Related Experiment Video
Updated: Jun 19, 2026
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Development of an AI-based restaurant menu demand prediction model utilizing sales and meteorological data
1Department of Food Engineering, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan, Chungcheongnam-do 31116 Republic of Korea.
Food Science and Biotechnology
|October 20, 2025
Summary
This study introduces an AI system for predicting daily restaurant sales, crucial for reducing food waste and improving efficiency. The system uses historical data and weather information to forecast demand for individual menu items.
Area of Science:
- Artificial Intelligence
- Operations Research
- Data Science
Background:
- Accurate demand forecasting is vital for restaurant operational efficiency.
- Key challenges include inventory management and minimizing food waste.
Purpose of the Study:
- To develop an AI-based system for predicting menu-specific daily sales.
- To enhance restaurant operational efficiency and sustainability through data-driven insights.
Main Methods:
- Utilized historical sales and meteorological data (2021-2023).
- Developed deep neural networks for multi-class classification of ~384 menu items.
- Implemented flexible visualization for sales prediction analysis.
Main Results:
- Achieved a high predictive performance with a mean Pearson correlation coefficient of 0.7945.
- Demonstrated the system's ability to forecast sales for individual menu items.
- Showcased practical visualization tools for sales data.
Conclusions:
- AI-driven demand prediction is feasible and effective for the restaurant industry.
- The system offers potential for transforming food service operations.
- Highlights a pathway toward increased sustainability and efficiency in food service.
Related Concept Videos
01:07What is Weather?
19.7K
Overview
19.7K
01:16What is Climate?
20.4K
Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
20.4K
01:03Prediction Intervals
3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.3K
01:29Application of Differentiation to Business
5
Calculus offers essential techniques for businesses seeking to optimize pricing strategies and revenue. In this case, a bakery wants to determine the ideal price and daily sales volume to maximize revenue. By modeling how changes in price affect demand and revenue, the bakery can apply calculus to make data-driven decisions.The demand function relates the price per cupcake to the number of cupcakes sold and captures how lower prices increase sales. Based on market data, the demand function can...
5