Convergence of Blockchain, IoT, and AI for Enhanced Traceability Systems: A Comprehensive Review

Abstract

The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a bibliometric and systematic literature review (SLR) to examine trends, research patterns, and methodologies in integrating BC IoT and AI into traceability systems. Bibliometric analysis of 530 documents from SCOPUS (2014–2024) identified key trends, while the SLR, conducted across multiple databases following PRISMA guidelines, refined the dataset to 43 peer-reviewed studies based on inclusion criteria. Recent research output has notably increased, focusing on agricultural supply chains and SCM, with India and China leading in publications. The analysis shows a predominance of experimental and hybrid methodologies, using Ethereum and Hyperledger Fabric as key platforms. Key trends include AI-driven analytics, real-time IoT data collection, and the need for secure, tamper-proof data by BC. However, interoperability, scalability, and standardization challenges hinder adoption. The paper proposes a four-layer framework for integrating BC, IoT, and AI to improve transparency, security, and efficiency and highlights the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.

Keywords

• Artificial intelligence
• bibliometric analysis
• blockchain
• Internet of Things (IoT)
• traceability
• supply chain and systematic literature review