Causal Relationship Graph for Business Process Improvement
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Solution Overview
Problem
Existing causal relationship graph systems in software development projects do not allow for the reflection of editing changes in order information, limiting the ability to efficiently plan improvement measures.
Innovation Solution
An improvement system that includes a first correlation calculation unit to read parameter information and calculate correlations between evaluation indexes, a graph generation unit to create a causal relationship graph based on these correlations and order information, an interface unit to present and receive changes from users, and an order calculation unit to update the order information and regenerate the graph accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a causal relationship graph is displayed based on initial order information, then the graph can be generated and presented to users, but editing changes to the graph cannot be reflected in the order information
Solution Approach 1:
The system implements a feedback mechanism where user edits to the causal relationship graph are detected by the detection unit, which then triggers the order calculation unit to recalculate order information based on the edited graph structure. This closed-loop feedback ensures that any modification to the graph is automatically reflected in the underlying order information, resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The system pre-establishes the causal relationship graph structure with defined nodes and edges before user editing occurs. By having the graph generation unit create the initial graph based on order information, and then using the detection unit to monitor changes, the system prepares the framework in advance to handle edits efficiently without requiring complex real-time restructuring logic.
2Productivity
If the system maintains fixed order information, then the initial graph can be generated quickly, but the system cannot adapt to user edits or changes
Solution Approach 1:
The system transitions from static order information to dynamic order information that can adapt to user edits. The order calculation unit continuously updates order information based on detected changes in the causal relationship graph, making the system dynamic rather than fixed. This allows the graph generation unit to efficiently regenerate graphs with updated order information, maintaining productivity while gaining adaptability.
Solution Approach 2:
The system implements self-service through automated detection and calculation. When users edit the causal relationship graph, the detection unit automatically detects these changes and triggers the order calculation unit to recalculate order information without requiring manual intervention. This self-service mechanism maintains high productivity by eliminating manual updates while enabling continuous adaptation to user edits.
3Adaptability or versatility
If the system allows real-time updates of order information based on graph edits, then adaptability improves, but the complexity of maintaining consistency increases
Solution Approach 1:
The feedback mechanism created by the detection unit and order calculation unit automatically maintains consistency between graph edits and order information. When the graph structure changes, the detection unit feeds this information back to the order calculation unit, which recalculates and updates order information accordingly. This automated feedback loop maintains consistency without requiring complex manual verification processes.
Solution Approach 2:
The order calculation unit acts as an intermediary between the graph structure (causal relationship graph) and the order information. It receives input from the detection unit about graph changes, processes this information, and generates updated order information that maintains consistency with the edited graph. This intermediary role simplifies consistency maintenance by centralizing the update logic in a dedicated component.
Data Source
AI summary
An improvement system includes: a first correlation calculation unit which reads parameter information, which is information regarding a plurality of evaluation indexes in a business process, and calculates a first correlation in which a correlation between the evaluation indexes is expressed by a correlation coefficient; a graph generation unit which creates a causal relationship graph, in which the evaluation indexes are set as nodes and the correlation between the evaluation indexes is represented by a link, on the basis of the first correlation and order information which is information regarding priorities of the plurality of evaluation indexes; an interface unit which presents the causal relationship graph to a user and receives a change to the causal relationship graph by the user; and an order calculation unit which updates the order information on the basis of the change to the causal relationship graph by the user, and enables the graph generation unit to generate the causal relationship graph after the change by the user.