ETA-Based Order Fulfillment Queue for Retail Drive-Through
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Solution Overview
Problem
Existing drive-through systems at retail facilities face inefficiencies due to mismatched customer arrival times and item preparation times, leading to increased queueing and service times.
Innovation Solution
A computer-implemented system that predicts customer arrival times using GPS-based location information, allowing for synchronized item preparation and retrieval, thereby eliminating choke points in the fulfillment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If customers arrive at the drive-through window without remote ordering or before item preparation is complete, then the system must handle unsynchronized arrivals, but this increases queueing and service times
Solution Approach 1:
The system performs preliminary actions by sending automated notifications to customers before their orders are ready, allowing them to prepare for arrival. The system also pre-prepares items based on predicted arrival times, ensuring synchronization between item readiness and customer arrival without requiring customers to wait in queue.
Solution Approach 2:
The system implements feedback mechanisms by tracking customer location data via GPS, monitoring order preparation status, and using this information to dynamically adjust notifications and preparation timing. This closed-loop feedback enables the system to synchronize item readiness with actual customer arrival times, reducing queueing delays.
2Productivity
If items are prepared in advance without knowing customer arrival times, then preparation can begin earlier, but items may cool down or be misplaced before customer arrival
Solution Approach 1:
The system dynamically adjusts item preparation timing based on real-time customer location data and predicted arrival times. Instead of static pre-preparation, the system continuously updates preparation schedules to match actual customer arrival patterns, ensuring items are ready at the optimal moment while maintaining quality standards for temperature-sensitive products.
Solution Approach 2:
The system performs preliminary actions by initiating item preparation processes based on predicted arrival times from customer location tracking. This allows preparation to begin in advance when appropriate, while the system maintains the capability to adjust or hold preparation based on actual arrival data, preventing quality degradation.
3Loss of time
If the system tracks customer location continuously to predict arrival times, then fulfillment can be synchronized, but this increases system complexity and data processing requirements
Solution Approach 1:
The system applies partial tracking by monitoring customer location at strategic intervals rather than continuous monitoring. It activates full tracking capabilities only when customers are in the vicinity of the facility or when order readiness approaches, reducing unnecessary data processing while maintaining effective fulfillment synchronization.
Data Source
AI summary
A computer program product for prioritizing order fulfillment at a retail sales facility receives purchase request information at a retail sales computing device that includes identifying information and order information for the first customer, receives location information for the first customer, processes the received location information for the first customer together with location information of the retail sales facility to predict an estimated time of arrival (ETA) for the first customer at the retail sales facility, schedules a time interval for acquiring updated location information for the first customer, and places the identifying information, order information and ETA for the first customer in an order fulfillment queue for all current customers. Entries in the order fulfillment queue are ordered in increasing ETA order. ETA is continuously updated for each customer, and thereby the order for fulfilling customer orders in the order fulfillment queue. For example, if a customer's distance from the retail sales facility increases (for example, due to a mistaken turn), that customer's order will drop to a later-served position in the queue. The location information for the first customer includes GPS-based location information of the first customer that is captured by a GPS device associated with a mobile computing device of the first customer. The location information for the retail sales facility includes GPS-based location information of the retail sales facility. The ETA prediction is performed using the GPS-based location information of the first customer and the GPS-based location information of the retail sales facility.