Blockchain-Based Supply Chain Management Using Machine Learning: Analyzing Decentralized Traceability and Transparency Solutions for Optimized Supply Chain Operations
Keywords:
Blockchain, Machine Learning, Supply Chain Management, Traceability, Transparency, Decentralization, Predictive Analytics, Anomaly Detection, Efficiency, Fraud ReductionAbstract
Blockchain technology has revolutionized various industries by offering decentralized, transparent, and secure solutions. In the realm of supply chain management, blockchain's potential is further enhanced when combined with machine learning (ML). This paper provides a comprehensive analysis of blockchain-based supply chain management using ML, focusing on decentralized traceability and transparency solutions. We discuss how blockchain and ML integration can optimize supply chain operations, enhance traceability, and improve transparency. Key topics include the role of blockchain in establishing a decentralized ledger for supply chain data, ML algorithms for predictive analytics and anomaly detection, and the benefits of decentralized traceability and transparency in improving supply chain efficiency and reducing fraud. We also explore challenges such as scalability, interoperability, and data privacy, along with future prospects for this innovative approach.
References
Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
Christidis K, Devetsikiotis M. Blockchains and Smart Contracts for the Internet of Things. IEEE Access. 2016;4:2292-2303. doi: 10.1109/ACCESS.2016.2566339.
Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
Crosby M, Pattanayak P, Verma S, Kalyanaraman V. Blockchain technology: Beyond bitcoin. Appl Innov Rev. 2016 May;2(6):6-13.
Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.
Dubey R, Gunasekaran A, Childe SJ, Papadopoulos T. Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. Int J Prod Econ. 2019 Jan; 34-51. doi: 10.1016/j.ijpe.2018.11.009.
Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
Huang K, Liu G, Xu X, Zhang L. A deep learning model for smart grid data imputation considering spatiotemporal correlation. IEEE Trans Smart Grid. 2021 Jan;12(1):291-300. doi: 10.1109/TSG.2020.3008104.
Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
Korpela K, Hallikas J, Dahlberg T. Digital supply chain transformation toward blockchain integration: A case study of a small and medium-sized enterprise. J Comput Inf Syst. 2017 Jul 3;58(4):316-326. doi: 10.1080/08874417.2017.1375787.
Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.
Liang X, Shetty S, Tosh D, Kamhoua C, Kwiat K, Njilla L. ProvChain: A Blockchain-based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability. In: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE Computer Society. 2017 May 14 (pp. 468-477).
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