Burn Planning System for Information Handling Systems
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Solution Overview
Problem
Existing methods for software installation and configuration in information handling systems are time-consuming and unpredictable, leading to increased dwell time in burn racks and reduced manufacturing throughput, with ad hoc planning relying on subject matter experts for manual calculations.
Innovation Solution
An information handling system that receives information on software to be burned and testing time for each target system, uses statistical analysis to determine predicted burn times and optimizes the order of software installation, automating the process to improve efficiency and reduce dwell time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual burn planning is performed by subject matter experts using ad hoc methods, then flexibility in handling individual system requirements is maintained, but burn time becomes unpredictable and dwell time in burn racks increases
Solution Approach 1:
The system performs self-service by automatically calculating predicted burn times using statistical analysis of historical data, eliminating the need for manual calculations by subject matter experts. The burn planning system autonomously optimizes the burn schedule based on learned patterns from previous burns, thereby reducing both burn time and manual complexity simultaneously.
Solution Approach 2:
The patent replaces the mechanical manual calculation process with an automated computational system that uses statistical analysis and machine learning algorithms. This substitution transforms the burn planning from a manual expert-driven process to an automated system-driven process, improving predictability while reducing the complexity of human intervention.
2Productivity
If systems are burned sequentially without optimization, then the burn process is simple to manage, but overall manufacturing throughput is reduced due to increased dwell time
Solution Approach 1:
The burn scheduling system dynamically adjusts the burn sequence based on predicted burn times for each system. Rather than using a static sequential approach, the system continuously optimizes the burn schedule by grouping systems with similar predicted burn times together, allowing for concurrent processing and improving manufacturing throughput while managing complexity through automation.
Solution Approach 2:
The system performs preliminary analysis by calculating predicted burn times for each system before the actual burn process begins. This advance planning allows the system to optimize the burn schedule in advance, grouping systems strategically to maximize throughput and minimize dwell time, rather than reacting to burn completion in real-time.
3Measurement precision
If burn planning is performed without statistical analysis of testing time, then the planning process is faster to execute, but burn time prediction accuracy decreases leading to suboptimal scheduling
Solution Approach 1:
The system performs preliminary statistical analysis of historical testing time data before the burn process to establish predictive models. By pre-processing and analyzing historical data to create statistical relationships between testing time and burn time, the system achieves high prediction accuracy while keeping the actual burn scheduling computation efficient and fast.
Solution Approach 2:
The system creates a statistical model that copies the relationship between testing time and burn time based on historical data. This model serves as a simplified representation that allows rapid prediction of burn times without requiring complex real-time analysis, thereby maintaining both accuracy and computational efficiency.
Data Source
AI summary
An information handling system may include a processor and a memory communicatively coupled to the processor. The information handling system may be configured to: receive, for each of a plurality of target information handling systems, information regarding software to be burned to the respective target information handling system; receive, for each of the target information handling systems, information regarding testing time; based on a statistical analysis of the information regarding the testing time, determine a predicted burn time for each target information handling system; and based on the respective predicted burn times, determine a desired order in which the target information handling systems are to be burned with the software.