Warehouse Item Retrieval Guidance Using Availability Prediction
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Solution Overview
Problem
In current online concierge systems, shoppers face difficulties in locating items within warehouses due to fluctuating inventory and obstructed visibility, leading to increased time spent fulfilling orders and customer frustration when alternative items are selected instead of available items.
Innovation Solution
An online concierge system uses a machine-learned model to predict item availability and provides instructions to shoppers based on probability, and collects information from successful retrievals to assist others in finding difficult-to-locate items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a shopper manually searches for items in a warehouse, then the shopper can locate items, but the time spent searching increases and items difficult to locate are missed
Solution Approach 1:
The system performs preliminary actions by capturing images of items and their locations in advance, creating a visual database before shoppers arrive. This allows shoppers to quickly identify item locations without manual searching, resolving the contradiction between detection accuracy and time consumption.
Solution Approach 2:
The system creates visual copies (photographs) of physical items and their warehouse locations. These image copies serve as digital twins that shoppers can view to locate actual items, eliminating the need for physical searching while maintaining accurate location information.
2Reliability
If inventory information is not updated in real-time, then the system is simpler to operate, but item availability predictions become inaccurate
Solution Approach 1:
The system implements feedback mechanisms where item retrieval outcomes are fed back into the machine learning model. This continuous feedback loop improves prediction accuracy over time without requiring complex real-time inventory updates, as the model learns from historical patterns.
Solution Approach 2:
The system replaces complex mechanical inventory tracking systems with a machine learning-based prediction system. Instead of continuously monitoring and updating physical inventory data, the ML model predicts availability based on learned patterns, reducing system complexity while maintaining reliability.
3Productivity
If shoppers are not provided with location information, then the system requires less data collection, but shoppers spend more time searching and may request alternative items
Solution Approach 1:
The system performs preliminary actions by pre-capturing and storing images of items and their locations. This advance preparation provides shoppers with location information without requiring real-time data collection, thus improving fulfillment speed while minimizing information loss.
Data Source
AI summary
Based on orders fulfilled by shoppers of an online concierge system, the online concierge system identifies items in an order that are difficult to find in a warehouse in which the order is fulfilled. When a shopper obtains a difficult to find item from the warehouse, the online concierge system prompts the shopper to provide information for finding the difficult to find item in the warehouse. The online concierge system stores the information for finding the difficult to find item from the shopper in association with the difficult to find item and with the warehouse. Subsequently, when a different shopper is fulfilling an order from the warehouse including the difficult to find item, the online concierge system displays the information for finding the difficult to find item in the warehouse to the different shopper.