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AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

Stauder, Max; Kühl, Niklas ORCID iD icon 1
1 Karlsruhe Service Research Institute (KSRI), Karlsruher Institut für Technologie (KIT)

Abstract:

Customers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms based on a stable order sequence for mixed-model assembly lines.
A major threat to this development is sequence scrambling, triggered by both operational and product-related root causes. Despite the introduction of just-in-time and fixed production times, the problem of sequence scrambling remains partially unresolved in the automotive industry. Negative downstream effects range from disruptions in the just-in-sequence supply chain to a stop of the production process. A precise prediction of sequence deviations at an early stage allows the introduction of counteractions to stabilize the sequence before disorder emerges. While procedural causes are widely addressed in research, the work at hand requires a different perspective involving a product-related view.
Built on unique data from a real-world global automotive manufacturer, a supervised classification model is trained and evaluated. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000136184
Originalveröffentlichung
DOI: 10.1007/s10696-021-09430-x
Scopus
Zitationen: 7
Web of Science
Zitationen: 6
Dimensions
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 0920-6299, 1572-9370, 1936-6582, 1936-6590
KITopen-ID: 1000136184
Erschienen in Flexible services and manufacturing journal
Verlag Springer Verlag
Band 34
Seiten 709-747
Vorab online veröffentlicht am 15.08.2021
Schlagwörter In-line vehicle sequencing, Sequence scrambling, Supervised classification, Artificial intelligence, Concept drift
Nachgewiesen in Web of Science
Dimensions
Scopus
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