IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p11097-d1195332.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/11097/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/14/11097/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    2. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Zarei, Esmaeil & Khan, Faisal & Abbassi, Rouzbeh, 2021. "Importance of human reliability in process operation: A critical analysis," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    4. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A quantitative measure of fitness for duty and work processes for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 595-601.
    5. Laumann, Karin & Rasmussen, Martin, 2016. "Suggested improvements to the definitions of Standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) performance shaping factors, their levels and multipliers and the nominal tasks," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 287-300.
    6. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Wang, Lijing & Wang, Yanlong & Chen, Yingchun & Pan, Xing & Zhang, Wenjin, 2020. "Performance shaping factors dependence assessment through moderating and mediating effect analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Wang, Lijing & Wang, Yanlong & Chen, Yingchun & Pan, Xing & Zhang, Wenjin & Zhu, Yanzhi, 2020. "Methodology for assessing dependencies between factors influencing airline pilot performance reliability: A case of taxiing tasks," Journal of Air Transport Management, Elsevier, vol. 89(C).
    9. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Chen, Shuai & Zhang, Li & Qing, Tao, 2021. "A human reliability analysis methodology based on an extended Phoenix method for severe accidents in nuclear power plants: Qualitative analysis framework," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    12. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A classification scheme of erroneous behaviors for human error probability estimations based on simulator data," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 1-13.
    13. Al-Douri, Ahmad & Levine, Camille S. & Groth, Katrina M., 2023. "Identifying human failure events (HFEs) for external hazard probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    14. Zhao, Yunfei & Smidts, Carol, 2021. "CMS-BN: A cognitive modeling and simulation environment for human performance assessment, part 1 — methodology," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    15. Shirley, Rachel Benish & Smidts, Carol & Zhao, Yunfei, 2020. "Development of a quantitative Bayesian network mapping objective factors to subjective performance shaping factor evaluations: An example using student operators in a digital nuclear power plant simul," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    16. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2021. "Analysis of recent operational events involving inappropriate actions: influencing factors and root causes," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Ji, Changcheng & Gao, Fei & Liu, Wenjiang, 2024. "Dependence assessment in human reliability analysis based on cloud model and best-worst method," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    18. Park, Jinkyun, 2024. "A framework to determine the holistic multiplier of performance shaping factors in human reliability analysis – An explanatory study," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    19. Dhruv Pandya & Luca Podofillini & Frank Emert & Antony J Lomax & Vinh N Dang, 2018. "Developing the foundations of a cognition-based human reliability analysis model via mapping task types and performance-influencing factors: Application to radiotherapy," Journal of Risk and Reliability, , vol. 232(1), pages 3-37, February.
    20. Kim, Yochan & Park, Jinkyun & Jung, Wondea & Jang, Inseok & Hyun Seong, Poong, 2015. "A statistical approach to estimating effects of performance shaping factors on human error probabilities of soft controls," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 378-387.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11097-:d:1195332. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.