Convergence of Blockchain and Machine Learning for Intelligent Supply Chain Management: A Systematic Analysis of Synergies, Applications, and Emerging Trends

DOI

10.2991/978-94-6463-894-3_7How to use a DOI?

Keywords

Blockchain; Machine Learning; Intelligent Forecasting; Inventory Optimization; Sustainability; Resilient Supply Chains; Industry 5.0 & Digital Transformation

Abstract

This extensive review analyzes 255 peer-reviewed articles (2020-2025) from the Scopus database to investigate the synergistic integration of blockchain and machine learning (ML) technologies in supply chain management (SCM). Our systematic review highlights five convergence themes at the core: (1) Immutable Traceability - blockchain-based decentralized ledgers ensure product path visibility and ML identifies supply chain data anomalies; (2) Intelligent Forecasting - ML-based algorithms drive demand forecasting and inventory optimization via blockchain-verified historical information; (3) Automated Compliance - smart contracts enforce business rules verified by ML-tested processes; (4) Sustainable Operations - blockchain records carbon footprint and ML optimizes resource usage and waste minimization; and (5) Cyber-Resilience - blockchain-based encryption protects IoT networks with ML-driven threat discovery. Industry-specific findings uncover revolutionary effects Agri-food supply chains achieve 20-30% waste reduction with blockchain origin tracking and ML spoilage forecasting, Pharmaceutical logistics accomplish 99.7% counterfeiting prevention through blockchain provenance tracking and ML pattern detection, Manufacturing demonstrates 25% maintenance cost savings through blockchain-component histories and ML failure prediction Implementation challenges are measured across the literature: scalability constraints impact 68% of real-time applications, integration costs stand at $2.4M on average for businesses, and skill gaps affect 82% of adoption efforts. Blockchain structures that are quantum-resistant, generative AI for supply chain planning, and standards across industries are among the priorities for future research. SCM systems that are transparent, self-optimizing, and robust in line with Industry 5.0 are made possible by this synergy.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Prerna Jain
AU  - Komal Arora
PY  - 2025
DA  - 2025/11/10
TI  - Convergence of Blockchain and Machine Learning for Intelligent Supply Chain Management: A Systematic Analysis of Synergies, Applications, and Emerging Trends
BT  - Proceedings of the International Conference on Policies, Processes and Practices for transforming Underdeveloped Economies into Developed Economies (PPP-UD 2025)
PB  - Atlantis Press
SP  - 77
EP  - 96
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-894-3_7
DO  - 10.2991/978-94-6463-894-3_7
ID  - Jain2025
ER  -