1. Designed a data-driven framework to optimize inventory policies (s, S policies) and minimize total supply chain costs, including holding, procurement, and stockout penalties. 2. Implemented Gradient Boosting (XGBoost/LightGBM) and Prophet models to generate robust, multi-horizon demand forecasts, mitigating the bullwhip effect. 3. Developed a comprehensive cost function and used Monte Carlo simulation to assess the impact of fluctuating lead times and demand volatility on operational expenditure. 4. Determined optimal reorder points and order quantities to meet a target service level while significantly reducing holding and shortage costs.