Shopper-Location Pairing Using Demand Forecast Feedback
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Solution Overview
Problem
Existing online shopping concierge platforms face inefficiencies in matching shoppers with warehouse locations, leading to suboptimal travel times and resource allocation.
Innovation Solution
A method and system that utilize machine learning models to optimize shopper-warehouse pairs based on travel times, incentivize or encourage relocation of shoppers to relocate from their respective current locations to the set of available warehouse locations, optimizing shopper-location pairs by minimizing travel time and maximizing productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If shoppers are matched with warehouse locations using traditional methods, then the matching process is simple, but travel time and resource allocation efficiency are suboptimal
Solution Approach 1:
The system pre-identifies available shoppers and warehouse locations before matching is needed, maintaining updated information about shopper availability and warehouse status in advance. This preliminary identification of resources enables faster optimization when matching requests occur, reducing the time penalty for using complex ML models.
Solution Approach 2:
The machine learning model automatically performs the optimization of shopper-location pairs without requiring manual intervention. The system self-adjusts and self-optimizes by continuously processing available data about shopper locations, warehouse locations, and travel times to generate optimized pairings autonomously.
2Productivity
If machine learning models are used to optimize shopper-location pairs, then travel time is reduced, but computational complexity increases
Solution Approach 1:
The system changes the parameters used for matching from simple distance or location-based criteria to multiple optimized parameters including predicted travel time, shopper availability status, warehouse location characteristics, and historical performance data. The ML model processes these multiple parameters simultaneously to generate optimized pairings that improve productivity.
Solution Approach 2:
The patent replaces traditional mechanical or manual matching methods with machine learning-based computational optimization. Instead of using rule-based systems or manual assignment, the system employs ML models to automatically analyze complex datasets and determine optimal shopper-location pairings, substituting computational intelligence for conventional matching approaches.
3Loss of time
If shoppers are incentivized to relocate to optimized locations, then overall system efficiency improves, but communication and coordination overhead increases
Solution Approach 1:
The system implements a feedback loop where it generates communications to incentivize shopper relocation, monitors whether shoppers actually relocate to the suggested locations, and uses this outcome data to refine future matching recommendations. This feedback mechanism ensures that the communication overhead translates into actual productivity improvements by continuously learning from shopper responses and relocation outcomes.
Data Source
AI summary
A device may obtain historical order data comprising orders submitted by users to an online system, each order indicating a retailer location and a timestamp. A device may generate a first set of training examples, each training example indicating order demand at a retailer location during one period of time from a first set of periods of time. A device may train the demand forecast prediction model with the first set of training examples. A device may apply the demand forecast prediction model to a second set of periods of time to predict order demand for each period of time in the second set of periods of time. A device may track order demand across each period of time in the second set of periods of time. A device may generate a second set of training examples, each training example indicating a difference between the predicted order demand and the tracked order demand at the retailer location during each period of time from the second set of periods of time. A device may retrain the demand forecast prediction model with the second set of training examples.