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Optimizing Human Performance to Enhance Safety: A Case Study in an Automotive Plant

Author

Listed:
  • Maria Chiara Leva

    (School of Environmental Health, Technological University Dublin, D07ADY7 Dublin, Ireland)

  • Micaela Demichela

    (SAfeR—Centro Studi su Sicurezza, Affidabilitàe Rischi, Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, 10129 Torino, Italy)

  • Carlos Albarrán Morillo

    (SAfeR—Centro Studi su Sicurezza, Affidabilitàe Rischi, Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, 10129 Torino, Italy)

  • Franco Modaffari

    (IVECO Group, Via Puglia, 35, 10156 Torino, Italy)

  • Lorenzo Comberti

    (SAfeR—Centro Studi su Sicurezza, Affidabilitàe Rischi, Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, 10129 Torino, Italy)

Abstract

Human factors play a relevant role in the dynamic work environments of the manufacturing sector in terms of production efficiency, safety, and sustainable performance. This is particularly relevant in assembly lines where humans are widely employed alongside automated and robotic agents. In this situation, operators’ ability to adapt to different levels of task complexity and variability in each workstation has a strong impact on the safety, reliability, and efficiency of the overall production process. This paper presents an application of a theoretical and empirical method used to assess the matching of different workers to various workstations based on a quantified comparison between the workload associated with the tasks and the human capability of the workers that can rotate among them. The approach allowed for the development of an algorithm designed to operationalise indicators for workload and task complexity requirements, considering the skills and capabilities of individual operators. This led to the creation of human performance (HP) indices. The HP indices were utilized to ensure a good match between requirements and capabilities, aiming to minimise the probability of human error and injuries. The developed and customised model demonstrated encouraging results in the specific case studies where it was applied but also offers a generalizable approach that can extend to other contexts and situations where job rotations can benefit from effectively matching operators to suitable task requirements.

Suggested Citation

  • Maria Chiara Leva & Micaela Demichela & Carlos Albarrán Morillo & Franco Modaffari & Lorenzo Comberti, 2023. "Optimizing Human Performance to Enhance Safety: A Case Study in an Automotive Plant," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11097-:d:1195332
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    References listed on IDEAS

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    1. T S Baines & O Benedettini, 2007. "Modelling human performance within manufacturing systems design: from a theoretical towards a practical framework," Journal of Simulation, Taylor & Francis Journals, vol. 1(2), pages 121-130, May.
    2. Groth, Katrina M. & Mosleh, Ali, 2012. "A data-informed PIF hierarchy for model-based Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 154-174.
    3. Daron Acemoglu & Pascual Restrepo, 2017. "Robots and Jobs: Evidence from US Labor Markets," Boston University - Department of Economics - Working Papers Series dp-297, Boston University - Department of Economics.
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