Enhancing Supply Chain Resilience: Application and Optimization of Blockchain Technology in Risk Management

EasyChair Preprint 15818

Abstract

This This paper introduces a novel solution to enhance supply chain risk management: the "Double Feedback Fuzzy Neural-based Blockchain Network (DF-BC)." This model is specifically designed to effectively mitigate risks for small- and medium-sized enterprises (SMEs) by integrating the advanced predictive capabilities of Double Feedback Fuzzy Neural Networks (DFFNN) with the security and transparency of blockchain technology. Utilizing blockchain, the model ensures a transparent and secure supply chain system by storing data in immutable blocks, thus guaranteeing data integrity and safety. Concurrently, the DFFNN processes this data, enabling precise risk management and accurate risk assessments. The foundation of this model is built on existing research, which provides effective techniques for addressing financial risks. However, the complexity of emerging risks, which can surpass traditional risk management methods, poses significant threats to the financial stability of supply chains. The DF-BC model offers a cutting-edge solution, guiding stakeholders toward anticipated outcomes, minimizing risks, and stabilizing the supply chain sector. Simulation results underscore the model's effectiveness, demonstrating improved risk prediction accuracy, enhanced processing efficiency, and overall better risk management practices within supply chain operations.

Keyphrases: Blockchain, DFFNN, Data Security, Predictive Analytics, SME, credit risk, risk management, supply chain

@booklet{EasyChair:15818,
  author    = {Prashnatita Pal and Bikash Chandra Sahana and Rituparna Bhattacharya and Jayanta Poray},
  title     = {Enhancing Supply Chain Resilience: Application and  Optimization of Blockchain Technology in Risk Management},
  howpublished = {EasyChair Preprint 15818},
  year      = {EasyChair, 2025}}