Sustainable Cloud-Based Blockchain Solution Integrated with a Supervised Attention Multi-Scale Temporal Convolutional Network for Efficient Supply Chain Management

Organizations should prefer SCM ability, as it maintains both operating profit and market competition in dynamic markets. The current methods of analysis make poor real -time paves that give rise to sub -standard decisions that thus reduce the quality of operating performance. Research develops SA-MTCN with DSA to improve the management of the supply chain through better performance properties that deal with current control limits. SA-MTCN supply chain requires monitored attention method and multi-level temporal layers through model design to detect the necessary and sophisticated patterns correctly. The Duck Herd algorithm (DSA) acts as an adaptation system for SA-MTCN Hyperpieter adjustment to produce more stable and efficient model operations. A large data set for the supply chain has established the efficiency of the proposed function by achieving 99.9 % accuracy in evaluation tests. Better predictions emerge from this method as the efficiency of the operational supply chains is increased through time -sensitive data analysis and its functionality informed decision results. Experimental testing resulted in a better performance of the SA-MTCN model when customized by DSA which today offers an efficiently reliable solution supply chain.