Modelling and Improving Maintenance Decisions: Having Foresight with Simulation and Artificial Intelligence

Machine breakdowns are one of the main sources of disruption and throughput fluctuation in highly automated production facilities. One element in reducing this disruption is ensuring that the maintenance team responds correctly to machine failures. It is, however, difficult to determine the current practice employed by the maintenance team, let alone suggest improvements to it. ‘Knowledge based improvement’ is a methodology that aims to address this issue, by (a) eliciting knowledge on current practice, (b) evaluating that practice and (c) looking for improvements. The methodology, based on visual interactive simulation and artificial intelligence methods, and its application to a Ford engine assembly facility are described.

• Record URL:
  • https://doi.org/10.4271/2002-01-0471
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • ROBINSON, S
  • Alifantis, A
  • Hurrion, R
  • Edwards, J
  • Ladbrook, J
  • Waller, T
• Conference:
  • SAE 2002 World Congress & Exhibition
  • Location: Detroit Michigan, United States
  • Date: 2002-3-4 to 2002-3-7
• Publication Date: 2001-9-24

Language

• English

Media Info

• Media Type: Web
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Automation; Equipment assemblies; Production; Simulation
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01800234
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2002-01-0471
• Files: TRIS, SAE
• Created Date: Dec 9 2021 10:21AM