Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Purpose This paper aims to identify, prioritise and explore the relationships between various barriers that are hindering machine learning (ML) adaptation for analysing accident data information in Indian petroleum industry. Design/methodology/approach The preferred reporting items systematic reviews meta-analysis (PRISMA) is initially used identify key as reported extant literature. decision-making trial evaluation laboratory (DEMATEL) technique then discover interrelationships barriers, which prioritised, based on three criteria (time, cost relative importance) using complex proportional assessment (COPRAS) multi-objective optimisation method by ratio analysis (MOORA). Delphi obtain analyse from 10 experts who work at facilities India. Findings findings provide practical insights management analysts use ML techniques when large amounts of data. will help organisations focus resources most significant obstacles overcome adopt primary tool analysis, can save time, money enable exploration valuable Originality/value first study a hybrid three-phase methodology consult with domain industry rank relationship these barriers.