Warehouse Asset Tracking via Computer Vision Scheduling
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Solution Overview
Problem
Storage facilities face inefficiencies due to assets becoming lost or delayed during loading and unloading operations, as existing tracking methods are time-consuming and prone to errors, leading to increased downtime and operational delays.
Innovation Solution
A scheduling system that utilizes IoT devices and machine learning models to track asset locations, statuses, and optimize loading/unloading operations by estimating arrival times and scheduling events based on real-time data and historical information, improving the flow of assets in and out of the facility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection or asset tracking tag methods are used to locate assets, then asset location can be determined, but the process is time-consuming and reduces operational efficiency
Solution Approach 1:
The patent replaces manual inspection and mechanical asset tracking tag methods with an automated computer vision system using cameras and machine learning algorithms. The system automatically detects, identifies, and tracks assets (vehicles, containers, assets) in the yard through image processing, eliminating the need for manual searching and significantly reducing the time required to locate assets while maintaining accurate tracking.
2Reliability
If the facility operates without automated scheduling, then operational flexibility is maintained, but assets become lost or delayed during loading and unloading operations
Solution Approach 1:
The patent implements a feedback-based automated scheduling system that continuously monitors asset locations, vehicle arrivals, and loading/unloading operations through computer vision and IoT sensors. The system uses this real-time data to dynamically adjust schedules, notify relevant parties of asset statuses, and optimize facility operations. This feedback loop ensures reliable asset tracking and coordination while managing complexity through automated decision-making algorithms.
3Productivity
If loading and unloading operations are not optimized, then operational simplicity is maintained, but downtime increases and facility throughput decreases
Solution Approach 1:
The patent employs preliminary action by using machine learning models to predict vehicle arrival times, asset locations, and optimal loading/unloading schedules before operations begin. The system proactively prepares and communicates schedules to relevant parties in advance, coordinates asset movement to loading docks, and anticipates potential delays. This proactive approach optimizes facility throughput and minimizes downtime while managing operational complexity through automated planning and coordination.
Data Source
AI summary
Techniques are described for providing an inventory scheduling system that tracks inventory assets as the assets arrive, are stored, and depart a facility. The scheduling system may also schedule facility events in substantially real-time as the assets and/or vehicles are enroute to the facility.