Identifying Critical Factors Affecting Human Error Probability in Power Plant Operations and Their Sustainability Implications

Vahideh Bafandegan Emroozi1, Azam Modares1
1Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

Tóm tắt

Human errors in power plants can have a significant impact on sustainability. Sustainability in the context of power plants involves ensuring the long-term viability of energy generation while simultaneously minimizing adverse environmental, social, and economic impacts. Human errors in the control and operation of power plants can result in energy losses, reducing the overall efficiency of the plant. This research aims to enhance organizational decision-making by identifying and evaluating key factors affecting human error probability (HEP) and their relationships in power plants. The study uses the cross-impact matrix multiplication applied to classification (MICMAC) method to identify key factors, dependencies, and interconnections influencing HEP. By recognizing and understanding these dependencies, managers can make informed decisions and implement appropriate adjustments to organizational conditions and personnel. Based on case study results, six sub-factors are identified as having the highest level of influence on HEP: the operating procedures, skills and experiences of personnel, ergonomics, the interruption of tasks, repetitiveness and simplicity of the task, and education and training plan. The insights gained from the research can be used to enhance understanding and implement effective strategies to mitigate the impact of human error, leading to improvements in sustainability within power plants.

Tài liệu tham khảo

Aalipour M, Ayele YZ, Barabadi A (2016) Human reliability assessment (HRA) in maintenance of production process: a case study. Int J Syst Assur Eng Manag 7(2):229–238. https://doi.org/10.1007/s13198-016-0453-z Alaswad S, Xiang Y (2017) A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliab Eng Syst Saf 157:54–63. https://doi.org/10.1016/j.ress.2016.08.009 Almeida JC, Ribeiro B, Cardoso A (2023) A human-centric approach to aid in assessing maintenance from the sustainable manufacturing perspective. Proc Comput Sci 220:600–607. https://doi.org/10.1016/j.procs.2023.03.076 Arantes A, Ferreira LMDF (2023) Development of delay mitigation measures in construction projects: A combined interpretative structural modeling and MICMAC analysis approach. Prod Plan Control 1–16. https://doi.org/10.1080/09537287.2022.2163934 Asadayoobi N, Taghipour S, Jaber MY (2022) Predicting human reliability based on probabilistic mission completion time using Bayesian network. Reliab Eng Syst Saf 221:108324. https://doi.org/10.1016/j.ress.2022.108324 Ayvaz S, Alpay K (2021) Predictive maintenance system for production lines in manufacturing: a machine learning approach using IoT data in real-time. Expert Syst Appl 173:114598. https://doi.org/10.1016/j.eswa.2021.114598 Azadeh A, Salehi V, Jokar M, Asgari A (2016) An integrated multi-criteria computer simulation-AHP-TOPSIS approach for optimum maintenance planning by incorporating operator error and learning effects. Intell Ind Syst 2(1):35–53. https://doi.org/10.1007/s40903-016-0039-8 Bafandegan Emroozi V, Fakoor A (2022) A new approach to human error assessment in financial service based on the modified CREAM and DANP. J Ind Syst Eng 14(4):95–120 Bafandegan Emroozi V, Modares A, Roozkhosh P (2023a) A new model to optimize the human reliability based on CREAM and group decision making. Qual Reliab Eng Int. hhttps://doi.org/10.1002/qre.3457 Bafandegan Emroozi V, Kazemi M, Doostparast M, Pooya A (2023b) Improving industrial maintenance efficiency: A holistic approach to integrated production and maintenance planning with human error optimization. Process Integr Optim Sustain. https://doi.org/10.1007/s41660-023-00374-3 Bafandegan Emroozi V, Roozkhosh P, Modares A, Roozkhosh F (2023c) Selecting green suppliers by considering the internet of things and CMCDM approach. Process Integr Optim Sustain 7(5):1167–1189. https://doi.org/10.1007/s41660-023-00336-9 Chandramowli S, Transue M, Felder FA (2011) Analysis of barriers to development in landfill communities using interpretive structural modeling. Habitat Int 35(2):246–253. https://doi.org/10.1016/j.habitatint.2010.09.005 Delgado Martínez AM, PantojaTimarán F (2015) Structural analysis for the identification of key variables in the Ruta del Oro, Nariño Colombia. DYNA 82(191):27–33. https://doi.org/10.15446/dyna.v82n191.45532 Dewangan DK, Agrawal R, Sharma V (2015) Enablers for competitiveness of Indian manufacturing sector: an ISM-Fuzzy MICMAC analysis. Proc Soc Behav Sci 189:416–432. https://doi.org/10.1016/j.sbspro.2015.03.200 Dubey R, Ali SS (2014) Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Glob J Flex Syst Manag 15(2):131–143. https://doi.org/10.1007/s40171-014-0058-9 Elidolu G, Akyuz E, Arslan O, Arslanoğlu Y (2022) Quantitative failure analysis for static electricity-related explosion and fire accidents on tanker vessels under fuzzy bow-tie CREAM approach. Eng Fail Anal 131:105917. https://doi.org/10.1016/j.engfailanal.2021.105917 Emroozi VB, Kazemi M, Modares A, Roozkhosh P (2024) Improving quality and reducing costs in supply chain: the developing VIKOR method and optimization. J Ind Manag Optim 20(2):494–524. https://doi.org/10.3934/jimo.2023088 Fathi MR, Zeinali M, Torabi M, Alavizadeh SM (2022) Structural analysis of the future of Iranian online banking employing a MICMAC approach. Technol Forecast Soc Chang 183:121943. https://doi.org/10.1016/j.techfore.2022.121943 Froger A, Gendreau M, Mendoza JE, Pinson E, Rousseau L-M (2018) Solving a wind turbine maintenance scheduling problem. J Sched 21(1):53–76. https://doi.org/10.1007/s10951-017-0513-5 Gholi-Nejad NS (2012) Structure of human errors in tasks of operators working in the control room of an oil refinery unit. Ind J Sci Technol 5(2):1–6. https://doi.org/10.17485/ijst/2012/v5i2.11 Gursel E, Reddy B, Khojandi A, Madadi M, Coble JB, Agarwal V, Yadav V, Boring RL (2023) Using artificial intelligence to detect human errors in nuclear power plants: a case in operation and maintenance. Nucl Eng Technol 55(2):603–622. https://doi.org/10.1016/j.net.2022.10.032 Hameed A, Khan F, Ahmed S (2016) A risk-based shutdown inspection and maintenance interval estimation considering human error. Process Saf Environ Prot 100:9–21. https://doi.org/10.1016/j.psep.2015.11.011 He Y, Kuai N-S, Deng L-M, He X-Y (2021) A method for assessing human error probability through physiological and psychological factors tests based on CREAM and its applications. Reliab Eng Syst Saf 215:107884. https://doi.org/10.1016/j.ress.2021.107884 Hejazi T-H, Roozkhosh P (2019) Partial inspection problem with double sampling designs in multi-stage systems considering cost uncertainty. J Ind Eng Manag Stud 6(1):1–17. https://doi.org/10.22116/jiems.2019.87659 Hobbs A (2021) Aircraft maintenance and inspection. In International Encyclopedia of Transportation (pp 25–33). Elsevier. https://doi.org/10.1016/B978-0-08-102671-7.10103-4 Hobbs A, Williamson A (2003) Associations between errors and contributing factors in aircraft maintenance. Human Factors 45(2):186–201. https://doi.org/10.1518/hfes.45.2.186.27244 Ighravwe DE, AyoolaOke S (2021) Applying fuzzy multi-criteria decision making framework in evaluating maintenance systems with emphasis on human tasks and errors. Mahasarakham Int J Eng Technol 7:6777. https://doi.org/10.14456/MIJET.2021.10 Kamble SS, Gunasekaran A, Sharma R (2020) Modeling the blockchain enabled traceability in agriculture supply chain. Int J Inf Manage 52:101967. https://doi.org/10.1016/j.ijinfomgt.2019.05.023 Kinker P, Swarnakar V, Singh AR, Jain R (2021) Identifying and evaluating service quality barriers for polytechnic education: an ISM-MICMAC approach. Mater Today: Proc 46:9752–9757. https://doi.org/10.1016/j.matpr.2020.09.129 Leśniak A, Górka M (2020) Structural analysis of factors influencing the costs of facade system implementation. Appl Sci 10(17):6021. https://doi.org/10.3390/app10176021 Lin C, Xu QF, Huang YF (2022) An HFM-CREAM model for the assessment of human reliability and quantification. Qual Reliab Eng Int 38(5):2372–2387. https://doi.org/10.1002/qre.3081 Manzano-Solís LR, Díaz-Delgado C, Gómez-Albores MA, Mastachi-Loza CA, Soares D (2019) Use of structural systems analysis for the integrated water resources management in the Nenetzingo river watershed. Mexico Land Use Policy 87:104029. https://doi.org/10.1016/j.landusepol.2019.104029 Marseguerra M, Zoia A (2007) Some insights in superdiffusive transport. Physica A 377(1):1–14. https://doi.org/10.1016/j.physa.2006.11.040 Meshkati N (1991) Human factors in large-scale technological systems’ accidents: three mile island, Bhopal. Chernobyl Ind Crisis Q 5(2):133–154. https://doi.org/10.1177/108602669100500203 MitraDebnath R, Shankar R (2012) Improving service quality in technical education: use of interpretive structural modeling. Qual Assur Educ 20(4):387–407. https://doi.org/10.1108/09684881211264019 Modares A, Bafandegan Emroozi V, Mohemmi Z (2021) Evaluate and control the factors affecting the equipment reliability with the approach dynamic systems simulation, case study: Ghaen Cement Factory. J Qual Eng Manag 11(2):89–106 Modares A, Motahari Farimani N, Bafandegan Emroozi V (2023a) Applying a multi-criteria group decision-making method in a probabilistic environment for supplier selection (Case study: Urban railway in Iran). J Optimiz Ind Eng 16-1:129–140. https://doi.org/10.22094/joie.2023.1950386.1929 Modares A, Kazemi M, Emroozi VB, Roozkhosh P (2023b) A new supply chain design to solve supplier selection based on internet of things and delivery reliability. J Ind Manag Optim 19(11):7993–8028. https://doi.org/10.3934/jimo.2023028 Modares A, Motahari Farimani N, Bafandegan Emroozi V (2023c) A vendor-managed inventory model based on optimal retailers selection and reliability of supply chain. J Ind Manag Optim 19(5):3075–3106. https://doi.org/10.3934/jimo.2022078 Modares A, Farimani NM, Dehghanian F (2024) A new vendor-managed inventory four-tier model based on reducing environmental impacts and optimal suppliers selection under uncertainty. J Ind Manag Optim 20(1):188–220. https://doi.org/10.3934/jimo.2023074 Morais C, Yung KL, Johnson K, Moura R, Beer M, Patelli E (2022) Identification of human errors and influencing factors: a machine learning approach. Saf Sci 146:105528. https://doi.org/10.1016/j.ssci.2021.105528 Ni L, Ahmad SF, Alshammari TO, Liang H, Alsanie G, Irshad M, Alyafi-AlZahri R, BinSaeed RH, Al-Abyadh MHA, Abu Bakir SMM, Ayassrah AYABA (2023) The role of environmental regulation and green human capital towards sustainable development: The mediating role of green innovation and industry upgradation. J Clean Prod. https://doi.org/10.1016/j.jclepro.2023.138497 Ocampo L, Aro JL, Evangelista SS, Maturan F, Yamagishi K, Mamhot D, Mamhot DF, Calibo-Senit DI, Tibay E, Pepito J, Quiñones R (2022) Research Productivity for augmenting the innovation potential of higher education institutions: an interpretive structural modeling approach and MICMAC analysis. J Open Innov: Technol Market Complexity 8(3):148. https://doi.org/10.3390/joitmc8030148 Patel MN, Pujara AA, Kant R, Malviya RK (2021) Assessment of circular economy enablers: hybrid ISM and fuzzy MICMAC approach. J Clean Prod 317:128387. https://doi.org/10.1016/j.jclepro.2021.128387 Poonia A, Sindhu S, Arya V, Panghal A (2022) Analysis of drivers for anti-food waste behaviour—TISM and MICMAC approach. J Ind Bus Res 14(2):186–212. https://doi.org/10.1108/JIBR-02-2021-0069 Roozkhosh P, Kazemi M (2022) Application of Internet of Things in the green supply chain and investigating the effective factors for selecting a green supplier (a case study: Mashhad Rubber Factory). Supply Chain Manag 24(75):61–73 Roozkhosh P, Pooya A, Agarwal R (2023a) Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach. Oper Manag Res 16(2):705–725. https://doi.org/10.1007/s12063-022-00336-x Roozkhosh P, Pooya A, Soleimani Fard O, Bagheri R (2023b) Revolutionizing supply Chain sustainability: an additive manufacturing-enabled optimization model for minimizing waste and costs. Process Integr Optim Sustain. https://doi.org/10.1007/s41660-023-00368-1 Saxena JP, Sushil, Vrat P (1990) Impact of indirect relationships in classification of variables-a micmac analysis for energy conservation. Syst Res 7(4):245–253. https://doi.org/10.1002/sres.3850070404 Sharma R, Kannan D, Darbari JD, Jha PC (2022) Analysis of collaborative sustainable practices in multi-tier food supply chain using integrated TISM-Fuzzy MICMAC model: a supply chain practice view. J Clean Prod 354:131271. https://doi.org/10.1016/j.jclepro.2022.131271 Velmurugan K, Saravanasankar S, Venkumar P, Sudhakarapandian R, Bona GD (2022) Hybrid fuzzy AHP-TOPSIS framework on human error factor analysis: implications to developing optimal maintenance management system in the SMEs. Sustain Futures 4:100087. https://doi.org/10.1016/j.sftr.2022.100087 Vishwakarma A, Dangayach GS, Meena ML, Gupta S (2022) Analysing barriers of sustainable supply chain in apparel & textile sector: a hybrid ISM-MICMAC and DEMATEL approach. Clean Logist Supply Chain 5:100073. https://doi.org/10.1016/j.clscn.2022.100073 Wang L, Cao Q, Zhou L (2018) Research on the influencing factors in coal mine production safety based on the combination of DEMATEL and ISM. Saf Sci 103:51–61. https://doi.org/10.1016/j.ssci.2017.11.007 Wu Y, Li C-C, Chen X, Dong Y (2018) Group decision making based on linguistic distributions and hesitant assessments: maximizing the support degree with an accuracy constraint. Inf Fusion 41:151–160. https://doi.org/10.1016/j.inffus.2017.08.008 Yao K, Yan S, Tran CC (2022) A fuzzy CREAM method for human reliability analysis in digital main control room of nuclear power plants. Nucl Technol 208(4):761–774. https://doi.org/10.1080/00295450.2021.1947123 Zare A, Hoboubi N, Farahbakhsh S, Jahangiri M (2022) Applying analytic hierarchy process and failure likelihood index method (AHP-FLIM) to assess human reliability in critical and sensitive jobs of a petrochemical industry. Heliyon 8(5):e09509. https://doi.org/10.1016/j.heliyon.2022.e09509