Developing a framework for AI-driven optimization of supply chains in energy sector
1 Kent Business School, University of Kent, UK.
2 Independent Researcher, Portharcourt Nigeria.
3 Independent Researcher, Lagos Nigeria.
Review
Global Journal of Advanced Research and Reviews, 2023, 01(02), 082-0101.
Article DOI: 10.58175/gjarr.2023.1.2.0064
Publication history:
Received on 09 September 2023; revised on 10 December 2023; accepted on 13 December 2023
Abstract:
The energy sector is increasingly recognizing the potential of Artificial Intelligence (AI) to optimize supply chain operations, enhance efficiency, and reduce costs. This paper presents a framework for AI-driven optimization of supply chains within the energy industry, focusing on the integration of AI technologies to address key challenges such as demand forecasting, resource allocation, and predictive maintenance. AI’s ability to process large volumes of data, uncover patterns, and enable real-time decision-making offers significant advantages over traditional supply chain management systems. By applying machine learning, deep learning, and natural language processing, energy companies can optimize their supply chain processes, improve operational visibility, and minimize downtime, leading to enhanced service delivery and cost savings. This framework explores how AI can be integrated into different stages of the energy supply chain, including sourcing, distribution, and consumption. AI-based demand forecasting models enable more accurate predictions of energy requirements, allowing for better resource planning and reduced waste. Additionally, AI tools can optimize logistics by analyzing transportation networks and providing real-time data on delays, route optimization, and fleet management. Predictive maintenance, driven by AI algorithms, can forecast equipment failures before they occur, reducing unplanned outages and enhancing asset longevity. Moreover, the paper highlights the role of AI in achieving sustainability goals by improving energy efficiency, supporting renewable energy integration, and reducing carbon footprints across the supply chain. It emphasizes the need for a strategic approach to AI implementation, including the development of data infrastructure, algorithm transparency, and collaboration among stakeholders. The paper concludes by proposing a comprehensive roadmap for energy companies to adopt AI-driven supply chain solutions, ensuring a transition towards smarter, more efficient, and sustainable operations. Challenges such as data quality, technological integration, and workforce adaptation are discussed, along with recommendations for overcoming these barriers to successful AI implementation in the energy sector.
Keywords:
AI; Supply Chain Optimization; Energy Sector; Demand Forecasting; Predictive Maintenance; Sustainability; Machine Learning; Logistics; Resource Allocation
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