Retail Pickup Wait Time Prediction Using Gradient Boosting
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Solution Overview
Problem
Wait times for picking up orders at retail locations are unpredictable due to variables such as the number of users, available workers, and unexpected arrivals, making conventional subjective estimates unreliable.
Innovation Solution
A machine learning model, specifically a gradient boosting model, is trained using historical data to predict wait times by analyzing factors like order queue size, user arrival times, and store conditions, providing accurate estimates even in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional subjective estimates are used to predict wait times, then the system is simple to operate, but the prediction accuracy is poor due to unpredictability of factors like number of users, available workers, and unexpected arrivals
Solution Approach 1:
The patent replaces manual subjective estimation with an automated machine learning model that processes historical data and real-time variables to predict wait times objectively, improving accuracy while maintaining operational simplicity through automation
Solution Approach 2:
The system uses historical data and real-time variables to automatically generate predictions without requiring manual intervention or complex user input, allowing the system to serve itself in generating accurate wait time estimates
2Measurement precision
If more factors are considered in wait time prediction (such as number of users, workers, arrival times), then the prediction accuracy improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent segments the prediction problem into distinct input categories (historical data, real-time variables, user-specific factors) and processes them through separate computational steps in the machine learning model, making the complex data processing manageable and systematic
Solution Approach 2:
The system pre-processes and stores historical data in advance, creating a foundation that can be quickly queried and combined with real-time variables during prediction, reducing the computational burden during actual wait time estimation
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain operations. The operations can include determining an estimated arrival time of a user at a physical store. The operations also can include generating an estimated wait time using a machine learning model and based on input data comprising the estimated arrival time and dynamic wait time data for the physical store. The operations additionally can include sending the estimated wait time to at least one of the physical store or a mobile device of the user. Other embodiments are disclosed.