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A Simulator for Human Error Probability Analysis (SHERPA)

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  • Di Pasquale, Valentina
  • Miranda, Salvatore
  • Iannone, Raffaele
  • Riemma, Stefano

Abstract

A new Human Reliability Analysis (HRA) method is presented in this paper. The Simulator for Human Error Probability Analysis (SHERPA) model provides a theoretical framework that exploits the advantages of the simulation tools and the traditional HRA methods in order to model human behaviour and to predict the error probability for a given scenario in every kind of industrial system. Human reliability is estimated as function of the performed task, the Performance Shaping Factors (PSF) and the time worked, with the purpose of considering how reliability depends not only on the task and working context, but also on the time that the operator has already spent on the work. The model is able to estimate human reliability; to assess the effects due to different human reliability levels through evaluation of tasks performed more or less correctly; and to assess the impact of context via PSFs. SHERPA also provides the possibility of determining the optimal configuration of breaks. Through a methodology that uses assessments of an economic nature, it allows identification of the conditions required for the suspension of work in the shift for the operator׳s psychophysical recovery and then for the restoration of acceptable values of reliability.

Suggested Citation

  • Di Pasquale, Valentina & Miranda, Salvatore & Iannone, Raffaele & Riemma, Stefano, 2015. "A Simulator for Human Error Probability Analysis (SHERPA)," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 17-32.
    • Handle: RePEc:eee:reensy:v:139:y:2015:i:c:p:17-32
    DOI: 10.1016/j.ress.2015.02.003
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    References listed on IDEAS

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    8. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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    10. Bolbot, Victor & Theotokatos, Gerasimos & Bujorianu, Luminita Manuela & Boulougouris, Evangelos & Vassalos, Dracos, 2019. "Vulnerabilities and safety assurance methods in Cyber-Physical Systems: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 179-193.
    11. Park, J. & Chang, Y.J. & Kim, Y. & Choi, S. & Kim, S. & Jung, W., 2017. "The use of the SACADA taxonomy to analyze simulation records: Insights and suggestions," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 174-183.
    12. Mario Fargnoli & Mara Lombardi & Daniele Puri, 2019. "Applying Hierarchical Task Analysis to Depict Human Safety Errors during Pesticide Use in Vineyard Cultivation," Agriculture, MDPI, vol. 9(7), pages 1-18, July.
    13. Chen, Yuanjiang & Feng, Wei & Jiang, Zhiqiang & Duan, Lingling & Cheng, Shuangyi, 2021. "An accident causation model based on safety information cognition and its application," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    14. Park, Jinkyun & Jung, Jae-Yoon & Jung, Wondea, 2016. "The use of a process mining technique to characterize the work process of main control room crews: A feasibility study," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 31-41.
    15. Bitterling, Cristian & Koreis, Jonas & Loske, Dominik & Klumpp, Matthias, 2022. "Comparing manual and automated production and picking systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 327-350, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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