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Adaptive decision support for shop-floor operators in automotive industry
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
2014 (English)In: Procedia CIRP, 2014, 440-445 p.Conference paper, Published paper (Refereed)
Abstract [en]

Today's operators on factory shop-floors are often not stationed, dealing with a single or few tasks but have increasing responsibilities demanding enhanced skills and knowledge in a production environment where any disturbance must be settled with adequate actions without delay to keep optimum output. To be able to respond to these demands, the operators need dynamic, distributed and adaptive decision support in real-Time, helping them to distinguish decision options and maximizing productivity despite incoming stochastic events. The minimum of time and option for operators to consider appropriate action both during normal production and when facing unexpected or unscheduled events point out the need of adaptive decision support for operators. When initiating this research project the question from the industry partner was the following: In what ways is it possible to support operators in making decisions for optimal productivity? By targeting this problem this paper introduces a novel framework for an adaptive decision-support system enabled by event-driven function blocks and based on decision logics. The proposed decision support systems' ability to adapt to the actual conditions on the shop-floor is validated through a case study, and its capability is compared to the voice message system installed on-site.

Place, publisher, year, edition, pages
2014. 440-445 p.
Keyword [en]
Adaptability, Decision support, Shop-floor operators, Artificial intelligence, Automotive industry, Floors, Manufacture, Productivity, Research, Stochastic systems, Actual conditions, Decision supports, Making decision, Optimal productivity, Production environments, Shop floor, Stochastic events, Adaptive decision support system, Decision support systems
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-167924DOI: 10.1016/j.procir.2014.01.085ISI: 000345458000075Scopus ID: 2-s2.0-84904470152OAI: oai:DiVA.org:kth-167924DiVA: diva2:818110
Conference
47th CIRP Conference on Manufacturing Systems, CMS 2014, 28 April 2014 through 30 April 2014, Windsor, ON
Note

QC 20150608

Available from: 2015-06-08 Created: 2015-05-22 Last updated: 2015-06-08Bibliographically approved

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Wang, Lihui

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf