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A tool to predict physical workload and task times from workstation layout design data

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  • Michael A. Greig
  • Judy Village
  • Filippo A. Salustri
  • Saeed Zolfaghari
  • W. Patrick Neumann

Abstract

This paper presents the development and proof of concept of a tool to predict worker and system performance using inputs of work element descriptions and hand locations from a seated, light assembly workstation layout. Tool inputs can be obtained in the design stage. Tool outputs include human factors (shoulder load, hand movement, reach zone acceptability) and system (element time and cycle time) information. Shoulder loads are predicted from two-dimensional shoulder models created from a digital human model. The tool is demonstrated on a previous observation-based assessment of a workstation redesign. Results reflected the findings of the observation assessment, but also provided more work cycle information as well as cumulative, work shift information. The tool enables prediction of workload and task performance times from design stage parameters without the need of an ergonomist. It can be used to predict critical components of the layout and plan workflow based on worker, workstation and task information. The tool is available for free downloaded at: www.researchgate.net/project/Workstation-Efficiency-Evaluator-WEE-Tool.

Suggested Citation

  • Michael A. Greig & Judy Village & Filippo A. Salustri & Saeed Zolfaghari & W. Patrick Neumann, 2018. "A tool to predict physical workload and task times from workstation layout design data," International Journal of Production Research, Taylor & Francis Journals, vol. 56(16), pages 5306-5323, August.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:16:p:5306-5323
    DOI: 10.1080/00207543.2017.1378827
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    Cited by:

    1. Dolgui, Alexandre & Hashemi-Petroodi, S. Ehsan & Kovalev, Sergey & Kovalyov, Mikhail Y., 2021. "Profitability of a multi-model manufacturing line versus multiple dedicated lines," International Journal of Production Economics, Elsevier, vol. 236(C).

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