Cafeteria Order Quantity Forecasting for Repeat Visitors
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Solution Overview
Problem
Conventional systems fail to accurately predict order quantities in high-volume supply cafeterias under closed environments, where regular visitors place orders in a concentrated manner, leading to inefficiencies and waste due to reliance on human intuition and lack of real-time data analysis.
Innovation Solution
An order quantity predicting system using Bayesian statistical methods to analyze past order history and real-time data from cafeteria cash registers, generating and updating order prediction models for individual users and attributes, and adjusting predictions in real-time based on actual order history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional visitor prediction models are used, then general order quantity prediction is possible, but accurate prediction under specific conditions with repeated visitors cannot be achieved
Solution Approach 1:
The system segments visitors into different types (repeated visitors vs. new visitors) and creates separate prediction models for each segment. The order prediction model specifically handles repeated visitors by utilizing their historical order data, while other visitors are handled by general prediction methods. This segmentation allows the system to achieve high accuracy for specific visitor conditions without compromising overall system versatility.
2Measurement precision
If detailed user attribute information and order history are collected, then prediction accuracy for repeated visitors improves, but system complexity increases
Solution Approach 1:
The system introduces an order history database as an intermediary component that stores and manages detailed user attribute information and order history. This database acts as a mediator between data collection and prediction processing, organizing complex data into structured formats that can be efficiently queried. The separation of data storage and processing functions reduces overall system complexity while maintaining high prediction accuracy.
3Loss of substance
If real-time data analysis is implemented, then waste from over-preparation is reduced, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary data processing and model training in advance, storing processed results in the order history database. When real-time prediction is needed, the system queries pre-processed data and applies straightforward calculation formulas rather than performing complex computations from scratch. This preliminary action significantly reduces real-time computational energy consumption while maintaining the ability to provide accurate real-time predictions for waste reduction.
Data Source
AI summary
To provide a system capable of predicting order quantities even under specific conditions where primary visitors are particular users who repeatedly visit a cafeteria and consume food and beverages.An order quantity predicting system for a cafeteria, the order quantity predicting system comprising a past order history database, an external factor database, an order pattern model generating unit, an order quantity prediction calculating unit, and an order quantity update calculating unit.