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

Rajan Kumar Gangadhari Orcid Logo, Vivek Khanzode, Shankar Murthy, Denis Dennehy Orcid Logo

Benchmarking: An International Journal, Volume: 30, Issue: 9, Pages: 3357 - 3381

Swansea University Author: Denis Dennehy Orcid Logo

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DOI (Published version): 10.1108/bij-03-2022-0161

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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...

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Published in: Benchmarking: An International Journal
ISSN: 1463-5771
Published: Emerald 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa60768
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spelling v2 60768 2022-08-06 Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry ba782cbe94139075e5418dc9274e8304 0000-0001-9931-762X Denis Dennehy Denis Dennehy true false 2022-08-06 CBAE 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. Journal Article Benchmarking: An International Journal 30 9 3357 3381 Emerald 1463-5771 Machine learning, Delphi, DEMATEL, MOORA, COPRAS, Petroleum industry 1 12 2023 2023-12-01 10.1108/bij-03-2022-0161 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Not Required 2024-06-06T14:14:58.5572837 2022-08-06T15:03:42.5692410 Faculty of Humanities and Social Sciences School of Management - Business Management Rajan Kumar Gangadhari 0000-0002-4841-7177 1 Vivek Khanzode 2 Shankar Murthy 3 Denis Dennehy 0000-0001-9931-762X 4 60768__25324__d3e88fcdc21d48fba7c238a646b03f34.pdf 60768.pdf 2022-10-06T12:49:28.4473756 Output 953486 application/pdf Accepted Manuscript true true eng
title Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
spellingShingle Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
Denis Dennehy
title_short Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
title_full Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
title_fullStr Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
title_full_unstemmed Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
title_sort Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry
author_id_str_mv ba782cbe94139075e5418dc9274e8304
author_id_fullname_str_mv ba782cbe94139075e5418dc9274e8304_***_Denis Dennehy
author Denis Dennehy
author2 Rajan Kumar Gangadhari
Vivek Khanzode
Shankar Murthy
Denis Dennehy
format Journal article
container_title Benchmarking: An International Journal
container_volume 30
container_issue 9
container_start_page 3357
publishDate 2023
institution Swansea University
issn 1463-5771
doi_str_mv 10.1108/bij-03-2022-0161
publisher Emerald
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2023-12-01T14:14:59Z
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score 11.012678

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