Authors: Dr. Srimathi Kannan
Abstract: The global supply chain network is currently more vulnerable to disruptions that can be caused by pandemics, geopolitics, and other reasons. In such cases, centralized and opaque logistics infrastructures are exposed to risks. This study recommends a reliable supply chain management framework that utilizes blockchain technology, IoT sensors, a hybrid deep learning algorithm to forecast consumer demand, and disruption management through smart contracts. The proposed architecture relies on the Hyperledger Fabric, a permissioned blockchain network, and guarantees data immutability and transparency. A temporal convolutional network with an attention mechanism enables forecasting demand at 95.2% accuracy over a 12-week time frame. After detecting a disruption, the automated smart contract system will engage in dynamic routing, inventory redistribution, and supplier substitution. Simulating the proposed solution on a multi-tier supply chain network with over 100 nodes resulted in 67% faster disruption resolution compared to conventional models and 94% customer satisfaction during disruption events, while conventional models were able to serve just 62% of consumers.
DOI: https://doi.org/10.5281/zenodo.20411670
