Labor Allocation System Using Foot Traffic Forecasting
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Solution Overview
Problem
Existing labor scheduling methods fail to accurately predict staffing needs based on foot traffic, leading to inefficient labor management and potential sales losses, as they rely heavily on historical sales data rather than traffic information.
Innovation Solution
A computerized method that uses historical traffic distribution data to forecast foot traffic at hourly intervals and provides staffing recommendations by distributing labor as a linear function of foot traffic, incorporating user-defined guidelines and allowing for customization up to 16 weeks in advance, with options for labor distribution using fixed hours or shopper-to-associate ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If historical sales data is used to predict future sales and labor demands, then the prediction process is simple, but the accuracy of predicting staffing needs deteriorates because sales data does not reflect real-time traffic patterns
Solution Approach 1:
The patent changes the input parameter from historical sales data to real-time foot traffic data. The system collects foot traffic counts at various intervals and uses these traffic patterns as the primary input for predicting staffing needs, thereby improving prediction accuracy while maintaining computational simplicity through straightforward traffic-to-staffing ratio calculations.
2Adaptability or versatility
If labor scheduling is performed manually by management, then flexibility in adjusting schedules is maintained, but the efficiency and accuracy of labor allocation deteriorates due to human error and inability to process large amounts of traffic data
Solution Approach 1:
The system enables self-service labor scheduling by automatically collecting foot traffic data, analyzing patterns, and generating staffing recommendations without requiring manual intervention. The computer system processes traffic data and produces optimized schedules independently, significantly improving efficiency while allowing managers to review and adjust recommendations if needed.
3Measurement precision
If foot traffic data is collected and analyzed at detailed time intervals, then the accuracy of staffing recommendations improves, but the complexity of data processing and system requirements increases
Solution Approach 1:
The patent segments the day into specific time intervals (e.g., 15-minute or 30-minute slots) and collects foot traffic data for each segment separately. This segmentation allows the system to analyze traffic patterns at a detailed level for accurate staffing recommendations while keeping the processing manageable by handling one time segment at a time rather than attempting to process continuous data streams.
Data Source
AI summary
A method and system for distributing labor based upon determinations of traffic in a facility, such as a store. Daily traffic forecast information is obtained for each day within a given time period from a source of such information. Calendar and event information for the facility for each day within the given time period is determined. Baseline days are selected from historical traffic data, and baseline averages and percentages for a predetermined time interval are also selected. The distribution of traffic for each day within the given time period at each time interval is determined using the baseline percentages for each time interval. Labor data is distributed. Labor recommendations are provided at each time interval for the given time period based on the distribution of traffic for each day, and at least one of other user-defined workforce requirements. The results of any or all the these steps are displayed to a user.