Online Shopper Allocation Using Capability Trees and Demand Forecasts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional online concierge systems inefficiently allocate shoppers due to hard-coded rules that fail to account for variable shopper availability and varying capabilities, leading to delayed order fulfillment and inefficiencies in handling order restrictions.
Innovation Solution
An online concierge system uses a machine-learned model to estimate shopper availability and maintains a tree structure for shopper capabilities, allowing dynamic adjustment of characteristics and efficient allocation through hierarchical scoring of shoppers based on their capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional online concierge systems allocate shoppers using hard-coded rules, then order fulfillment can be performed, but the system cannot efficiently account for variable shopper availability and varying capabilities, leading to delayed fulfillment and reduced productivity
Solution Approach 1:
The system transitions from static hard-coded rules to dynamic machine-learned models that continuously adapt to variable shopper availability and capabilities. The model learns from historical data and adjusts shopper allocations in real-time based on current conditions, enabling the system to respond flexibly to changing circumstances while maintaining high fulfillment efficiency.
Solution Approach 2:
The invention changes the parameters of shopper allocation by introducing a machine-learned model that considers multiple varying parameters simultaneously - shopper availability across different time windows, individual shopper capabilities, and order requirements. This allows the system to optimize allocations based on actual conditions rather than fixed rules, improving both productivity and adaptability.
2Speed
If the system allocates a specific amount of estimated shoppers for fulfilling orders within a time interval, then short-term orders can be fulfilled rapidly, but significant increases in orders occupy available shoppers, limiting time windows for scheduling
Solution Approach 1:
The system dynamically adjusts shopper allocation across multiple time windows based on order demand and shopper availability. Rather than fixing allocations to specific intervals, the machine-learned model flexibly redistributes shopper resources as orders come in, maintaining rapid fulfillment capability while preserving scheduling options across the entire planning horizon.
Solution Approach 2:
The machine-learned model serves multiple functions simultaneously - it optimizes for rapid short-term fulfillment while also managing overall shopper utilization across all time windows. This multi-functional approach allows the system to handle both urgent orders and scheduled orders efficiently, maximizing the utility of available shopper resources.
3Reliability
If shoppers with specific characteristics are allocated to fulfill restricted orders, then those restricted orders can be fulfilled, but the same shoppers cannot fulfill unrestricted orders, potentially delaying other fulfillments
Solution Approach 1:
The system applies local quality by matching specific shopper characteristics to specific order requirements. The machine-learned model identifies which shoppers have the necessary capabilities for restricted orders and allocates them accordingly, while simultaneously identifying other suitable shoppers for unrestricted orders. This localized matching ensures reliable fulfillment of restricted orders without compromising overall productivity.
Solution Approach 2:
The machine-learned model incorporates feedback loops that monitor order fulfillment outcomes and shopper performance. This feedback enables the system to learn from past allocations and improve future decisions, balancing the need to fulfill restricted orders reliably with the goal of maximizing overall throughput by making smarter allocation decisions.
4Reliability
If the system hard-codes restrictions for assigning shoppers to orders, then restrictions can be accounted for, but the system cannot efficiently adapt when order requirements or shopper characteristics change
Solution Approach 1:
The system replaces static hard-coded restrictions with dynamic machine-learned models that automatically adapt when order requirements or shopper characteristics change. The model learns the relationships between shopper capabilities and order restrictions from data, enabling it to handle new restriction types and characteristic variations without requiring manual rule updates.
Solution Approach 2:
The invention substitutes the mechanical system of hard-coded rules with an intelligent machine-learned model. This replacement allows the system to account for restrictions reliably while gaining the ability to adapt to changes automatically, as the model can process and learn from varied input data without requiring structural modifications to the underlying system.
Data Source
AI summary
An online concierge system maintains a data store with discrete time intervals for fulfilling orders and information on shoppers categorized in a tree structure based on their characteristics. When an order is received from a user's client device, the system employs a machine learning model trained on historical data to estimate the number of available shoppers and their corresponding levels in the tree structure. The order is tagged based on the items it contains, and these tags are matched to the levels of capable shoppers. Shoppers are grouped and scored based on their levels, and a group with the minimum score and capability to fulfill the order is selected. A shopper from this group is then dispatched for order fulfillment, leading to a decrement in the estimated number of shoppers in the selected group. This approach optimizes the allocation of shoppers to orders, ensuring efficient and timely fulfillment.