Innovative Integration of Machine Learning Predictive Models within Blockchain Frameworks for Supply Chain Fault Tolerance

The study came up with a new way to make the supply chain more fault-tolerant by combining Machine Learning Predictive Models with Blockchain Frameworks (MLBH). This idea was put to the test against six common ways to handle problems in the supply chain. These are Lean Supply Chain Management, Supply Chain Risk Assessment, Demand Forecasting Models, Vendor-Managed Inventory (VMI), and Inventory Optimization. We judged success by how well it could be scaled, how much money it saved, how accurate the predictions were, how open it was, and how much it couldn't be changed. It was clear that the MLBH method worked better. It turned out to be better than traditional methods in every way, such as being open, scalable, saving money, protecting data, not being able to be changed, and making accurate predictions. Businesses that want to improve their performance and return on investment (ROI) should think about the suggested method, which has been shown to make supply chains more resilient and efficient. The cost-benefit analysis showed that the suggested answer had a strong return on investment (ROI), even though it would require some upkeep and implementation costs.