Predictive Device Disposition for Refurbishment and E-Waste Reduction
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Solution Overview
Problem
Current device management approaches lead to decreased device sustainability, excessive power consumption, and increased carbon footprint due to devices being used until they deteriorate beyond refurbishment, with no proactive failure prediction capabilities.
Innovation Solution
A device disposition management platform using machine learning algorithms to predict optimal times for reselling or recycling devices based on operational data, generating alerts for proactive device management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If devices are used until they stop working or deteriorate beyond repair, then device utilization is maximized, but device sustainability decreases and electronic waste increases
Solution Approach 1:
The system performs preliminary actions by predicting device failures before they occur using machine learning algorithms that analyze operational data. This allows proactive intervention to refurbish or replace devices before they deteriorate beyond repair, maximizing their useful life while preventing electronic waste from premature disposal
Solution Approach 2:
The system implements continuous feedback loops by monitoring operational data from devices, analyzing it through machine learning models, and using the results to predict future failures. This feedback mechanism enables dynamic adjustment of device management strategies to optimize both utilization and sustainability
2Loss of energy
If devices are used until they stop working, then operational costs are minimized, but power consumption increases due to devices in disrepair
Solution Approach 1:
The system takes preliminary action by predicting device failures before they occur, allowing proactive maintenance or replacement. This prevents devices in disrepair from consuming excessive power while minimizing the time devices are non-operational, as interventions are scheduled before complete failure
3Reliability
If conventional reactive approaches are used for device management, then system complexity is minimized, but ability to prevent and minimize adverse effects of failures is limited
Solution Approach 1:
The system enables self-service by allowing devices to automatically report their operational status and health metrics. The machine learning models then autonomously analyze this data and generate predictions, reducing the need for complex manual monitoring systems while improving reliability through continuous automated assessment
Data Source
AI summary
A method comprises collecting operational data from a plurality of devices, predicting one or more details corresponding to disposition of respective ones of the plurality of devices based at least in part on the operational data, and generating and causing transmission of one or more alerts to at least one user device based at least in part on the one or more details corresponding to the disposition of the respective ones of the plurality of devices.