Oil, gas, and other newer renewable energy forms constitute an energy sector that requires efficient logistics systems to ensure that energy is produced, delivered, and distributed with the required timely fashion. Within the last several years, prior to 2023 the increasing interest is observed regarding using the power of data-driven models to solve long-existing inefficiencies and struggles in the energy logistics processes. The paper examines the application of data analytics, predictive models and optimisation algorithms to enhance performance of operational activities in major logistics disciplines including inventory management, transportation, fleet management, and supply chain integration. The review of the available literature and industry case studies, through a detailed study, has identified the critical success factors and technological enablers which enable adoption of data driven systems in energy logistics. Implementation issues quoted in the study are quality of data and technological limitations and ability of the organization to adopt technology. Findings suggest that despite the fact that the data-driven approach is not yet implemented universally throughout the sector has significant potential to lower operations costs, create better decision-making, and improve the responsiveness to the market needs. The study then ends up with recommendations that companies in the energy industry should invest in data infrastructure, enhance analytical capabilities, and embrace scalable models to enable them to arrive at long term logistics efficiency.
Maxwell Collins Bawuah (Mon,) studied this question.
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