AI-Enhanced Supply Chain Transparency Using Blockchain

A Case Study in Global Trade

Authors

  • Emily Smith ., Associate Professor, Department of Supply Chain Management, Massachusetts Institute of Technology, Cambridge, MA, USA Author

Keywords:

Artificial Intelligence, Blockchain Technology, Supply Chain Transparency, Global Trade, Real-Time Data Analytics

Abstract

This paper presents a case study on the integration of artificial intelligence (AI) and blockchain technology to enhance supply chain transparency, focusing on how real-time data analytics powered by AI can improve tracking and accountability. In today’s complex global trade environment, the need for enhanced transparency in supply chains has never been more crucial. Traditional supply chain systems often suffer from inefficiencies, lack of visibility, and trust issues among stakeholders. This study examines how AI algorithms can analyze vast amounts of data generated along the supply chain, while blockchain technology ensures the integrity and immutability of this data. By exploring a specific case study, this paper highlights the practical applications of these technologies in fostering transparency, reducing fraud, and improving decision-making processes. The findings underscore the potential for AI and blockchain to revolutionize supply chain management, offering a blueprint for future implementations across various industries.

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References

Gayam, Swaroop Reddy. "Artificial Intelligence in E-Commerce: Advanced Techniques for Personalized Recommendations, Customer Segmentation, and Dynamic Pricing." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 105-150.

Chitta, Subrahmanyasarma, et al. "Decentralized Finance (DeFi): A Comprehensive Study of Protocols and Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 124-145.

Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Predictive Maintenance of Banking IT Infrastructure: Advanced Techniques, Applications, and Real-World Case Studies." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 86-122.

Putha, Sudharshan. "AI-Driven Predictive Analytics for Maintenance and Reliability Engineering in Manufacturing." Journal of AI in Healthcare and Medicine 2.1 (2022): 383-417.

Sahu, Mohit Kumar. "Machine Learning for Personalized Marketing and Customer Engagement in Retail: Techniques, Models, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 219-254.

Kasaraneni, Bhavani Prasad. "AI-Driven Policy Administration in Life Insurance: Enhancing Efficiency, Accuracy, and Customer Experience." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 407-458.

Vangoor, Vinay Kumar Reddy, et al. "Energy-Efficient Consensus Mechanisms for Sustainable Blockchain Networks." Journal of Science & Technology 1.1 (2020): 488-510.

Kondapaka, Krishna Kanth. "AI-Driven Demand Sensing and Response Strategies in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 459-487.

Kasaraneni, Ramana Kumar. "AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 488-530.

Pattyam, Sandeep Pushyamitra. "AI-Enhanced Natural Language Processing: Techniques for Automated Text Analysis, Sentiment Detection, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 371-406.

Kuna, Siva Sarana. "The Role of Natural Language Processing in Enhancing Insurance Document Processing." Journal of Bioinformatics and Artificial Intelligence 3.1 (2023): 289-335.

George, Jabin Geevarghese. "Utilizing Rules-Based Systems and AI for Effective Release Management and Risk Mitigation in Essential Financial Systems within Capital Markets." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 631-676.

Katari, Pranadeep, et al. "Cross-Chain Asset Transfer: Implementing Atomic Swaps for Blockchain Interoperability." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 102-123.

Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.

Venkata, Ashok Kumar Pamidi, et al. "Implementing Privacy-Preserving Blockchain Transactions using Zero-Knowledge Proofs." Blockchain Technology and Distributed Systems 3.1 (2023): 21-42.

Namperumal, Gunaseelan, Akila Selvaraj, and Deepak Venkatachalam. "Machine Learning Models Trained on Synthetic Transaction Data: Enhancing Anti-Money Laundering (AML) Efforts in the Financial Services Industry." Journal of Artificial Intelligence Research 2.2 (2022): 183-218.

Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Debasish Paul. "AI-Driven Synthetic Data Generation for Financial Product Development: Accelerating Innovation in Banking and Fintech through Realistic Data Simulation." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 261-303.

Pradeep Manivannan, Priya Ranjan Parida, and Chandan Jnana Murthy, “Strategic Implementation and Metrics of Personalization in E-Commerce Platforms: An In-Depth Analysis”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, pp. 59–96, Aug. 2021

Yellepeddi, Sai Manoj, et al. "Blockchain Interoperability: Bridging Different Distributed Ledger Technologies." Blockchain Technology and Distributed Systems 2.1 (2022): 108-129.

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Published

18-09-2024

How to Cite

[1]
Emily Smith, “AI-Enhanced Supply Chain Transparency Using Blockchain: A Case Study in Global Trade”, Distrib Learn Broad Appl Sci Res, vol. 10, pp. 309–316, Sep. 2024, Accessed: Nov. 24, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/156

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