Hierarchical Process Flow Modeling for Dynamic Quality Assessment
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Solution Overview
Problem
Conventional process flow modeling systems are static, linear, and lack multi-dimensional, collaborative, and interconnected features, failing to provide data-driven and dynamic capabilities for capturing, visualizing, and assessing the interdependent aspects of process flows.
Innovation Solution
A system and method for creating dynamic, hierarchical process models that utilize a process model application to aggregate and disseminate information related to events and activities, employing a catalog of catalog elements to facilitate the creation of user stories and functional requirements, and enabling the aggregation and dissemination of data across various levels of an enterprise hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional process flow modeling systems are used, then the system structure is simple and easy to operate, but the system lacks multi-dimensional, collaborative, and interconnected features, failing to provide data-driven and dynamic capabilities
Solution Approach 1:
The system divides the process flow model into hierarchical levels (enterprise level, business process level, task level) and modular components (events, activities, data flows). This segmentation enables complex interdependent aspects to be managed through structured decomposition while maintaining overall system coherence and enabling multi-dimensional analysis capabilities.
Solution Approach 2:
The patent implements nested hierarchical structures where lower-level details (tasks, activities) are contained within higher-level processes, which are contained within enterprise-level goals. This nesting enables the system to accommodate complexity at multiple levels while providing a unified view that resolves the contradiction between adaptability and structural simplicity.
2Loss of information
If conventional linear sequential process flow views are used, then the layout is simple and easy to understand, but the system does not provide opportunities for data-driven and dynamic features to capture and visualize interdependent aspects
Solution Approach 1:
The system transitions from conventional two-dimensional linear sequential views to multi-dimensional visualizations that include temporal, hierarchical, and interdependent dimensions. This enables comprehensive capture of process interdependencies while maintaining usability through structured presentation of complex relationships across multiple axes.
Solution Approach 2:
The system incorporates data-driven feedback mechanisms that automatically update process flow visualizations based on executed process data, enabling dynamic adjustment of the model to reflect actual interdependencies and variations, thereby reducing information loss while managing visualization complexity through adaptive rendering.
3Reliability
If static process flow models are used, then the model structure is stable and easy to maintain, but the system cannot provide dynamic and data-driven assessment of process quality and feasibility
Solution Approach 1:
The system implements dynamic process flow models that automatically adjust and update based on executed process data, enabling data-driven assessment of process quality and feasibility. The model structure maintains stability through hierarchical organization while incorporating dynamic elements that respond to actual process performance, resolving the contradiction between reliability and structural simplicity.
Solution Approach 2:
The system dynamically changes model parameters such as timing, resource allocation, and process routing based on executed process data and performance metrics. This enables accurate assessment of process quality while managing complexity through parameterized adjustments rather than complete structural reconfiguration.
4Difficulty of detecting and measuring
If conventional static event editing is used, then the editing process is simple and straightforward, but the system does not reveal inconsistencies, inaccuracies, connections among process flows, and opportunities
Solution Approach 1:
The system incorporates automated feedback mechanisms that analyze the process flow model for inconsistencies, inaccuracies, and connection opportunities, providing intelligent assistance during the editing process. This enables detection of complex issues while maintaining ease of operation through automated analysis rather than manual inspection of all connections.
Solution Approach 2:
The system introduces an intermediary analysis layer that automatically examines relationships between process flow elements, events, and data flows. This intermediary function reveals hidden connections and potential issues without requiring users to manually trace all relationships, thereby improving detection capability while preserving editing simplicity.
Data Source
AI summary
A network system that associates data traits and characteristics of a hierarchical arrangement of process elements within a process model application. The hierarchical arrangement organizes a process flow into enterprise hierarchy structures. The system will automatically receive data related to traits and create characteristics that allows the system to measure and utilize qualitative aspects of the process. The system creates user interface displays of the characteristics data to inform user decisions.