Predictive Storage Slot Allocation With AR Warehouse Guidance
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Solution Overview
Problem
Existing storage systems lack efficient methods for organizing items in storage infrastructures, leading to inefficiencies such as wasted space, increased labor costs, and difficulty in locating items, particularly in industrial warehouses where forklift operators spend time searching for empty slots and items are often misplaced due to improper allocation.
Innovation Solution
A predictive machine learning model that utilizes augmented reality and IoT sensors to optimize storage space allocation by analyzing item attributes, storage space configurations, and user behavior, generating optimal storage recommendations and providing real-time guidance through a gaze tracking device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If forklift operators manually search for empty slots and allocate inventory without systematic guidance, then operational flexibility is maintained, but time consumption and labor costs increase significantly
Solution Approach 1:
The system pre-calculates and identifies optimal storage slots for incoming inventory before the forklift operator arrives. The predictive model analyzes item attributes, storage space characteristics, and usage patterns in advance to determine the best placement locations, eliminating the need for operators to search manually
Solution Approach 2:
An augmented reality interface acts as an intermediary between the predictive model and the forklift operator. The system overlays directional arrows and highlights on the warehouse shelves through AR glasses, guiding operators to the pre-determined optimal storage locations without requiring them to interpret complex data or make allocation decisions
2Productivity
If storage spaces are allocated without considering item attributes and space characteristics, then allocation speed is maintained, but storage efficiency and space utilization deteriorate
Solution Approach 1:
The system applies different allocation strategies to different storage spaces based on their specific characteristics. Each storage slot is evaluated individually for attributes such as size, location, accessibility, and current usage patterns, allowing the predictive model to match items to the most suitable spaces rather than applying a uniform allocation rule
Solution Approach 2:
The predictive model dynamically adjusts allocation decisions by analyzing multiple parameters including item dimensions, weight, turnover rate, storage space capacity, location accessibility, and historical usage data. The model optimizes the combination of these parameters to determine the best storage assignment for each item
3Reliability
If storage infrastructure lacks real-time information and predictive capabilities, then system simplicity is maintained, but inventory management effectiveness and item accessibility worsen
Solution Approach 1:
The system continuously collects data on item storage locations, retrieval patterns, and space utilization through sensors and tracking systems. This feedback is fed back into the predictive model to refine future allocation decisions, improving inventory management effectiveness over time through learned patterns and optimized strategies
Solution Approach 2:
The predictive model autonomously generates storage allocation recommendations without requiring human intervention or complex manual processes. The system self-optimizes by automatically analyzing data patterns and making intelligent decisions about item placement, reducing the need for human expertise in storage management
Data Source
AI summary
Provided are a computer program product, system, and method for using a predictive machine learning to determine storage spaces to store items in a storage infrastructure. Information on the storage infrastructure indicates storage spaces in the storage infrastructure, including physical configurations of the storage spaces, usages of the storage spaces, items placed in the storage spaces, and a predictive model. The predictive model receives as inputs, attributes of a target item to add to the storage spaces, the physical configuration of the storage spaces, attributes of the storage spaces, and the usages of the storage spaces. The predictive model processes the inputs to output an available storage space to optimize storage of the target item. An augmented reality representation of information on storage of the target item with respect to the available storage space is generated on a computer display.