Picker Recommendation Matching for Forecasted Order Positioning
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Solution Overview
Problem
Existing online concierge systems inefficiencies arise when multiple pickers wait at retailer locations for orders, leading to late deliveries and picker dissatisfaction due to mismatched order distribution based on current location, resulting in suboptimal resource utilization.
Innovation Solution
An online concierge system uses a machine learning model to predict order availability and location-based matching, generating recommendations for pickers to accept actual orders, move to forecasted order locations, or check back later, optimizing order distribution through a matching and positioning function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple pickers wait at the same retailer location for orders, then the risk of late delivery for orders at that location is reduced, but the efficiency of servicing orders at other locations deteriorates due to mismatched order distribution
Solution Approach 1:
The system performs preliminary actions by forecasting future orders before they are placed. It predicts the location and timing of upcoming orders and proactively directs pickers to those locations in advance, rather than waiting for orders to be placed and then reacting. This allows pickers to be positioned optimally before the actual order arrives, preventing both late deliveries and inefficient waiting at wrong locations.
Solution Approach 2:
The system dynamically adjusts picker assignments based on real-time order placement patterns and forecasted demand. Instead of static waiting locations, the system continuously updates recommendations to move pickers between retailer locations based on where orders are actually being placed and where they are predicted to be placed next. This dynamic reallocation optimizes both delivery reliability and overall productivity.
2Reliability
If pickers wait at retailer locations in anticipation of orders, then delivery reliability improves, but picker satisfaction deteriorates due to idle waiting time and potential discontinuation of servicing
Solution Approach 1:
The system implements feedback by continuously monitoring actual order placement patterns and comparing them against forecasted predictions. It uses this feedback to refine its forecasting model and adjust picker recommendations in real-time. The system provides feedback to pickers about expected order volumes and timing at specific locations, allowing them to make informed decisions about where to position themselves, thereby reducing idle waiting time while maintaining delivery reliability.
3Loss of energy
If notifications are sent to pickers based on current geographical locations, then the cost for pickers is reduced, but the efficiency of order distribution deteriorates when forecasted orders differ from current locations
Solution Approach 1:
The system performs preliminary actions by forecasting future orders before they are placed and proactively directing pickers to those locations in advance. This allows pickers to move to forecasted order locations before actual orders are placed, ensuring they are positioned optimally when orders arrive. This preliminary positioning reduces both picker costs (by minimizing unnecessary travel) and improves distribution efficiency (by ensuring pickers are at the right locations at the right time).
Data Source
AI summary
An online concierge system receives location information associated with pickers and actual orders associated with a geographical zone. A model trained to predict a likelihood an actual order associated with the zone will be available for servicing within a timeframe is accessed and applied to forecasted orders. Each picker is matched to an order for servicing by minimizing a value of a function that is based on a difference between a location associated with each picker matched to an actual order and an associated retailer location, a difference between the location associated with each picker matched to a forecasted order and an associated retailer location, and the predicted likelihood. Recommendations for accepting an actual order, moving to a retailer location associated with a forecasted order, or checking back later with the system are generated based on the matches and sent for display to a client device associated with each picker.