Weather-Ready / Graduate analytics practicum
Planning dinner service with weather forecasts
A restaurant manager needs to decide how many guests to prepare for before dinner begins. I built an AI prototype connecting weather and local context with guest estimates, explanations and feedback after service.
- Setting
- MS in Business Analytics practicum, informed by restaurant discovery interviews.
- My contribution
- Connecting forecasting, explanations, and stored feedback in a working prototype.
- Intended use
- Help managers interpret demand estimates alongside what they know about an upcoming service.
- Project stage
- Prototype; restaurant pilot outcomes remain unmeasured.
An estimate of dinner guests, with a planning range
A weather forecast alone does not tell a restaurant how many people it will serve. Weather-Ready estimates dinner guests using weather and local signals, then explains what affects that estimate.
In the screen below, Wednesday is marked as slower than usual, with heavy rain, an estimate of 136 guests, and a planning range of 112–155. The screen also asks the manager to confirm the service setup. These are values in the interface example, rather than observed restaurant results.
Forecast screen from the Weather-Ready prototype. Guest counts and ranges are values shown in this interface example.
View full-size forecast screenAccounting for patio closures and private events
A closed patio, an early closing, or a private event changes what a normal evening means. The service-plan form lets a manager record those known changes, instead of treating every change in guest numbers as a weather effect.
After service, managers can record what actually happened. I connected forecasts, explanations and stored feedback so planning and later review used the same workflow. Feedback updates the prototype’s stored state for later runs; whether that improves forecast quality still needs testing.
The prototype's service-plan form records operating changes such as a closed patio, early closing, or a private event.
View full-size service-plan screenHow the prototype connects these steps
A FastAPI backend supports a React and TypeScript interface, with DuckDB storing the data and Azure OpenAI-backed agents supporting the workflow. Forecasting logic combines demand signals and explanations; feedback updates stored state for subsequent runs.
How the prototype connects these steps
A FastAPI backend supports a React and TypeScript interface, with DuckDB storing the data and Azure OpenAI-backed agents supporting the workflow. Forecasting logic combines demand signals and explanations; feedback updates stored state for subsequent runs.
What a restaurant pilot would need to test
The practicum produced a working prototype. A restaurant pilot would need to compare its forecasts with a baseline, measure errors under different service conditions and test whether the explanations help managers plan service.