Restricted accessResearch articleFirst published online 2026
Determining training needs of welders in equipment manufacturing industry: A systematic approach using Delphi fuzzy method and FAHP for traditional and immersive trainings
The metal equipment manufacturing industry is inherently high-risk, particularly in welding operations. Effective training is critical to ensure welders’ safety and health. Systematic identification and prioritization of educational needs are essential for creating impactful training programs tailored to these high-risk environments.
Objective
This study aims to identify and prioritize essential training topics for welders using the Fuzzy Delphi Method (FDM) and Fuzzy Analytical Hierarchy Process (FAHP) to enhance safety, health, and productivity.
Methods
A total of 15 experts participated in this study, including 13 industry professionals (factory inspectors, engineers, and safety directors) and 2 academic experts (professors). Their professional backgrounds encompassed areas such as occupational health and safety, welding safety supervision, and HSE management. Their educational qualifications ranged from BSc to PhD. Expert opinions were collected in two phases. first, the Fuzzy Delphi Method (FDM) was used to refine the training topics, and second, the Fuzzy Analytic Hierarchy Process (FAHP) was employed to prioritize them based on their relative importance.
Results
Of 18 proposed topics, 11 met the 0.7 retention threshold. The highest-ranked topics were Working at Height and Use of Personal Protective Equipment (PPE), both with a normalized weight of 0.149. Other key areas included Welding Safety in Confined Spaces (0.142) and Electrical Hazards in Welding (0.112). Expert agreement across rounds was strong, with final consensus variation under 0.2.
Conclusions
Effective health and safety training is essential for high-risk industries like welding. Accurate identification of training needs ensures that tailored educational content enhances employee safety and organizational productivity.
WanjariMBWankhedeP. Occupational hazards associated with welding work that influence health status of welders. Int J Curr Res Rev2020; 12: 51–55.
2.
KahPMartikainenJ. Current trends in welding processes and materials: improve in effectiveness. Rev adv Mater Sci2012; 30: 189–200.
3.
HassimAMRozaliMZ. Safety knowledge of welding workers in the manufacturing industry. Res Innov Tech Vocat Educ Train2022; 2: 81–89.
4.
AlamMDAli KhanI. Ergonomic design of foot-operated resistance spot welding interface using multi-criteria decision-making approach. WORK2025: 10519815241311180. In press.
5.
GargMDasGVuppuluriPP. Assessing occupational hazards in welding operations: a machine learning-based approach for worker safety in Indian foundries. WORK2024; 79: 405–415.
6.
TongRLiHZhangB, et al.Modeling of unsafe behavior risk assessment: a case study of Chinese furniture manufacturers. Saf Sci2021; 136: 105157.
7.
KhairatF, editor. Preventing Accidents Through the Implementation of The Work Standards in the Welding Workplace. In 5th International Conference on Health Sciences (ICHS 2018). Atlantis Press; 2019.
8.
KhorshidikiaSRismanchianMPopovG,et al.Enhancing occupational safety in welding: identifying hazards and improving personal protective equipment utilization through hierarchical task analysis. Health Scope2025; 15: e164956.
9.
NakaiAKajiharaYNishimotoK,et al.Information-sharing system supporting onsite work for chemical plants. J Loss Prev Process Ind2017; 50: 15–22.
10.
EshaghiMSepehrP. Effect of training interventions on the reduction of unsafe behaviors using deming model. Int J Occup Hygiene2016; 8: 69–77.
11.
ZhaoDLucasJ. Virtual reality simulation for construction safety promotion. Int J Inj Contr Saf Promot2015; 22: 57–67.
12.
HabibiEKhorshidikiaSRismanchianM,et al.Identifying Training Needs and Occupational Hazards Among Welders: A Cross-sectional Study in the Metal Manufacturing Industry. Health Scope2025; 14. doi:10.5812/healthscope-160737
13.
ChenH-MWuH-YChenP-S. Innovative service model of information services based on the sustainability balanced scorecard: applied integration of the fuzzy Delphi method, Kano model, and TRIZ. Expert Syst Appl2022; 205: 117601.
AliabadiMMMohammadfamIKhorshidikiaS. Human error identification and risk assessment in loading and unloading of petroleum products by road trucks using the SHERPA and fuzzy inference system method. Heliyon2024; 10. doi:10.1016/j.heliyon.2024.e34072
16.
KhorshidikiaSHabibiE. Applying the fuzzy Delphi method in the cement industry: enhancing decision-making and operational efficiency. Sleep Science2025; 18: A046.
17.
AliabadiMMPourhasanAMohammadfamI. Risk modelling of a hydrogen gasholder using Fuzzy Bayesian network (FBN). Int J Hydrogen Energy2020; 45: 1177–1186.
18.
Mirzaei AliabadiMMohammadfamISalimiK. Identification and evaluation of maintenance error in catalyst replacement using the HEART technique under a fuzzy environment. Int J Occup Saf Ergon2022; 28: 1291–1303.
19.
KhorshidikiaSRismanchianMHabibiE. A narrative review on the application of Delphi and fuzzy Delphi techniques in the cement industry. Work2025; 80: 1507–1517.
20.
LavasaniSMZendeganiACelikM. An extension to Fuzzy fault tree analysis (FFTA) application in petrochemical process industry. Process Saf Environ Prot2015; 93: 75–88.
21.
RajakarunakaranSKumarAMPrabhuVA. Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station. J Loss Prev Process Ind2015; 33: 109–123.
22.
AkyuzECelikECelikM. A practical application of human reliability assessment for operating procedures of the emergency fire pump at ship. Ships Offsh Struct2018; 13: 208–216.
23.
Serrano-GuerreroJRomeroFPOlivasJA. Fuzzy logic applied to opinion mining: a review. Knowl Based Syst2021; 222: 107018.
24.
Mirzaei AliabadiMMohammadfamISalimiK. Identification and evaluation of maintenance error in catalyst replacement using the HEART technique under a fuzzy environment. Int J Occup Saf Ergon2022; 28: 1291–1303.
25.
TorfiFFarahaniRZRezapourS. Fuzzy AHP to determine the relative weights of evaluation criteria and fuzzy TOPSIS to rank the alternatives. Appl Soft Comput2010; 10: 520–528.
26.
Keshavarz GhorabaeeMZavadskasEKOlfatL,et al.Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica2015; 26: 435–451.
27.
ShiSJiangBMengX. Assessment of gas and dust explosion in coal mines by means of fuzzy fault tree analysis. Int J Min Sci Technol2018; 28: 991–998.
28.
ZhangJKangJSunL,et al.Risk assessment of floating offshore wind turbines based on fuzzy fault tree analysis. Ocean Eng2021; 239: 109859.
29.
LiuYEckertCMEarlC. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst Appl2020; 161: 113738.
30.
GhasemiFBabamiriMPashootanZ. A comprehensive method for the quantification of medication error probability based on fuzzy SLIM. PLoS One2022; 17: e0264303.
31.
LiaoCWYaoKCTsaiCT, et al.Constructing and validating professional competence indicators for underwater welding technicians for offshore wind power generation in Taiwan. Sustainability2023; 15: 10801.
32.
FatemiFArdalanAAguirreB, et al.Constructing the indicators of assessing human vulnerability to industrial chemical accidents: a consensus-based Fuzzy Delphi and Fuzzy AHP approach. PLoS Curr2017; 9, dis. 526884afe308f8876dce69c45357ecd.
33.
AmaniFBahadoramMHazratiS. Evaluation of occupational injuries among welders in Northwest Iran. Journal of Preventive Epidemiology2017; 2.
34.
Alexander VA, Sindhu KNC, Zechariah P et al. Occupational safety measures and morbidity among welders in Vellore, Southern India. Int J Occup Environ Health 2016; 22: 300–306.
35.
NalugyaAKiguliJWafulaST, et al.Knowledge, attitude and practices related to the use of personal protective equipment among welders in small-scale metal workshops in Nansana municipality, Wakiso district, Uganda. Health Psychol Behav Med2022; 10: 731–747.
36.
Alfaro-ViquezDZamora-HernandezMFernandez-VegaM, et al.Integrating virtual reality into welding training: an industry 5.0 approach. Electronics (Basel)2025; 14: 1964.
37.
AlizadehSSMortazaviSBSepehriMM. Analysis of occupational accident fatalities and injuries among male group in Iran between 2008 and 2012. Iran Red Crescent Med J2015; 17: e18976.
38.
PirvuVRontescuCBogatuA-M, et al.Research on occupational risk assessment for welder occupation in Romania. Processes2024; 12: 1295.
39.
HemmatiARostamiAFarhangianS, editors. Safety Requirement for Welding Job at Confined Space. 1st International Conference on Oil, Gas, Petrochemical and HSE. Bushehr, Iran: Islamic Azad University of Bushehr, 2017.
40.
Taheri FardAAhmadiniaHKafiM, et al.The study of pulmonary, renal and hepatic status in door and window welders in Rafsanjan in 2018: a short report. Journal of Rafsanjan University of Medical Sciences2020; 18: 1065–1072.
41.
MohebianZ. Exploring the risk of welders’ exposure to the gases and metal fumes in a shipbuilding industry: A case study. Health and Safety at Work2020.
42.
LamontagneADKeegelTLouieAM, et al.A systematic review of the job-stress intervention evaluation literature, 1990–2005. Int J Occup Environ Health2007; 13: 268–280.
43.
ZoharD. Thirty years of safety climate research: reflections and future directions. Accid Anal Prev2010; 42: 1517–1522.
44.
HsuY-LLeeC-HKrengVB. The application of fuzzy Delphi method and fuzzy AHP in lubricant regenerative technology selection. Expert Syst Appl2010; 37: 419–425.
45.
TsaiHLeeALeeH, et al.An application of the fuzzy Delphi method and fuzzy AHP on the discussion of training indicators for the regional competition, Taiwan national skills competition, in the trade of joinery. Sustainability2020; 12: 4290.
46.
Gamboa-SánchezMCCotrina-TeatinoMAVega-GonzalezJA, et al.Effective critical risk management in welding operations for mining: A case study on incident reduction. International Journal of Safety & Security Engineering2024; 14. doi:10.18280/ijsse.140403
47.
AisyahISMokhtarAKurniawatiD,et al.Empowerment of dermo youth organization with design training, welding techniques and OHS towards the development of economic independence of village communities. Journal of Community Service and Empowerment2024; 5: 327–335.
