AI-Enabled ETO Quoting & Supply Chain Decision Support

Designed an AI-enabled decision support framework to transform the Engineer-to-Order (ETO) quotation process by integrating Supply Chain, Procurement, Engineering, Sales, and Finance. The project analyzed end-to-end quoting workflows, identified operational bottlenecks, benchmarked leading enterprise solutions, and proposed an AI-driven operating model leveraging Microsoft Power Platform to improve quotation lead time, cost accuracy, supplier selection, and sourcing decisions through intelligent automation and analytics.