How Can Business Analytics Enhance Decision-Making in Oil and Gas Surface Facilities?
Open accessAbstract
This systematic literature review examines the application of business analytics in oil and gas surface facilities, analyzing 43 peer-reviewed publications (2012–2025). It identifies three levels of analytics—descriptive, predictive, and prescriptive—each addressing operational challenges through specific technologies. Descriptive analytics enhances visibility via IoT sensors and dashboards, predictive analytics enables proactive maintenance using machine learning, and prescriptive analytics optimizes operations through simulations and multi-objective decision models. Implementation examples show efficiency gains, including a 28% reduction in separation unit losses, an 18% decrease in compression system energy use, and a 64% improvement in methane emissions identification. However, adoption remains hindered by data integration issues, workforce skill gaps, and organizational resistance. This study proposes a business analytics framework to guide surface facility managers in selecting analytics solutions while emphasizing progressive capability development. Future research should explore digital twins, real-time data integration, ESG reporting, workforce training, and edge computing for remote facilities. This review provides a structured approach to analytics implementation, balancing efficiency, economic performance, and environmental sustainability.
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AI-driven analytics enable reductions in energy consumption, emissions, and process losses in real time.
“In energy- and environmentally intensive industries, a systematic review shows that the combination of descriptive, predictive, and prescriptive analytics enables reductions in energy consumption, emissions, and process losses in real time.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [32]
AI shifts sustainable business models from reactive toward proactive and optimization-oriented management.
“Prasetya et al. [32] found that, at the operational level, AI shifts sustainable business models (SBMs) from reactive toward proactive and optimization-oriented management.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [32]
Reported cases show an 18% reduction in energy consumption of compression systems and a 64% improvement in methane leak detection.
“Reported cases-such as an 18% reduction in energy consumption of compression systems and a 64% improvement in methane leak detection-demonstrate that AI-driven decision-making has a direct environmental impact.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [32]
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References 43 · Cited by 3
- Authors
- Prasetya, A., Wasesa, M., & Sunitiyoso, Y.
- Affiliations
- Bandung Institute of Technology (ID)
- Venue
- IEEE Access
- Published
- 2025
- Volume
- 13
- Pages
- 113291-113304
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4411639802
- Cited by
- 3
- References
- 43
- Citations by year
- 2026: 3
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- business analytics, decision-making, descriptive analytics, predictive analytics, prescriptive analytics, IoT sensors, dashboards, predictive maintenance, machine learning, multi-objective optimization, methane emissions, data integration, workforce skills gap, organizational resistance, digital twin, edge computing, ESG reporting
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