Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
Benchmarking: An International Journal
ISSN: 1463-5771
Article publication date: 6 September 2022
Issue publication date: 1 December 2023
Abstract
Purpose
This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.
Design/methodology/approach
The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.
Findings
The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.
Originality/value
This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.
Keywords
Citation
Gangadhari, R.K., Khanzode, V., Murthy, S. and Dennehy, D. (2023), "Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry", Benchmarking: An International Journal, Vol. 30 No. 9, pp. 3357-3381. https://doi.org/10.1108/BIJ-03-2022-0161
Publisher
:Emerald Publishing Limited
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