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Augmented Go & See: An approach for improved bottleneck identification in production lines

Hofmann, C. 1; Staehr, T. 1; Cohen, S. 1; Stricker, N. 1; Haefner, B. 1; Lanza, G. 1
1 Karlsruher Institut für Technologie (KIT)

Abstract:

Bottlenecks in production lines are often shifting and thus hard to identify. They lead to decreased output, longer throughput times and higher work in progress. Go & See is a well-established Lean practice where managers go to the shop floor to see the problems first hand. Mixed reality is a promising technology to improve transparency in complex production environments. Until recently, mixed reality applications have been very demanding in terms of computing power requiring high performance hardware. This paper presents an approach for real-time KPI visualization using mixed reality for bottleneck identification in production lines relying on the bring-your-own device principle. The developed application uses image recognition to identify work stations and visualizes cycle times and work in progress in augmented reality. With this additional information, it is possible to discern different root causes for bottlenecks, for example systematically higher or varying cycle times due to breakdowns. This solution can be classified according to the acatech industry 4.0 maturity model as a level 3 - transparency - application. It could be shown that the identification of bottlenecks and underlying reasons has been improved compared to standard Go & See.


Verlagsausgabe §
DOI: 10.5445/IR/1000095788
Originalveröffentlichung
DOI: 10.1016/j.promfg.2019.03.023
Scopus
Zitationen: 19
Dimensions
Zitationen: 25
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Young Investigator Network (YIN)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 2351-9789
KITopen-ID: 1000095788
Erschienen in 9th Conference on Learning Factories, CLF 2019; Braunschweig; Germany; 26 March 2019 through 28 March 2019. Ed.: C. Herrmann
Verlag Elsevier
Seiten 148-154
Serie Procedia Manufacturing ; 31
Schlagwörter Mixed reality, Learning factory, bottlenecks
Nachgewiesen in Scopus
Dimensions
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