Contingency Architecture Planning for Resilient System Development
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Solution Overview
Problem
Existing design and planning techniques struggle to identify the most resilient system architectures due to uncertainties in component availability and supply chain disruptions, leading to high risk and potential significant losses.
Innovation Solution
A method involving identifying uncertain elements, calculating their probabilities of unavailability, and selecting contingency architectures based on expected values to develop more resilient systems, using a database of computed information and algorithms to optimize system designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modern design and planning techniques are used to identify system architectures, then the optimum value (performance) is improved, but the reliability (resilience to uncertainties) deteriorates
Solution Approach 1:
The patent applies preliminary action by identifying and evaluating contingency architectures before uncertainties actually occur. The system pre-calculates expected values for multiple potential architectures considering various uncertainty scenarios (component failures, supply chain disruptions, technology under-performance), allowing selection of the most resilient architecture before implementation. This proactive approach enables the system to prepare fallback plans in advance rather than reacting to failures when they happen.
Solution Approach 2:
The patent employs parameter changes by transforming the architecture selection criterion from purely optimizing for maximum performance (optimum value) to optimizing for expected value that incorporates probability-weighted outcomes. This parameter transformation allows comparison of architectures based on their expected performance under uncertainty, enabling selection of architectures with higher resilience even if their peak performance is lower. The expected value calculation changes the decision parameter from deterministic optimum to probabilistic expectation.
2Reliability
If contingency architectures are identified and evaluated, then the reliability (resilience) is improved, but the loss of time (computational and analysis time) increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on evaluating only the most critical uncertainties and their corresponding contingency architectures. Rather than exhaustively analyzing every possible failure mode and contingency, the system identifies key uncertain elements (components, technologies, suppliers, manufacturing methods) and evaluates contingencies for those specific elements. This selective approach reduces analysis time while still capturing the most significant resilience risks.
3Reliability
If architectures with higher expected values are selected, then the reliability (resilience) is improved, but the productivity (optimum performance) decreases
Solution Approach 1:
The patent employs parameter changes by transforming the architecture selection criterion from purely optimizing for maximum performance (optimum value) to optimizing for expected value that incorporates probability-weighted outcomes. This parameter transformation allows comparison of architectures based on their expected performance under uncertainty, enabling selection of architectures with higher resilience even if their peak performance is lower. The expected value calculation changes the decision parameter from deterministic optimum to probabilistic expectation.
Data Source
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AI summary
A method of developing an architecture including the steps of identifying (200) a plurality of final architectures, identifying (202) a plurality of uncertain elements that will go into each said final architecture, identifying (206), for each of the uncertain elements, a probability that the uncertain element will not be available and identifying a plurality of candidate contingency architectures replacement for each combination of the uncertain elements for each of the final architectures, and identifying (208) a contingency architecture for that architecture and each combination of the uncertain elements not being available, identify (212) an expected value of each said final architecture, wherein the expected value of the final architecture is less than its optimum value and is a probability weighted value.