Work

Efficiency enhancement of energy supply chains using a machine learning-driven network evaluation framework for blockchain adoption

Year: 2025

Type: article

Abstract: The Oil and Gas (O&G) sector is essential to the global energy supply, yet it still struggles with inefficiencies caused by conventional, paper-driven decision-making processes in its supply chain. This study aims to assess the integration of blockchain technology as a strategic solution to these challenges. It examines strategies for blockchain integration considering organizational, supply chain, technological, and external factors. A novel approach based on four network-based data-driven optimization models is utilized to investigate a case study in Norway's O&G sector over a five-year period (April to October 2020). These models incorporate uncertainty to assess the effectiveness of various blockchain integration strategies. The findings demonstrate that “single-use strategy” is effective in low-risk scenarios, while “substitution strategy” is optimal in high-risk scenarios. The application of the Super-Efficiency Network Data Envelopment Analysis (SENDEA) model represents its robustness in evaluating blockchain adoption within the industry. Furthermore, a an efficient Machine Learning (ML) technique; i.e., Principal Component Analysis (PCA), is utilized to further assess blockchain adoption strategies. Therefore, policymakers are recommended to support risk-based blockchain adoption by directing organizations to implement single-use strategies in low-risk environments and substitution strategies in high-risk circumstances. Additionally, they can take advantage of practical evaluation frameworks provided by the SENDEA and uncertain models to systematically appraise and prioritize blockchain strategies based on their potential impacts.

Source: Energy Strategy Reviews

DOI: 10.1016/j.esr.2025.101816

Authors: Ardavan Babaei, Erfan Babaee Tirkolaee, Shahryar Sorooshian, Sadia Samar Ali, Gongming Wang