Details
| Translated title of the contribution | Applications of machine learning in manufacturing from a job order and product perspective an overview |
|---|---|
| Original language | German |
| Pages (from-to) | 358-362 |
| Number of pages | 5 |
| Journal | ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb |
| Volume | 116 |
| Issue number | 5 |
| Early online date | 19 May 2021 |
| Publication status | Published - 31 May 2021 |
Abstract
Machine learning as a subfield of artificial intelligence can contribute to accelerating the design of processes in manufacturing, reducing cycle times, improving quality, and making better use of production capacities. This article provides a systematized overview of machine learning applications for product- and order-related processes and supports practitioners in identifying application areas in a focused manner and exploiting value-added potential.
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
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In: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, Vol. 116, No. 5, 31.05.2021, p. 358-362.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - Anwendungen des maschinellen Lernens in der Produktion aus Auftrags- und Produktsicht
T2 - Ein Überblick
AU - Denkena, Berend
AU - Dittrich, Marc André
AU - Noske, Hendrik
AU - Kramer, Kathrin
AU - Schmidt, Matthias
PY - 2021/5/31
Y1 - 2021/5/31
N2 - Machine learning as a subfield of artificial intelligence can contribute to accelerating the design of processes in manufacturing, reducing cycle times, improving quality, and making better use of production capacities. This article provides a systematized overview of machine learning applications for product- and order-related processes and supports practitioners in identifying application areas in a focused manner and exploiting value-added potential.
AB - Machine learning as a subfield of artificial intelligence can contribute to accelerating the design of processes in manufacturing, reducing cycle times, improving quality, and making better use of production capacities. This article provides a systematized overview of machine learning applications for product- and order-related processes and supports practitioners in identifying application areas in a focused manner and exploiting value-added potential.
KW - Machine learning
KW - Order-related processes
KW - Product-related processes
KW - Production control
KW - Production planning
KW - Use cases
UR - http://www.scopus.com/inward/record.url?scp=85106971450&partnerID=8YFLogxK
U2 - 10.1515/zwf-2021-0068
DO - 10.1515/zwf-2021-0068
M3 - Artikel
AN - SCOPUS:85106971450
VL - 116
SP - 358
EP - 362
JO - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
JF - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
SN - 0947-0085
IS - 5
ER -