Interactive DOE Plan Exploration and Model Management
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Solution Overview
Problem
Conventional tools for exploring statistical configurations lack flexibility for interactive exploration and adjustment, making it difficult to refine, compare, and explore different options efficiently.
Innovation Solution
A computer-program product that includes a graphical user interface for generating and managing design of experiment plans, allowing users to create and compare different plans and test plans interactively, with features like plan settings containers, test plan containers, and dynamic graphical representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional tools use a structured step-by-step approach for exploring statistical configurations, then the process is systematic and organized, but the flexibility for interactive exploration and adjustment is limited
Solution Approach 1:
The system enables dynamic adjustment of statistical configurations through interactive graphical user interfaces. Users can modify plan settings, factors, and models in real-time without following a fixed sequential process. The system responds to user inputs dynamically, allowing flexible exploration of different statistical designs interactively.
Solution Approach 2:
The system divides the statistical configuration into separate manageable components including plan settings, factors, models, and test plans. Each component can be independently adjusted and explored, allowing users to interact with specific elements without affecting the entire configuration, thereby enhancing flexibility while maintaining organization.
2Productivity
If users follow a rigid step-by-step approach in conventional tools, then the workflow is structured, but the time required to explore alternative configurations and redo prior work increases
Solution Approach 1:
The system pre-establishes a framework of plan settings, factors, and models that can be instantly accessed and modified. Users can build upon pre-configured elements rather than starting from scratch, enabling rapid exploration of alternative statistical designs without redundant setup work.
Solution Approach 2:
The system allows users to create multiple versions or copies of statistical configurations that can be compared side-by-side. This enables efficient exploration of alternative designs by copying and modifying existing configurations rather than creating entirely new ones, reducing the time required to evaluate different options.
3Adaptability or versatility
If conventional tools lack flexible adjustment capabilities, then the system structure remains simple, but the ability to refine and compare statistical configurations is compromised
Solution Approach 1:
The system employs a universal framework where plan settings, factors, models, and test plans can serve multiple purposes across different statistical configurations. This multi-functional design enables the same structural elements to support diverse analytical needs, enhancing adaptability while managing complexity through standardized components.
Solution Approach 2:
The system organizes statistical configurations in a nested structure where factors are contained within plans, models are associated with plans, and test plans are linked to specific configurations. This nesting allows users to explore and refine individual elements without disrupting the overall structure, providing versatility while maintaining organizational clarity.
Data Source
AI summary
A computer-implemented system, computer-implemented method, and computer-program product includes receiving, via a graphical user interface, a request to generate an initial design of experiment plan based on one or more experiment factors; generating a first plan settings container object that represents the initial design of experiment plan in computer memory; and instantiating, within the graphical user interface, a plan explorer section that includes a plan settings graphical entry that displays a graphical representation of a plan factors data object and a plan model data object associated with the first plan settings container object.