FoodWise AI — Predict Less. Waste Less. Save More.

Inspiration

Food waste can begin before food is even served. Food providers have to decide how much food to prepare before knowing the actual demand. Demand can change depending on the day of the week, expected customers, holidays, special events, weather, and historical consumption patterns.

This creates a practical challenge: preparing too much can lead to unnecessary food waste and cost, while preparing too little can result in shortages and poor availability.

We built FoodWise AI around a simple idea: better demand visibility can help food providers make better preparation decisions.

What the Project Does

FoodWise AI is a predictive food-demand management prototype designed for college canteens, restaurants, cafeterias, and food-service businesses.

The system uses historical consumption and contextual factors such as expected customers, day of the week, holidays, special events, and weather to estimate expected demand.

Instead of only showing a predicted number, FoodWise AI converts the prediction into a practical preparation recommendation and provides an estimated view of potential food and cost impact.

How It Works

The system follows:

Collect → Analyze → Predict → Recommend → Track

FoodWise AI takes relevant demand inputs, generates a prediction, recommends a preparation quantity, and provides a dashboard for monitoring previous predictions and impact.

How We Built It

The current prototype was built using:

• React
• Vite
• JavaScript
• Tailwind CSS
• Recharts
• LocalStorage

The current prediction engine uses a transparent rule-based baseline approach, combining a weighted historical average with contextual adjustments.

The current prototype does not use a trained machine-learning model.

Prototype Features

The prototype includes:

• Dashboard
• Demand prediction interface
• Analytics
• Prediction history
• Recommended preparation quantity
• Estimated food impact
• Estimated cost impact

The interface is designed to make the prediction understandable and useful for a food-service decision maker.

What We Learned

While developing FoodWise AI, we learned that prediction alone is not enough. A useful solution needs to connect prediction with an actual operational decision.

For this reason, our system focuses on the complete flow:

Historical Data → Context → Demand Prediction → Preparation Recommendation → Tracking

We also learned the importance of keeping the prediction logic transparent and clearly separating the current prototype from future machine-learning capabilities.

Challenges

One of the main challenges was designing a prediction approach that could work in the prototype without claiming access to large real-world datasets or trained machine-learning models.

We therefore implemented a transparent rule-based baseline that can later be replaced or enhanced with trained forecasting models when sufficient real-world data becomes available.

Another challenge was presenting the prediction in a way that is useful to the end user. Instead of stopping at a demand estimate, we connected the prediction to a recommended preparation quantity and impact view.

Future Scope

Future development can include:

• Real sales/POS data integration
• Trained machine-learning forecasting models
• Weather API integration
• Inventory tracking
• Automated alerts
• POS integration
• Advanced forecasting
• Cloud deployment
• Real-world pilot testing and impact measurement

These features are future scope and are not claimed as part of the current prototype.

Impact

FoodWise AI is designed to help food providers make more informed preparation decisions before food is produced.

Potential impact areas include:

• Reducing avoidable food waste
• Improving preparation decisions
• Supporting better food availability
• Improving cost visibility
• Supporting sustainability goals

The actual level of impact would need to be measured through real-world pilot testing using real operational data.

Conclusion

FoodWise AI moves food-waste management from simply reacting to waste toward making better preparation decisions before waste occurs.

FoodWise AI — Predict Less. Waste Less. Save More.

Built With

• css
• javascript
• react
• recharts
• tailwind
• vite