AI-driven supply chain optimization for enhanced efficiency in the energy sector
1 Independent Researcher, Nigeria.
2 Shell Petroleum Development Company, Lagos Nigeria.
3 Shell Energy Nigeria PLC.
4 Shell Nigeria Gas (SEN/ SNG), Nigeria.
Review Article
Magna Scientia Advanced Research and Reviews, 2021, 02(01), 087-108
Article DOI: 10.30574/msarr.2021.2.1.0060
Publication history:
Received on 12 July 2021; revised on 28 August 2021; accepted on 30 August 2021
Abstract:
Artificial Intelligence (AI) has revolutionized supply chain management, offering significant potential for optimizing efficiency in the energy sector. The integration of AI-driven technologies into supply chain processes enables predictive analytics, real-time monitoring, and automated decision-making, which contribute to improving operational performance, reducing costs, and enhancing sustainability. This paper explores the role of AI in optimizing supply chains within the energy industry, focusing on key areas such as demand forecasting, inventory management, transportation, and maintenance scheduling. AI-driven algorithms can analyze vast amounts of data to predict demand patterns, allowing energy companies to optimize inventory levels and minimize the risks associated with overstocking or stockouts. Furthermore, AI can enhance logistics and transportation efficiency by optimizing routes, reducing fuel consumption, and improving delivery timelines, leading to significant cost savings. AI's impact extends to predictive maintenance, where machine learning models can analyze sensor data to predict equipment failures before they occur, minimizing downtime and maintenance costs. This capability is particularly crucial in the energy sector, where equipment reliability is vital for uninterrupted service delivery. Additionally, AI-driven supply chain optimization promotes sustainability by optimizing energy use, reducing waste, and improving resource management. It enables energy companies to meet regulatory standards, achieve sustainability targets, and enhance corporate social responsibility (CSR) initiatives. In conclusion, AI-driven supply chain optimization offers transformative benefits for the energy sector by enhancing efficiency, reducing costs, and promoting sustainability. As AI technologies continue to evolve, their application in supply chain management will become increasingly critical for the energy sector’s competitiveness and operational excellence. This paper highlights the need for energy companies to embrace AI technologies to maintain a competitive edge, reduce environmental impact, and improve overall supply chain resilience. The future of supply chain optimization in the energy sector lies in the continued adoption and integration of AI for smarter, more efficient, and sustainable operations.
Keywords:
AI; Supply Chain Optimization; Energy Sector; Predictive Analytics; Demand Forecasting; Inventory Management; Transportation Efficiency; Predictive Maintenance; Sustainability; Resource Management
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0