Sweet Spot Identification in Complex Systems
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Solution Overview
Problem
In complex systems, finding the ideal solution or 'sweet spot' for multiple variables is difficult due to indirect impacts and human bias, leading to suboptimal design solutions in fields like vehicle manufacturing.
Innovation Solution
A computer-implemented method that receives measurement values from a complex system, prioritizes criteria attributes, and uses a sequential computing algorithm to filter measurement value sets based on objective values, identifying optimal values or ranges for each criteria attribute to determine the sweet spot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually analyze complex systems to find optimal solutions, then expertise and experience can be applied, but human bias contaminates the solution and indirect impacts are missed
Solution Approach 1:
The patent replaces manual human analysis with an automated computer-based system that processes measurement data through sequential filtering algorithms. This substitution eliminates human bias while systematically evaluating multiple criteria attributes and their interrelationships, providing objective identification of sweet spots in complex systems.
Solution Approach 2:
The patent introduces a computer-based measurement and analysis system as an intermediary between the complex system under study and the decision-making process. This intermediary objectively collects, processes, and analyzes measurement data across multiple criteria attributes, preventing human bias from contaminating the analysis while revealing indirect impacts between variables.
2Adaptability or versatility
If multiple criteria attributes are evaluated in complex systems, then comprehensive optimization is achieved, but the number of variables increases making analysis difficult
Solution Approach 1:
The patent segments the analysis process into sequential filtering stages, where measurement value sets are progressively filtered based on each prioritized criteria attribute. This segmentation breaks down the complex multi-variable analysis into manageable steps, evaluating one criteria attribute at a time while considering its relationship with other attributes, thus making comprehensive optimization tractable.
Solution Approach 2:
The patent performs preliminary prioritization of criteria attributes before the main analysis. By determining the priority order of criteria attributes in advance, the system prepares a structured approach to handle multiple variables systematically, reducing the complexity of simultaneous multi-variable optimization by establishing a predetermined evaluation sequence.
3Reliability
If systematic automated analysis is implemented, then human bias is eliminated and indirect impacts are recognized, but the system complexity increases
Solution Approach 1:
The patent replaces subjective human judgment with automated computer-based analysis that systematically processes measurement data. This substitution ensures reliability and objectivity by eliminating human bias while using algorithmic approaches to handle the complexity of analyzing multiple interrelated criteria attributes and their indirect impacts.
Data Source
AI summary
To identify the sweet spot for criteria attributes (i.e., variables) within a complex system, a data source having objectively acceptable values or ranges of values for the criteria attributes for each variant of the system is generated. The identification system used to analyze the complex system to identify the sweet spot receives measurement values from a testing system that measures values of the criteria attributes from variants of the complex system and provides the measurement values to the identification system. The criteria attributes may be prioritized for determination of the sweet spot, including a selection of a value or range of values that are to be used for the most highly prioritized criteria attribute. The prioritized criteria attributes and objectively acceptable values can be used to filter the measurement values to identify the sweet spot for the variants of the complex system.