Data Distribution Platform for Restaurant Crowdedness Matching
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Solution Overview
Problem
Existing systems do not effectively increase sales of restaurant tenants and satisfaction of corporate tenant employees.
Innovation Solution
A data distribution platform that acquires employee information and restaurant crowdedness levels, calculates predicted crowdedness, and matches employees with under-crowded restaurants, thereby incentivizing visits through targeted promotions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If employees are directed to restaurants during peak hours to increase sales, then restaurant sales increase, but employee satisfaction decreases due to longer waiting times
Solution Approach 1:
The system dynamically adjusts recommendations based on real-time crowdedness levels. When crowdedness is high, the system recommends alternative restaurants or time slots, transforming the static promotion approach into a dynamic one that adapts to changing conditions, thereby maintaining employee satisfaction while still promoting restaurant visits
Solution Approach 2:
The system incorporates feedback loops by monitoring crowdedness levels and adjusting recommendations accordingly. This feedback mechanism ensures that employees are not directed to overcrowded restaurants, preventing dissatisfaction while maintaining sales through iterative optimization of recommendations
2Ease of operation
If employees are directed to restaurants during off-peak hours to reduce crowdedness, then employee satisfaction increases, but restaurant sales decrease
Solution Approach 1:
The system changes the parameter of recommendation timing based on crowdedness thresholds. Instead of always recommending off-peak hours, it adjusts recommendations based on current crowdedness parameters, suggesting off-peak times when crowdedness is high and potentially peak times when crowdedness is already low, thereby optimizing both satisfaction and sales
Solution Approach 2:
The system performs preliminary actions by proactively recommending restaurants before employees arrive, allowing them to plan their visits during optimal times. This advance planning enables the system to guide traffic to underutilized restaurants or time slots, preventing overcrowding while still driving sales through strategic recommendations
3Ease of operation
If the system provides detailed real-time crowdedness information to all employees, then employee satisfaction increases, but system complexity increases
Solution Approach 1:
The system implements self-service by providing automated, real-time crowdedness information through a user-friendly interface. Employees can independently query restaurant status and receive recommendations without requiring complex manual processes, thereby enhancing satisfaction while keeping the system architecture relatively simple through automation
Data Source
AI summary
A data distribution platform includes: employee information storage means in which the employee information of each employee is stored; store information storage means in which the predicted crowdedness level and the target crowdedness level of each restaurant are stored; restaurant extraction means for extracting, from the store information storage means, a restaurant of which the predicted crowdedness level is lower than the target crowdedness level as a restaurant to which a customer should be sent; employee extraction means for extracting, from the employee information storage means, an employee who can use the restaurant extracted by the restaurant extraction means as an employee to be induced to go to the restaurant; and output means for outputting a combination of the restaurant extracted by the restaurant extraction means and the employee who can use the restaurant as a matching result.