Order Grouping via Shopper Selection Time Prediction
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Solution Overview
Problem
Conventional online concierge systems face inefficiencies in order fulfillment due to limitations in grouping orders based on estimated delivery times, which can lead to increased fulfillment times and reduced efficiency when shoppers are scarce.
Innovation Solution
The online concierge system employs a selection prediction model trained on historical data to estimate the time a shopper will take to select a group of orders for fulfillment, allowing for more accurate scheduling and grouping of orders, even if some have estimated delivery times later than specified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional online concierge systems prevent grouping of orders with estimated delivery times later than specified delivery times, then individual order fulfillment deadlines are met, but order fulfillment efficiency decreases and shopper time is wasted
Solution Approach 1:
The system performs preliminary estimation of shopper selection time using a trained machine learning model before finalizing order grouping decisions. This preliminary action allows the system to account for the time shoppers need to review and select orders, enabling more accurate timing of order assignments and groupings, thus improving both delivery compliance and fulfillment efficiency
Solution Approach 2:
The system uses historical order fulfillment data to train a machine learning model that provides feedback on estimated shopper selection times. This feedback loop continuously improves the accuracy of time estimates, allowing the system to optimize order grouping strategies that balance deadline compliance with maximum fulfillment efficiency
2Reliability
If limited number of shoppers are available, then individual order fulfillment is ensured, but overall fulfillment time increases and system capacity is reduced
Solution Approach 1:
The system merges multiple individual orders into groups that can be fulfilled by a single shopper when the estimated selection time and combined fulfillment time still meet delivery deadlines. This merging approach maximizes the utilization of limited shopper resources while maintaining reliable delivery guarantees and reducing total system fulfillment time
3Measurement precision
If shoppers are given more time to select individual orders, then selection accuracy improves, but fulfillment efficiency decreases and delivery times are delayed
Solution Approach 1:
The system performs preliminary estimation of the time required for accurate order selection using a trained machine learning model. This preliminary time estimation enables the system to optimize the balance between giving shoppers sufficient time for accurate selection and maintaining high fulfillment speed, by assigning orders or groups with timing that accounts for the estimated selection duration
Data Source
AI summary
An online concierge system receives orders from users identifying items and a warehouses from which the items are obtained. The online concierge system displays groups of one or more orders to shoppers, allowing a shopper to select a group of orders for fulfillment. When selecting groups of orders to display to shoppers, the online concierge system accounts for costs for fulfilling different groups and displays groups having costs satisfying one or more criteria, while maintaining one or more restrictions on times to fulfill orders. The online concierge system trains a selection prediction model to predict an amount of time for a shopper to select a group of orders and determines an estimated fulfillment time for the group from the predicted amount of time. Accounting for the predicted selection time allows the online concierge system to identify a larger number of groups for which costs of fulfillment are determined.