Develops an intelligent analytics framework that enhances supplier selection and manages delay risks in sustainable supply chains, suggesting improved decision-making.
Key Points
This research aims to create a framework that utilizes intelligent analytics for effective supplier selection while managing delay risks in sustainable supply chains.
Developed an analytics framework using machine learning techniques
Implemented supervised learning algorithms such as support vector machine, decision tree, and random forest
Conducted performance analysis of various machine learning models including neural networks
Analyzed supplier data across sustainability dimensions and delay risk
Neural networks showed the most reliable performance in supplier assessment
Intelligent models identified hidden patterns in supplier performance data
Provided robust and explainable rankings for supplier selection
Delivered prescriptive insights that align with sustainability goals