Research Article | Open Access | Download PDF
Volume 74 | Issue 9 | Year 2026 | Article Id. IJETT-V74I9P118 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I9P118Towards Automation and Elimination of Subjectivity in Industrial Risk Management Based on Artificial Intelligence
Kawtar Benderouach, Idriss Bennis, Abdelouahad Bellat, Khalifa Mansouri
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 31 Jan 2026 | 23 Jul 2026 | 27 Jul 2026 | 30 Sep 2026 |
Citation :
Kawtar Benderouach, Idriss Bennis, Abdelouahad Bellat, Khalifa Mansouri, "Towards Automation and Elimination of Subjectivity in Industrial Risk Management Based on Artificial Intelligence," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 9, pp. 220-235, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I9P118
Abstract
Risk management is one of the fundamental components of a company's success, whatever its sector of activity. This paper reviews the work done in the field of risk management, both traditionally and by integrating Artificial Intelligence. It highlights the way in which risk management was solved in the traditional way, discussing methods and approaches and citing the limitations of traditional risk management. It also outlines research into the integration of Artificial Intelligence into risk management in the various phases of the risk management process. Based on the open literature published in the various journals in the field of risk management, the stages of the risk management process are the result of human judgment, which may identify risks or, for psychological or/and social reasons, may not, so human subjectivity influences the risk identification phase and all phases of the risk management process. To reduce this subjectivity and to reduce human contact in this phase, our article proposes a model based on Artificial Intelligence that attempts to identify risks and treat them in order to reduce or eliminate existing risks.
Keywords
Risk management, Artificial Intelligence, Machine Learning, Natural Language Processing, Subjectivity.
References
[1] Hany Khalil, and Fouad Khalaf, “A Systemic Risk
Management Model to Manage the Equipment Maintenance System in Oil and Gas
Companies,” MATEC Web of Conferences, vol. 281, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Alireza Noroozi et al., “The Role of Human Error in risk
Analysis: Application to Pre- and Post-Maintenance Procedures of Process
Facilities,” Reliability Engineering and System Safety, vol. 119, pp.
251-258, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Guozhi Cao et al.,
“Spatially Resolved risk Assessment of Environmental Incidents in China,” Journal
of Cleaner Production, vol. 219, pp. 856-864, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Aymen Mili et al., “Dynamic risk Management Unveil
Productivity Improvements,” Journal of Loss Prevention in the Process
Industries, vol. 22, no. 1, pp. 25-34, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Faisal I. Khan, and Mahmoud M. Haddara, “Risk-based
Maintenance (RBM): a Quantitative Approach for Maintenance/Inspection
Scheduling and Planning,” Journal of Loss Prevention in the Process
Industries, vol. 16, no. 6, pp. 561-573, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[6] T. Karkoszka, “Evaluation of the Processes with Application
of the Environmental Risk Assessment,” Procedia Engineering, vol. 132,
pp. 146-152, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Desheng Wue et al., “Risk
Management and Operations Research: a Review and Introduction to the Special
Volume,” Annals of Operations Research, vol. 237, no. 1‑2, pp. 1-5, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] A. Mili et al., “Risks Analyses Update based on Maintenance
Events,” IFAC Proceedings Volumes, vol. 41, no. 2, pp. 34-39, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jiaming Cui et al., “Product Quality Accidents Risk Analyzing
Approach based on the Extended FTA and Failure Cost,” Procedia CIRP, vol. 56, pp. 502-507, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Netta Liin Rossing et al.,
“A Functional HAZOP Methodology,” Computers and Chemical Engineering,
vol. 34, no. 2, pp. 244-253, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Frank J. Groen, Carol Smidts,
and Ali Mosleh, “QRAS-the Quantitative Risk Assessment System, Reliability
Engineering and System Safety, vol. 91, no. 3, pp. 292-304, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Chandra Sekhar Mandal, and
Mandira Agarwal, “A Review on Quantitative Risk Assessments for Oil and Gas
Installations and Changes in Risk Evaluation Techniques,” Materials Today:
Proceedings, vol. 99, pp. 145-153, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jon Espen Skogdalen, and Jan Erik Vinnem, “Quantitative Risk
Analysis of Oil and Gas Drilling, using Deepwater Horizon as Case Study,” Reliability
Engineering and System Safety, vol. 100, pp. 58‑66, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Abroon Qazi, and Pervaiz Akhtar, “Risk
Matrix Driven Supply Chain Risk Management: Adapting Risk Matrix based Tools to
Modelling Interdependent Risks and Risk Appetite,” Computers and Industrial
Engineering, vol. 139, pp. 1-36, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ariful Islam, and Des
Tedford, “Risk Determinants of small and Medium-Sized Manufacturing Enterprises
(SMEs) - an Exploratory study in New Zealand,” Journal of Industrial
Engineering International, vol. 8, no. 1, pp. 1-13, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ali Bou Nassif et al.,
“Machine Learning for Anomaly Detection: A Systematic Review,” IEEE Access,
vol. 9, pp. 78658-78700, 2021.
[CrossRef] [Google Scholar] [Publisher
Link]
[17] Dazhong Wu et al., “A
Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool
Wear Prediction using Random Forests,” Journal of Manufacturing Science and
Engineering, vol. 139, no. 7, pp. 1-8, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fan Zhang et al.,
“Construction site Accident Analysis using Text MINING and Natural Language
Processing Techniques,” Automation in Construction, vol. 99, pp.
238‑248, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Marvin Rausand, and Stein
Haugen, Risk Assessment: Theory, Methods, and Applications, 2nd
ed., John Wiley and Sons, 2020.
[Publisher Link]
[20] Ammar Ahmed, Berman Kayis,
and Sataporn Amornsawadwatana, “A Review of Techniques for Risk Management in
Projects,” Benchmarking: An International Journal, vol. 14, no. 1, pp.
22‑36, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Faisal Khan, Samith
Rathnayaka, and Salim Ahmed, “Methods and Models in Process Safety and Risk
Management: Past, Present and Future,” Process Safety and Environmental
Protection, vol. 98, pp. 116‑147, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[22] N.S. Arunraj, and J. Maiti,
“Risk-based Maintenance-Techniques and Applications,” Journal of Hazardous
Materials, vol. 142, no. 3, pp. 653-661, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[23] J. Tixier et al., “Review
of 62 Risk Analysis Methodologies of Industrial Plants,” Journal of Loss
Prevention in the Process Industries, vol. 15, no. 4, pp. 291‑303, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[24] W. Sghaier, E. Hergon, and
A. Desroches, “Gestion Globale Des Risques,” Transfusion Clinique et
Biologique, vol. 22, no. 3, pp. 158‑167, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Department of Defense,
Procedures for Performing a Failure Mode, Effects, and Criticality Analysis,
MIL-STD-1629A, 1980. [Online]. Available:
https://www.dsiintl.com/wp-content/uploads/2017/04/mil_std_1629a.pdf
[26] M. Shahrokhi, and A.
Bernard, “Risk Assessment/Prevention in Industrial Design Processes,” 2004
IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat.
No.04CH37583), The Hague, Netherlands, vol. 3, pp. 2592‑2598, 2004.
[CrossRef] [Google Scholar] [Publisher
Link]
[27] Steven Kmenta, Peder Fitch,
and Kosuke Ishii, “Advanced Failure Modes and Effects Analysis of Complex
Processes,” Proceedings of the ASME 1999 Design Engineering Technical
Conferences, Las Vegas, Nevada, USA, pp. 267‑275, 1999.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Charles F. Eubanks, Steven
Kmenta, and Kosuke Ishii, “Advanced Failure Modes and Effects Analysis using
Behavior Modeling,” Proceedings
of the ASME 1997 Design Engineering Technical Conferences. Volume 3: 9th
International Design Theory and Methodology Conferenc Sacramento,
California, USA, vol. 80456, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Gionata Carmignani,
“An Integrated Structural Framework to Cost-based FMECA: The Priority-Cost
FMECA,” Reliability Engineering and System Safety, vol. 94, no. 4, pp.
861-871, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Nicholas de Galvez et al.,
“EZID: A new Approach to Hazard Identification During the Design Process by
Analysing Energy Transfers,” Safety Science, vol. 95, pp. 1-14, 2017
[CrossRef] [Google Scholar] [Publisher Link]
[31] N. Bizon, M. Oproescu, and
G.R. Sisman, “Failure Risk Analysis using Data from a Power Station Remote
Monitored,” International Journal on Technical and Physical Problems of
Engineering, vol. 8, no. 3, pp. 42-51, 2016.
[Google Scholar] [Publisher Link]
[32] Ahmad Soltanzadeh et al.,
“Introducing FMEA plus method for Comprehensive Safety risk Assessment in the
Steel Industry,” PLOS One, vol. 20, no. 10, pp. 1-20, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Oleg Bazaluk et al.,
“Improvement of the Occupational risk Management Process in the work Safety
System of the Enterprise,” Frontiers in Public Health, vol. 11, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Yi Qi et al., “A
Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems,” arXiv
preprint, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher
Link]
[35] M. Shahrokhi, and A.
Bernard, “Risk Assessment/Prevention in Industrial Design Processes,” 2004
IEEE International Conference on Systems, Man and Cybernetics, The Hague,
Netherlands, vol. 3, pp. 2592-2598, 2004.
[CrossRef] [Google Scholar] [Publisher
Link]
[36] F. Redmill, “Exploring
Subjectivity in Hazard Analvsis,” Engineering Management Journal, vol. 12, no.
3, pp. 1-8, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[37] A. Azarian, A. Siadat, and
P. Martin, “A new Strategy for Automotive off-board Diagnosis based on a
Meta-Heuristic Engine,” Engineering Applications of Artificial Intelligence,
vol. 24, no. 5, pp. 733-747, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Jelena Petronijevic, Alain Etienne, and Jean-Yves
Dantan, “Human Factors Under Uncertainty: A Manufacturing Systems Design
using Simulation-Optimisation Approach,” Computers & Industrial
Engineering, vol. 127, pp. 665-676, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Habib Hadj-Mabrouk,
“Integration of Human Factors in Feedback,” 16th Congress “Lambda
Mu,” Risk Management and Operational Safety_LM16. No. Communication 1C-3,
pp. 1-7, 2008.
[Google Scholar]
[40] I. Maglogiannis et al., Emerging
Artificial Intelligence Applications in Computer Engineering: Real Word AI
Systems with Applications in EHealth, HCI, Information Retrieval and Pervasive
Technologies, IOS Press, 2007.
[Google Scholar] [Publisher Link]
[41] Richard S. Sutton, Introduction:
The Challenge of Reinforcement Learning, Reinforcement Learning, Springer,
vol. 173, pp. 1-3, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[42] R. Gentleman, and V.J.
Carey, Unsupervised Machine Learning, Bioconductor Case Studies, Springer,
pp. 137-157, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Yann LeCun, Yoshua Bengio,
and Geoffrey Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp.
436-444, 2015.
[CrossRef] [Google Scholar] [Publisher
Link]
[44] Attila Gulyás, István Kiss, and István Berta,
“Artificial Intelligence in Electrostatic risk Management,” Journal of
Electrostatics, vol. 71, no. 3, pp. 387-391, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Sundas Matloob, Yang Li, and
Khurram Zaman Khan, “Safety Measurements and Risk Assessment of Coal Mining
Industry Using Artificial Intelligence and Machine Learning,” Open Journal
of Business and Management, vol. 9, no. 3, pp. 1198-1209, 2021.
[Google Scholar]
[46] Priscilla Grace George, and
V.R. Renjith, “Evolution of Safety and Security Risk Assessment Methodologies
Towards the use of Bayesian Networks in Process Industries,” Process Safety
and Environmental Protection, vol. 149, pp. 758-775, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Jing Liu et al., “Dynamic
Deep Learning Algorithm based on Incremental Compensation for Fault Diagnosis
Model,” International Journal of Computational Intelligence Systems,”
vol. 11, no. 1, pp. 846-860, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Fan Zhang et al.,
“Construction site Accident Analysis using text Mining and Natural Language
Processing Techniques,” Automation in Construction, vol. 99, pp.
238‑248, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Daniel Santos et al.,
“Machine Learning Approaches to Traffic Accident Analysis and Hotspot
Prediction,” Computers, vol. 10, no. 12, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher
Link]
[50] Yehoshua Bar-Hillel, “The Present
Status of Automatic Translation of Languages,” Advances in Computers,
vol. 1, pp. 91-163, 1960.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Natalia Grabar, and Thierry
Hamon, “Utilizing Various Approaches to Detect and Categorize Chemical and
Bacteriological Risks,”
Risk and NLP, TALN, pp. 1-12, 2016.
[Google Scholar]
[52] Schmitt Eglantine, “A
Method for Leveraging Artificial Intelligence for Incident risk Management,” Lambda Mu 21 Congress, “Risk
Management and Digital Transformation: Opportunities and Threats, pp. 1-8, 2018.
[Google Scholar]
[53] Dipankar Chakrabarti et
al., “Use of Artificial Intelligence to Analyse Risk in Legal Documents for a
Better Decision Support,” TENCON 2018 - 2018 IEEE Region 10 Conference,
Jeju, Korea (South), pp. 0683-0688, 2018.
[CrossRef] [Google Scholar] [Publisher
Link]
[54]Yang Zou, Arto Kiviniemi, and
Stephen W. Jones, “Retrieving Similar cases for Construction Project risk
Management using Natural Language Processing Techniques,” Automation in
Construction, vol. 80, pp. 66-76, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Claire Emma Birnie et al.,
“Improving the Quality and Efficiency of Operational Planning and Risk
Management with ML and NLP,” SPE Offshore Europe Conference and Exhibition, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Xixi Lu et al.,
“Application of Machine Learning Technology for Occupational Accident Severity
Prediction in the case of Construction Collapse Accidents,” Safety Science,
vol. 163, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Siddhartha Agarwal et al., “Application of Natural
Language Processing and Machine Learning for Analyzing Mining Accident Reports
and Automating the Process of root Cause Analysis,” International Journal of
Coal Science and Technology, vol. 12, no. 91, pp. 1-33, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Patrick Jonk et al.,
“Natural Language Processing of Aviation Occurrence Reports for Safety
Management,” arXiv preprint, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher
Link]
[59] A. Guzman et al., Artificial
Intelligence Improving Safety and risk Analysis: A Comparative Analysis for
Critical Infrastructure,” 2016 IEEE International Conference on Industrial
Engineering and Engineering Management (IEEM), Bali, Indonesia, pp.
471-475, 2016.
[CrossRef] [Google Scholar] [Publisher
Link]
[60] Herbert A. Simon, The
Sciences of the Artificial, MIT Press, 2019.
[CrossRef] [Publisher Link]
[61] Darko Stanojević and
Velimir Ćirović, “Contribution to Development of risk analysis Methods by
Application of Artificial Intelligence Techniques,” Quality and Reliability
Engineering International, vol. 36, no. 7, pp. 2268-2284, 2020,
[CrossRef] [Google Scholar] [Publisher Link]
[62] K. Xu et al., “Fuzzy Assessment of FMEA
for Engine Systems,” Reliability
Engineering and System Safety, vol. 75, no. 1, pp. 17-29, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[63] S. Vinodh et al., “Fuzzy
Assessment of FMEA for Rotary Switches: A Case Study,” The TQM Journal,
vol. 24, no. 5, pp. 461-475, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Behnam Vahdani, M. Salimi,
and M. Charkhchian, “A new FMEA Method by Integrating Fuzzy Belief Structure
and TOPSIS to Improve Risk Evaluation Process,” The International Journal of
Advanced Manufacturing Technology, vol. 77, no. 1-4, pp. 357-368, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Mehri Mangeli, Alireza
Shahraki, and Faranak Hosseinzadeh Saljooghi, “Improvement of risk Assessment
in the FMEA using Nonlinear model, Revised Fuzzy TOPSIS, and support Vector
Machine,” International Journal of Industrial Ergonomics, vol. 69, pp.
209-216, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Alfredo Arcos Jiménez et
al., “Maintenance Management based on Machine Learning and Nonlinear Features
in wind Turbines,” Renewable Energy, vol. 146, pp. 316-328, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Alexander Guzman Urbina,
and Atsushi Aoyama, “Pipeline risk Assessment using Artificial Intelligence: A
case from the Colombian oil Network,” Process Safety Progress, vol. 37,
no. 1, pp. 110-116, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Lubka Tchankova, “Risk Identification-basic
Stage in Risk Management,” Management of Environmental Quality, vol. 13,
no. 3, pp. 290-297, 2002.
[CrossRef] [Google Scholar] [Publisher Link]