Modeling Monetary Risk Using Technology Maturity Distributions
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Solution Overview
Problem
Manufacturers face challenges in reliably quantifying technical risk associated with project proposals involving new technologies or uncertain markets, as existing methods rely on qualitative measures like Technology Readiness Levels (TRLs), making it difficult to accurately estimate cost and revenue uncertainties.
Innovation Solution
A system and method for modeling quantitative risk/return as a function of qualitative technology maturity levels, using lognormal and triangular distributions to assign risk values and maturity measures, enabling robust modeling of monetary measures across various contexts without modification, and facilitating rapid prototyping of business cases using Monte Carlo simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If qualitative measures like Technology Readiness Levels (TRLs) are used to assess technical risk, then the assessment process is simple and widely applicable, but the ability to accurately quantify cost and revenue uncertainties is insufficient
Solution Approach 1:
The patent transforms the qualitative TRL scale into a quantitative framework by assigning specific numerical values and probability distributions to each TRL level. This allows the model to convert qualitative maturity assessments into quantifiable risk and return parameters, enabling accurate cost and revenue uncertainty analysis while maintaining the simplicity of the original TRL classification system.
Solution Approach 2:
The patent introduces an intermediary modeling layer that bridges qualitative TRL assessments and quantitative financial outcomes. This intermediary model uses predefined relationships between TRL levels, technical risk, and monetary measures to translate qualitative inputs into quantitative outputs without requiring direct modification of the original TRL framework.
2Measurement precision
If extensive data collection and analysis are performed to accurately quantify technical risk, then measurement precision improves, but the time and complexity of the assessment process increases
Solution Approach 1:
The patent pre-establishes the relationships between TRL levels, technical risk parameters, and monetary outcomes before actual assessments are performed. By pre-defining probability distributions, risk factors, and modeling relationships, the system eliminates the need for extensive real-time data collection and analysis, enabling rapid quantification of technical risk while maintaining high measurement precision.
Solution Approach 2:
The patent uses a standardized model that requires only partial data input (basic TRL assessment and key project parameters) rather than comprehensive data collection. The predefined relationships and probability distributions fill in the gaps, allowing accurate risk quantification with minimal time investment while avoiding the need for excessive data gathering.
3Adaptability or versatility
If a robust and flexible modeling framework is created to handle various contexts, then adaptability improves, but the complexity of the model structure increases
Solution Approach 1:
The patent creates a universal modeling framework that can handle multiple contexts (different industries, project types, and monetary measures) through a single standardized structure. The model uses generic components such as TRL-based risk distributions and modular relationship definitions that can be applied across diverse scenarios without requiring context-specific modifications, thereby achieving high adaptability with manageable complexity.
Solution Approach 2:
The patent segments the modeling framework into independent, modular components: TRL assessment module, risk parameter assignment module, probability distribution selection module, and monetary outcome calculation module. Each segment can be independently configured and applied, allowing the overall system to maintain flexibility while keeping individual components simple and manageable.
Data Source
AI summary
A systems, methods and computer program products are provided for modeling a monetary measure of a good, such as a cost or revenue associated with the good. A method begins by selecting at least one qualitative measure of maturity for at least one technology associated with the good, where each qualitative measure of maturity is associated with a distribution such that each technology is correspondingly associated with a distribution. Next, a monetary point is associated with each technology, and thereafter a monetary distribution is determined for each technology based upon a respective monetary point and a respective distribution. A plurality of monetary values are selected by randomly selecting the plurality of monetary values for each technology based upon a respective monetary distribution. Finally, the monetary measure for the good are modeled based upon the selected monetary values for each technology.