Digital Twin-Enabled Supply Chain Simulation for Improving, Renewable Energy Supply Chain Resilience
1 Department of Business Administration, MBA (Business Intelligence & Data Analytics), College of Business & Economics, Fayetteville State University, NC, United States of America.
2 Msc Information Technology Management, School of Information Technology, Western Governors University, Salt Lake City, UT.
3 Business Administration, Sainte Felicite University, Cotonou, BN.
4 Geophysicist / Data Analyst, Danvic Petroleum International, Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2021, 09(02), 214-231
Publication history:
Received on 15 January 2021; revised on 22 February 2021; accepted on 25 February 2021
Abstract:
The renewable energy sector faces unprecedented supply chain challenges characterized by geographic dispersion of resources, intermittent production patterns, complex logistics networks, and vulnerability to disruptions. This study investigates the application of digital twin technology integrated with advanced simulation modeling to enhance renewable energy supply chain resilience. Employing a mixed-methods research design, we conducted simulation experiments using real-world data from 47 renewable energy installations across solar, wind, and hydroelectric sectors, complemented by 34 expert interviews with supply chain managers and digital transformation specialists. Our findings reveal that digital twin-enabled supply chain simulations achieve 43% improvement in disruption prediction accuracy, 38% reduction in inventory carrying costs, 52% faster response to supply chain anomalies, and 31% enhancement in overall supply chain resilience metrics compared to traditional supply chain management approaches. The study demonstrates that integration of IoT sensors, real-time data analytics, and predictive modeling within digital twin frameworks enables proactive risk mitigation, dynamic resource optimization, and adaptive capacity planning. We develop a comprehensive Digital Twin Maturity Model (DTMM) for renewable energy supply chains, identifying five evolutionary stages from basic monitoring to fully autonomous adaptive systems. Statistical analysis (N=47 installations) reveals strong positive correlations between digital twin sophistication and supply chain performance (r=0.72, p<0.001), with particularly significant impacts on demand forecasting accuracy (β=0.68, p<0.001) and disruption recovery time (β=-0.54, p<0.01). The research contributes theoretically by extending digital twin frameworks to renewable energy contexts and practically by providing implementation roadmaps, capability requirements, and value quantification models. Findings indicate that successful digital twin deployment requires organizational readiness across technological infrastructure, data governance, analytical capabilities, and change management dimensions, with median implementation costs of $285K-$840K yielding average payback periods of 14-22 months through operational efficiency gains and risk mitigation benefits.
Keywords:
Digital Twin; Supply Chain Simulation; Renewable Energy; Resilience; Industry 4.0; IoT; Predictive Analytics; Risk Management; Sustainability; Operational Efficiency
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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