. The application of the model shows that with the presented method it is possible to reduce buffers in the production, optimize transportation routes and reduce waiting and therefore cycle times in job shop productions for an increasing productivity.
AB - In this article, a method for a scalable autonomous data acquisition for an analysis and optimization of production systems based on interpretation of the material flow within small and medium-sized manufacturing enterprises is presented. The data is acquired locally and combined centrally to interpret the material flow as a basis for the optimization of the material flow as well as individual processes. When it is not completely observable for efficiency reasons or due to technical restrictions, one can also reconstruct relevant but unobservable system behavior based on system knowledge and actual measurements. A validation of the method is carried out in a company maintaining engines. The application of the model shows that with the presented method it is possible to reduce buffers in the production, optimize transportation routes and reduce waiting and therefore cycle times in job shop productions for an increasing productivity.
KW - Hidden non-Markovian model
KW - Interpretation
KW - Job shop
KW - Material flow
KW - Optimization
KW - SME
UR - http://www.scopus.com/inward/record.url?scp=84910119795&partnerID=8YFLogxK
U2 - 10.1007/s11740-014-0545-z
DO - 10.1007/s11740-014-0545-z
M3 - Article
AN - SCOPUS:84910119795
VL - 8
SP - 659
EP - 668
JO - Production Engineering
JF - Production Engineering
SN - 0944-6524
IS - 5
ER -