IoT Tracking Devices For Programmatic Supply Chain Status Updates
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Solution Overview
Problem
Existing supply chain management systems rely heavily on manual intervention for asset tracking, leading to human error, unreliability, and inefficiency, which hinders the accurate allocation and utilization of resources.
Innovation Solution
Implementing IoT-based tracking devices that utilize environmental sensor data, such as temperature and RF signal strength, to programmatically determine the status of assets, reducing the need for manual intervention and enhancing accuracy through machine learning-based state transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for asset tracking, then ease of operation is maintained, but reliability and accuracy deteriorate due to human error
Solution Approach 1:
The tracking device automatically monitors its own status and environment without human intervention. The processor detects when the device is paired or unpaired from an asset based on environmental sensor data, and the system autonomously updates asset status records, eliminating the need for manual tracking while maintaining high reliability
Solution Approach 2:
The patent replaces manual mechanical tracking processes with an automated electronic system. Environmental sensors, processors, and wireless communicators substitute for human operators, using digital signal processing and machine learning algorithms to determine asset status, thereby improving reliability while reducing manual operation requirements
2Productivity
If automated tracking is implemented, then productivity and accuracy improve, but device complexity increases
Solution Approach 1:
The tracking device integrates multiple functions into a single unit: environmental sensing, signal processing, machine learning-based status determination, and wireless communication. This multi-functional design improves productivity by consolidating operations while managing complexity through integrated architecture rather than separate components
Solution Approach 2:
The system uses machine learning models that learn from historical data to predict asset status transitions. Instead of complex real-time analysis, the system uses trained models that replicate patterns from past data, simplifying the computational complexity while maintaining high productivity in status determination
3Measurement precision
If manual status updates are used, then system complexity is reduced, but information accuracy and timeliness deteriorate
Solution Approach 1:
The system continuously monitors environmental sensor data and uses machine learning models to determine asset status transitions. The feedback loop processes real-time data, compares it against learned patterns, and automatically updates status records, ensuring high measurement precision through continuous automated monitoring rather than periodic manual updates
Data Source
AI summary
This disclosure describes systems, methods, and devices related to programmatically updating the status of assets tracked in a supply chain environment. A system may be configured to obtain first sensor data collected from a first tracking device associated with a first asset, determine asset workflow information associated with the first asset, determine an intelligence engine configured to generate an inference of a status associated with the first asset, determine the status associated with the first asset, wherein the status is determined by the intelligence engine based on the first sensor data and the asset workflow information, and store the status of the first asset in an asset tracking database.