Architecture Resilience Planning Under Component Uncertainty
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Solution Overview
Problem
Modern 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, high-reward architectures that often result in significant losses when contingencies arise.
Innovation Solution
A method that identifies a plurality of final architectures, assesses the probability of uncertain element unavailability, and selects contingency architectures to maximize the expected value, using a database of computed information to optimize resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If modern design and planning techniques are used to identify system architectures, then the architecture development process is streamlined, but the ability to identify resilient architectures facing uncertainty deteriorates
Solution Approach 1:
The method performs preliminary identification of multiple alternative architectures and their contingency plans before uncertainties materialize. By pre-calculating probability-weighted expected values for different architectural paths and preparing contingency architectures in advance, the system enables informed decision-making that balances development efficiency with resilience to future uncertainties.
2Reliability
If high-risk, high-reward architectures are pursued to maximize optimum value, then the potential reward is increased, but the risk of significant losses when contingencies arise increases
Solution Approach 1:
The method calculates probability-weighted expected values that incorporate potential losses from contingencies, effectively cushioning against high-risk architectures. By quantifying the expected value as a probability-weighted average including contingency scenarios, the system prevents over-commitment to high-risk paths while maintaining the opportunity for high rewards, thus reducing the impact of potential losses.
Solution Approach 2:
The method establishes a feedback mechanism where the calculated expected values and contingency probabilities inform subsequent architecture selection decisions. This feedback loop allows continuous adjustment of architectural choices based on quantified risk assessments, ensuring that high-risk architectures are only pursued when their expected value justifies the potential loss risk.
3Reliability
If contingency architectures are identified for each combination of uncertain elements, then the resilience to supply chain disruptions is improved, but the complexity of the architecture increases
Solution Approach 1:
The method changes the parameter of analysis from binary availability (available/not available) to probability-weighted expected values. By transforming the complexity of managing multiple contingency scenarios into a probabilistic framework, the system reduces architectural complexity while maintaining resilience. The probability weights allow systematic evaluation of multiple contingencies without requiring separate detailed plans for each scenario.
Data Source
AI summary
A method of developing an architecture including the steps of identifying a plurality of final architectures, identifying a plurality of uncertain elements that will go into each said final architecture, identifying, 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 a contingency architecture for that architecture and each combination of the uncertain elements not being available, identify 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.