Decision Logic Wizard for Business Rule Elicitation
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Solution Overview
Problem
Capturing decision logic in business operations is complex and time-consuming for Subject Matter Experts, often resulting in incomplete logic that does not align with business objectives, requiring significant investment and expertise.
Innovation Solution
A method that allows business analysts to create and edit decision logic through a form-based interface and decision analysis engine, using a wizard tool for guidance, without requiring extensive technical knowledge or setup, focusing on Key Performance Indicators and previous logic to enhance quality and reduce cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Subject Matter Experts are forced to think exhaustively about all rules, then decision logic completeness is improved, but time consumption and complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating initial decision logic rules based on business objectives and data patterns before requiring expert input. The wizard tool pre-fills rule templates and suggests logic structures, reducing the exhaustive thinking burden on SMEs while maintaining logic completeness.
Solution Approach 2:
The wizard tool acts as an intermediary between business objectives and final decision logic. It translates high-level business goals into structured rule proposals and guides SMEs through iterative refinements, reducing the time and complexity of direct expert engagement while ensuring logic completeness.
2Manufacturing precision
If heavy up-front investment and long preparation phases are used, then decision logic quality is improved, but project cost and duration increase
Solution Approach 1:
The system enables self-service decision logic creation where business users can independently define objectives, select data, and refine rules through the wizard tool without requiring extensive external expertise. The automated rule generation and validation reduce the need for expensive vendor specialists and system integrators.
Solution Approach 2:
The system changes the parameters of decision logic creation by using automated algorithms to generate initial rules and by providing interactive refinement capabilities. This reduces the quantity of expert hours needed while maintaining high logic quality through iterative improvement rather than lengthy preparation phases.
3Stability of the object's composition
If traditional elicitation methodologies are used, then decision logic structure is improved, but alignment with actual business operations decreases
Solution Approach 1:
The system introduces dynamics to the elicitation process through iterative cycles where business objectives are refined, data is reselected, and rules are regenerated. This dynamic approach ensures the final decision logic continuously aligns with actual business operations rather than relying on static, pre-defined structures.
Solution Approach 2:
The wizard tool provides feedback by automatically validating generated rules against selected data and business objectives, then presenting refined versions for approval. This feedback loop ensures the decision logic structure remains aligned with actual business needs while maintaining proper structure through automated validation.
4Manufacturing precision
If multiple iterations are required to complete elicitation, then decision logic accuracy is improved, but productivity and cycle time worsen
Solution Approach 1:
The system performs preliminary rule generation automatically based on business objectives and data patterns, providing an accurate initial draft that reduces or eliminates the need for multiple manual iterations. The wizard tool pre-configures rule structures and data selections, enabling accuracy to be achieved in fewer cycles.
Solution Approach 2:
The system replaces the mechanical process of manual, iterative rule creation with automated algorithmic generation. The wizard tool uses computational methods to produce accurate decision logic directly from business objectives and data, substituting repeated manual refinement cycles with automated processing that achieves similar or better accuracy faster.
Data Source
AI summary
The present invention is a method of creating decision logic. A first set of rules for the decision logic is received. The user is queried for data and this data is stored in a memory. A first decision is generated for a user based at least in part on the data. Input is received from the user for editing the first set of rules to create a new set of rules for the decision logic in the context of data. A final decision is generated derived at least in part from the data using the new set of rules.