Data Consultant

As a Data Consultant with the Business Intelligence Group at UIUC, I partnered with a Japan-based food export/import company to design a data-driven demand forecasting solution that enhanced business efficiency and operational agility. By integrating 12 external supply chain indicators such as CPI, GDP, and exchange rates, I built a comprehensive model to forecast product demand with greater precision and reliability.

Improved inventory turnover through machine learning–driven demand forecasting and smarter supply chain decisions.

To achieve this, I implemented and benchmarked five machine learning models (SARIMA, XGBoost, Random Forest, LSTM, and Linear Regression) to identify the best-performing approach for dynamic inventory optimization. This analytical framework enabled the client to make data-backed procurement decisions, cutting costs and boosting forecast accuracy through continuous model refinement and business collaboration.