Shopper Location Guidance for Hard-to-Find Warehouse Items
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In current online concierge systems, shoppers face difficulties in locating items within warehouses due to fluctuating inventory and items being obscured or located in hard-to-reach areas, leading to increased time spent fulfilling orders and customer frustration.
Innovation Solution
A machine-learned model predicts item availability and provides instructions to shoppers based on probability, and stores location information for difficult-to-find items for future reference, allowing efficient retrieval by other shoppers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If shoppers manually search for items in the warehouse, then they can locate items that are available, but they spend excessive time searching especially for items that are obscured or in hard-to-reach areas
Solution Approach 1:
The system performs preliminary actions by predicting item availability before the shopper arrives at the warehouse. The machine-learned model analyzes historical data, current inventory status, and item characteristics to determine which items are likely to be available and easy to locate, allowing shoppers to be prepared in advance and avoid unnecessary searching
Solution Approach 2:
The system introduces an intermediary component - the machine-learned prediction model - that acts as a mediator between the warehouse inventory system and the shopper. This intermediary processes complex information about item locations, availability, and accessibility to provide simplified guidance to shoppers, reducing their search time and effort
2Loss of information
If the system provides real-time inventory information to shoppers, then item availability can be confirmed before arrival, but the system complexity increases due to the need for machine-learned models and data processing
Solution Approach 1:
The system implements self-service by automatically gathering, processing, and analyzing inventory data without requiring manual intervention. The machine-learned model autonomously predicts item availability by processing historical order data, current inventory status, and item characteristics, then provides this information to shoppers automatically, reducing the need for complex manual system management
Solution Approach 2:
The system changes parameters by transforming raw inventory data into predicted availability probabilities. The machine-learned model processes multiple input parameters (historical availability, current inventory levels, item characteristics, warehouse conditions) and outputs a simplified prediction that indicates whether an item is likely to be available and easy to locate, making complex information more manageable
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.