Predicted Analysis of Supply Chain Collaboration Using Machine Learning Approach

Supply chain cooperation means a long-term relationship that mainly involves organisations that usually engage in information sharing and work together to plan and orchestrate changes to the business processes in a bid to improve performance. To enhance the formation of supply chain relations, this research explores how ML can address transparency issues, misalignments in incentives, and inefficiencies. This research work adopted a dataset of differing 180,519 cases in an effort to improve inventory control, demand estimations, and logistics chaining through predictive analysis. Maintaining data integrity and effectively dealing with missing values prompted the 70:15:15 data split across training, validation, and test sets. Finally, after training and testing, a new model, namely Support Vector Machine (SVM), fared better than the baseline model, which is K-Nearest Neighbours (KNN). The SVM achieved better results than the KNN model with 98. 91% accuracy, 98. 96% sensitivity, and 98. 65% specificity. These findings highlight how cooperation and efficiency in the supply chain can be enhanced through the use of machine learning. Integrating these methods into current supply chain systems for better collaboration and resilience may be the focus of future research into real-time data processing.