Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 239-248 |
| Seitenumfang | 10 |
| Fachzeitschrift | Proceedings of the Conference on Production Systems and Logistics |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 1st Conference on Production Systems and Logistics, CPSL 2020 - Stellenbosch, Südafrika Dauer: 17 März 2020 → 20 März 2020 |
Abstract
Logistics performance becomes an ever more important strategic factor for manufacturing companies to obtain a competitive advantage. Yet, numerous companies fail to meet their own corporate goals or customer requirements. One of the most important objectives in logistics is speed in terms of short delivery times which are mainly determined by the production throughput times. Derivation of effective improvement measures requires a profound understanding of logistic cause-effect relationships. At a time of increasing digitalization, an increasing amount of feedback data is available that offers great potentials to discover novel insights. Yet, the vast amount of data can also be overwhelming and result in unsystematic and ineffective analysis of less meaningful data. Therefore, in this paper a systematic procedure is presented that allows data-based identification of throughput time potentials in production departments. The quantitative analysis framework is based on a generic driver tree structuring the influencing factors on throughput time. The approach will boost the understanding about logistics relations and will particularly help SMEs to focus on the most relevant influencing factors and data. Furthermore, it provides a basis for future more advanced information systems that will help companies to continuously improve their logistics performance and adapt their supply chains to ever-changing conditions.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Ingenieurwesen (insg.)
- Maschinenbau
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Technologie- und Innovationsmanagement
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Strategie und Management
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in: Proceedings of the Conference on Production Systems and Logistics, 2020, S. 239-248.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Data-based identification of throughput time potentials in production departments
AU - Härtel, Lasse
AU - Nyhuis, Peter
PY - 2020
Y1 - 2020
N2 - Logistics performance becomes an ever more important strategic factor for manufacturing companies to obtain a competitive advantage. Yet, numerous companies fail to meet their own corporate goals or customer requirements. One of the most important objectives in logistics is speed in terms of short delivery times which are mainly determined by the production throughput times. Derivation of effective improvement measures requires a profound understanding of logistic cause-effect relationships. At a time of increasing digitalization, an increasing amount of feedback data is available that offers great potentials to discover novel insights. Yet, the vast amount of data can also be overwhelming and result in unsystematic and ineffective analysis of less meaningful data. Therefore, in this paper a systematic procedure is presented that allows data-based identification of throughput time potentials in production departments. The quantitative analysis framework is based on a generic driver tree structuring the influencing factors on throughput time. The approach will boost the understanding about logistics relations and will particularly help SMEs to focus on the most relevant influencing factors and data. Furthermore, it provides a basis for future more advanced information systems that will help companies to continuously improve their logistics performance and adapt their supply chains to ever-changing conditions.
AB - Logistics performance becomes an ever more important strategic factor for manufacturing companies to obtain a competitive advantage. Yet, numerous companies fail to meet their own corporate goals or customer requirements. One of the most important objectives in logistics is speed in terms of short delivery times which are mainly determined by the production throughput times. Derivation of effective improvement measures requires a profound understanding of logistic cause-effect relationships. At a time of increasing digitalization, an increasing amount of feedback data is available that offers great potentials to discover novel insights. Yet, the vast amount of data can also be overwhelming and result in unsystematic and ineffective analysis of less meaningful data. Therefore, in this paper a systematic procedure is presented that allows data-based identification of throughput time potentials in production departments. The quantitative analysis framework is based on a generic driver tree structuring the influencing factors on throughput time. The approach will boost the understanding about logistics relations and will particularly help SMEs to focus on the most relevant influencing factors and data. Furthermore, it provides a basis for future more advanced information systems that will help companies to continuously improve their logistics performance and adapt their supply chains to ever-changing conditions.
KW - Data analysis
KW - Logistics
KW - Production controlling
KW - Throughput time
UR - http://www.scopus.com/inward/record.url?scp=85164025120&partnerID=8YFLogxK
U2 - 10.15488/9665
DO - 10.15488/9665
M3 - Conference article
AN - SCOPUS:85164025120
SP - 239
EP - 248
JO - Proceedings of the Conference on Production Systems and Logistics
JF - Proceedings of the Conference on Production Systems and Logistics
T2 - 1st Conference on Production Systems and Logistics, CPSL 2020
Y2 - 17 March 2020 through 20 March 2020
ER -