An Integrated Approach to Optimize Risk in SCM using Blockchain and Machine Learning Techniques

Abstract

In the dynamic field of supply chain management (SCM), identifying and mitigating risks is crucial for sustaining resilience and efficiency. Conventional risk management strategies frequently inadequately handle the complexities and uncertainties intrinsic to contemporary supply networks. This study investigates a hybrid approach that combines blockchain technology with machine learning to improve risk detection and management in supply chain management. Blockchain provides a decentralised and transparent system for monitoring supply chain operations, guaranteeing data integrity and transparency. Machine learning enhances this process by examining extensive amounts of past and present information to discern trends, forecast future dangers, and propose mitigation solutions. The suggested system utilises blockchain's inviolability and machine learning's predictive powers to tackle significant difficulties including identifying fraud, demand forecasting, supplier assessment, and disruption prediction. Case studies and quantitative assessments illustrate the efficacy of the hybrid method in mitigating vulnerabilities and enhancing decision-making. This research enhances current understanding in digital supply chain management and offers practical insights for practitioners aiming to implement novel technology to alleviate supply chain hazards. This hybrid system utilises the immutable characteristics of blockchain to provide safe and open data storage, while machine learning models derive actionable insights, hence improving productivity and making decisions. The experiment demonstrates four machine learning models and we achieved 97% accuracy for two model for Random Forest and Support Vector Machine that outperform from other models.