AN UNCERTAINTY-AWARE MULTI-CRITERIA FRAMEWORK FOR EVALUATING BLOCKCHAIN-BASED TRACEABILITY SYSTEMS

Abstract

Blockchain technology has transformed data management by offering a decentralized, transparent, and secure way to record transactions. In order to improve supply chain visibility, security, authenticity, and dependability, these blockchain-based tracking and tracing system capabilities are essential. Choosing the best architecture and configuration becomes a difficult decision-making task as industries use these systems more and more. Furthermore, a wide range of factors, including cost, security, and scalability, influence decision-making. This study assesses different blockchain-based tracking systems using the fuzzy framework. The COCOSO model was introduced in the study within the Picture Fuzzy (PF) framework. By considering several conflicting factors, this decision-making tool assesses the actual situation of a blockchain-based tracking and tracing system. Furthermore, the importance of decision-making in blockchain-based systems has been established, along with their implications for society, risk reduction, ethics, and the environment. Additionally, a comparison analysis was carried out to demonstrate the effectiveness of the suggested model in order to confirm the validity and dependability of the suggested strategy. The concluding remarks, which outline the topic's advantages, disadvantages, and future course, bring the conversation to an end. KEYWORDS: COCOSO method, multi-criteria decision making (MCDM), blockchain-based tracking and tracing system, and picture fuzzy number (PFN)