Mai Shan Yun Management Dashboard

Inspiration

Restaurant managers struggle with inventory decisions daily: when to reorder, what's being wasted, and where costs can be cut. We wanted to turn raw sales data into clear, actionable insights that prevent both stockouts and overstocking.

What it does

Our dashboard analyzes 6 months of sales data to provide real-time inventory monitoring, automated reorder alerts, cost optimization recommendations, and predictive forecasting. It identifies critical stock levels, tracks ingredient usage patterns, and highlights cost-saving opportunities.

How we built it

We built an interactive Streamlit dashboard using Python, Pandas for data processing, and Plotly for visualizations. We merged sales data with ingredient recipes to calculate actual usage, compared supply levels against consumption rates, and implemented machine learning models for revenue forecasting.

Challenges we ran into

Mapping menu items to ingredient usage was complex since items share ingredients. We had to handle inconsistent data formats across monthly files and create a robust ingredient mapping system. Performance optimization was also challenging with large datasets.

Accomplishments that we're proud of

We successfully integrated shipment tracking, cost analysis, and predictive analytics into one cohesive dashboard. Our system accurately identifies critical stock levels and provides specific reorder recommendations. The interface is intuitive enough for non-technical restaurant managers to use.

What we learned

We learned how to work with real-world messy data, implement effective data visualization strategies, and translate technical analysis into business value. We also gained experience collaborating as a team on different dashboard components simultaneously.

What's next for Mai Shan Yun Management Dashboard

We plan to add automated email alerts for critical stock, integrate with supplier APIs for direct ordering, implement more sophisticated forecasting models, and expand to support multiple restaurant locations with comparative analytics.