Predictive Analytics for Mitigating Supply Chain Disruptions in Energy Operations

• Author(s): Ekene Cynthia Onukwulu ; Ikiomoworio Nicholas Dienagha ; Wags Numoipiri Digitemie ; Peter Ifechukwude Egbumokei
• Paper ID: 1702929
• Page: 256-282
• Published Date: 30-09-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 3 September-2021

Abstract

Predictive analytics has become a critical tool for mitigating supply chain disruptions in energy operations, providing organizations with the capability to anticipate and address potential challenges before they impact the business. In the energy sector, supply chain disruptions can stem from a variety of factors, including market fluctuations, equipment failures, geopolitical events, and natural disasters. By leveraging advanced data analytics techniques, predictive models can forecast potential disruptions and recommend proactive measures to minimize their effects, ensuring the continuity of operations and reducing associated risks. The application of predictive analytics in energy supply chains involves the collection and analysis of large datasets, such as historical performance data, market trends, weather patterns, and supplier performance metrics. Machine learning algorithms and statistical models are used to identify patterns and correlations that can predict future disruptions. These predictions enable energy organizations to optimize inventory management, refine procurement strategies, and enhance logistics planning, ultimately improving operational efficiency and reducing costs. Furthermore, predictive analytics aids in identifying critical vulnerabilities within the supply chain, such as reliance on single-source suppliers or regions prone to natural disasters. By addressing these vulnerabilities, energy companies can diversify their supply chains, develop contingency plans, and establish more resilient operational frameworks. The integration of real-time data with predictive models further enhances the accuracy of forecasts, allowing companies to respond more rapidly to emerging threats. Key benefits of predictive analytics include improved decision-making, reduced downtime, cost savings, and enhanced risk management. However, successful implementation requires a robust data infrastructure, skilled data scientists, and a strong organizational commitment to adopting data-driven decision-making processes. In conclusion, predictive analytics represents a transformative approach to mitigating supply chain disruptions in energy operations, providing companies with the tools necessary to navigate an increasingly complex and volatile global market.

Keywords

Predictive Analytics, Supply Chain Disruptions, Energy Operations, Machine Learning, Risk Management, Inventory Optimization, Data-Driven Decision-Making, Operational Efficiency, Resilience

Citations

IRE Journals:
Ekene Cynthia Onukwulu , Ikiomoworio Nicholas Dienagha , Wags Numoipiri Digitemie , Peter Ifechukwude Egbumokei "Predictive Analytics for Mitigating Supply Chain Disruptions in Energy Operations" Iconic Research And Engineering Journals Volume 5 Issue 3 2021 Page 256-282

IEEE:
Ekene Cynthia Onukwulu , Ikiomoworio Nicholas Dienagha , Wags Numoipiri Digitemie , Peter Ifechukwude Egbumokei "Predictive Analytics for Mitigating Supply Chain Disruptions in Energy Operations" Iconic Research And Engineering Journals, 5(3)