Document Type : Research Paper
Authors
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.
Abstract
The oil and gas industry faces challenges such as complexity of a multi-layered and global supply network, operational and geopolitical risks, cybersecurity threats in critical infrastructure, sustainability pressures, and need for real-time data-driven decision-making. The main objective of the study is to design and analyze an autonomous supply chain framework based on artificial intelligence, big data and Internet of Things in the oil and gas industry with an emphasis on sustainability and cybersecurity. The research is 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 conducted using the Fuzzy Cognitive Mapping method. The study period is summer and fall 2025 and winter 2026. The population of the study is Iranian oil and gas industry experts, which completed by 23 experts using a structured questionnaire using purposive sampling. Achieving autonomy in the oil and gas supply chain is contingent upon the synergy and application of advanced digital technologies. Within this context, operational intelligence serves as a crucial mediating factor, transforming technological capabilities into economic and environmental value. In other words, the establishment of data-driven digital infrastructure is the primary prerequisite for attaining sustainability, while resilience and trust within this ecosystem can only be realized through robust cybersecurity. The proposed model, functioning as a decision support system, empowers managers in this industry to optimally prioritize digital investments, evaluate the implications of security policies, and simulate various digital transformation scenarios prior to operationalization.
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