Designing an Autonomous Supply Chain Framework based on Artificial Intelligence, Big Data, and Internet of Things in the Oil and Gas Industry with a Sustainability and Cybersecurity Approach

Zahedi, Amir Ehsan; Esmaeili, Mehran

doi:10.22054/jims.2026.92638.3028

Designing an Autonomous Supply Chain Framework based on Artificial Intelligence, Big Data, and Internet of Things in the Oil and Gas Industry with a Sustainability and Cybersecurity Approach

Document Type : Research Paper

Authors

amir ehsan zahedi ¹

Mehran esmaeili ²

¹ Assistant Professor. Management department. Administration sciences and economy faculty. Arak university. Arak. Iran.

² Master of Business Administration student, Management department, Faculty of Humanities, Islamic Azad University, Najafabad Branch, Najafabad, Iran.

https://doi.org/10.22054/jims.2026.92638.3028

Abstract

The oil and gas industry faces challenges such as the complexity of a multi-layered and global supply network, operational and geopolitical risks, cybersecurity threats in critical infrastructure, sustainability pressures, and the need for real-time data-driven decision-making. The main objective of the study was to design and analyze an autonomous supply chain framework based on artificial intelligence, big data, and the Internet of Things in the oil and gas industry with an emphasis on sustainability and cybersecurity. The research was applied and developmental in terms of its purpose; qualitative-quantitative in terms of the nature of the data; and descriptive in terms of the data collection method, which was conducted using the Fuzzy Cognitive Mapping method. The study period was summer and fall 2025 and winter 2026. The population of the study was Iranian oil and gas industry experts, and 23 experts completed a structured questionnaire using purposive sampling. Achieving autonomy in the oil and gas supply chain was contingent upon the synergy and application of advanced digital technologies. Within this context, operational intelligence served as a crucial mediating factor, transforming technological capabilities into economic and environmental value. In other words, the establishment of data-driven digital infrastructure was the primary prerequisite for attaining sustainability, while resilience and trust within this ecosystem could only be realized through robust cybersecurity. The proposed model, functioning as a decision support system, empowered managers in this industry to optimally prioritize digital investments, evaluate the implications of security policies, and simulate various digital transformation scenarios prior to operationalization.
Introduction
As one of the most strategic industries in the world, the oil and gas industry is highly dependent on the effective performance of its supply chain. It has a multi-layered, extensive, and interconnected chain that ranges from the provision of specialized equipment and raw materials to extraction, refining, transmission, and distribution operations. The capital-intensive nature, geographical dispersion of assets, dependence on critical infrastructure, sensitivity to geopolitical fluctuations, and the requirement for operational continuity have presented the oil and gas industry with significant management complexities. In addition, disruptions caused by geopolitical crises, fluctuations in the global energy market, environmental constraints, and rapid technological changes have made the need to redesign the industry’s supply chain architecture more apparent than ever. In such a context, advanced digital technologies can play an important role in reinventing the oil and gas industry’s supply chain. Artificial intelligence, through machine learning algorithms and predictive analytics, enables demand forecasting, inventory optimization, automated operation scheduling, and predictive maintenance of equipment. The Industrial Internet of Things (IoT) enables real-time monitoring of the status of assets, transmission lines, and operational equipment by creating a network of smart sensors. Big data also provides strategic insights for decision-making by aggregating and analyzing a huge volume of structured and unstructured data. The synergy of these technologies facilitates the transition from reactive management to predictive and autonomous management in the oil and gas industry. Despite these capabilities, the deployment of an autonomous supply chain in the oil and gas industry faces serious challenges. One of the most important of these challenges is the issue of cybersecurity. The widespread digitalization of operational infrastructure and the convergence of information technology (IT) and operational technology (OT) have significantly increased the level of cyber attacks. The connection of field equipment, industrial control systems, smart pipelines, and remote-control centers, in the absence of appropriate security mechanisms, can lead to widespread disruptions, economic losses, environmental threats, and even human hazards. Along with security considerations, the issue of sustainability has also become one of the central concerns of the oil and gas industry. Increasing pressures from environmental regulations, carbon emission reduction commitments, stakeholder expectations, and sustainable development requirements have forced organizations active in this industry to review their operating models. Accordingly, the main objective of the present study was to design and analyze an autonomous supply chain framework based on artificial intelligence, big data, and the Internet of Things in the oil and gas industry with an emphasis on sustainability and cybersecurity.
Methodology
The present research was applied and developmental in terms of its purpose; qualitative-quantitative in terms of the nature of the data; and descriptive in terms of the method of data collection. The study period was summer and fall 2025 and winter 2026. The research aimed to design an autonomous supply chain framework based on artificial intelligence, big data, and the Internet of Things in the oil and gas industry with a sustainability and cybersecurity approach, and used the fuzzy cognitive mapping method. The statistical population of the research included experts in the following fields in the oil and gas industry: supply chain management, digital transformation and information technology, cybersecurity, sustainability management, and HSE. The sampling was carried out purposively (judgmentally), and the criteria for selecting experts were: at least 10 years of relevant professional experience; managerial or specialized experience in digital or operational projects; and familiarity with analytical systems and smart infrastructures. A total of 23 experts completed the questionnaire. In this study, Mental Modeler software was used to model the causal structure of an autonomous supply chain based on artificial intelligence, big data, and the Internet of Things.
Findings and Conclusion
Findings from modeling using fuzzy cognitive mapping showed that the transition to an autonomous supply chain in the oil and gas industry was not a purely technological process, but rather a systemic and multidimensional transformation that requires synergy between digital infrastructure, smart decision-making capabilities, cyber governance mechanisms, and sustainability requirements. The results of the study showed that the relationships between these dimensions were nonlinear, dynamic, and feedback in nature, and the impact of each variable became meaningful in interaction with other network components. The first important finding of the study was the determining role of artificial intelligence maturity, big data infrastructure, and IoT integration as the main drivers of autonomous supply chain transformation. Analysis of centrality and degree of influence indices showed that these variables had the greatest causal impact on other model components and acted as the driving force of digital transformation. One of the most important findings of the study was the identification of “real-time decision-making” as the most central variable of the network, indicating its pivotal role in connecting digital enablers to performance outcomes. Findings from sensitivity analysis and scenario building showed that cybersecurity played a role beyond a control or protection factor and acted as a stabilizing pillar of the entire autonomous structure. A decrease in the maturity level of cyber governance or an increase in cyber threats caused a significant decline in digital trust, reduced chain transparency, weakened real-time decision-making, and ultimately a decline in system resilience. The research results showed that sustainability in the autonomous supply chain was a direct consequence of the smartization of operational processes. Simulation scenarios showed that strengthening digital enablers indirectly reduced waste, increased energy efficiency, reduced carbon emissions, and strengthened economic sustainability.

Keywords

Autonomous Supply Chain

Big Data

Internet of Things

Sustainability

Cybersecurity

Subjects

supply chain management

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