Micro Manufacturing Centers for Low-Carbon IT Remanufacturing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in efficiently tracking the remaining life of information handling system components, predicting future failures, and optimizing the reuse process while minimizing environmental impact, particularly the carbon footprint, without compromising security or incurring excessive costs.
Innovation Solution
A system and method that securely tracks component lifecycle information using geographically distributed micro manufacturing centers, utilizing robotic tools and manual labor to optimize component reuse, remanufacture, and recycling, considering cost, carbon footprint, and delivery time constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If components are harvested, tested, and reused from failed information handling systems, then environmental impact and landfill commitment are reduced, but the expense of breakdown, testing, and rebuilding becomes prohibitive
Solution Approach 1:
The system segments the reuse process into modular components including distributed micro manufacturing centers, automated robotic disassembly, and component testing facilities. This segmentation enables parallel processing of multiple systems simultaneously, reducing per-unit costs while maintaining environmental benefits.
Solution Approach 2:
The system implements self-service through automated robotic tools that perform disassembly, component testing, and reassembly operations. This automation eliminates manual labor costs and increases processing efficiency, making the reuse economically viable while maintaining reduced environmental impact.
2Productivity
If information handling systems are transported to centralized facilities for repair and remanufacture, then component reuse efficiency improves, but transportation carbon footprint increases
Solution Approach 1:
The centralized facility is segmented into multiple distributed micro manufacturing centers located geographically close to customer sites. This segmentation enables local processing of failed systems, maintaining high component reuse efficiency through specialized equipment while minimizing transportation distances and associated carbon footprints.
Solution Approach 2:
The system introduces mobile robotic tools as intermediaries that can be deployed to customer sites to perform initial disassembly and component harvesting. This intermediary approach enables on-site processing that reduces the need for transporting entire systems to centralized facilities, thereby reducing transportation carbon footprint while maintaining reuse efficiency.
3Adaptability or versatility
If manual labor is used for disassembly and reassembly of information handling systems, then flexibility and adaptability improve, but labor costs and processing time increase
Solution Approach 1:
The system implements dynamic hybrid automation where robotic tools handle standardized, repetitive disassembly and reassembly operations to reduce processing time. Manual labor is dynamically deployed for complex, non-standardized tasks requiring adaptability. This dynamic allocation optimizes both processing speed and flexibility based on task requirements.
Solution Approach 2:
The robotic tools are designed with universal capabilities to perform multiple functions including disassembly, component testing, cleaning, and reassembly across different information handling system types. This universality maintains flexibility and adaptability while eliminating the need for specialized manual labor for each operation, thereby reducing overall processing time.
Data Source
AI summary
Information handling systems are transported to a selected of plural micro manufacturing centers for harvesting of component modules to reuse in remanufactured information handling systems through an automated process that uses a robotic arm to harvest the component modules and rebuild the information handling systems. Component module health lifecycle information is applied to select components for harvesting. Carbon footprint, energy consumption, costs, any manual labor involved and time criticality are factored to select a geographical location to harvest the component modules and to send the system to subsequent end user locations.


