Process Insight Generation via Selective Blocker Analysis
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Solution Overview
Problem
Current process mining techniques require significant time and expertise, often taking weeks to months to generate insights, and typically analyze the entire state space of a business process, providing either a broad overview or a focused analysis, with limited flexibility for modifying data extraction once a project is complete.
Innovation Solution
A software system that utilizes standardized milestones and blockers to analyze business processes, allowing users to select specific milestones and blockers for analysis, generating insights quickly by querying business data, and enabling the use of machine learning models to fill query templates with underlying data, resulting in faster and more focused insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional process mining techniques are used to analyze the entire state space of a business process, then comprehensive process insights are obtained, but the analysis time increases to weeks or months
Solution Approach 1:
The patent extracts and analyzes only the relevant portions of the business process state space by identifying specific milestones and their associated blockers, rather than analyzing the entire state space. This selective extraction approach maintains comprehensive insights for critical process areas while significantly reducing analysis time from weeks/months to minutes/hours.
Solution Approach 2:
The patent segments the business process into discrete milestones and blockers, allowing independent analysis of each segment. This segmentation enables the system to focus computational resources on specific process areas of interest rather than processing the entire state space uniformly, thereby reducing overall analysis time while maintaining insight quality.
2Loss of time
If process mining projects are customized to focus on specific process areas, then analysis time is reduced, but the ability to modify data extraction after project completion is limited
Solution Approach 1:
The patent implements a dynamic system where milestones and blockers can be selectively added, removed, or modified after the initial analysis setup. The system allows users to dynamically adjust which process areas are monitored and analyzed, enabling flexible modification of data extraction focus without requiring complete project reconfiguration, thus maintaining both speed and adaptability.
Solution Approach 2:
The patent creates a universal framework that can analyze multiple different process areas through a common milestone and blocker structure. This multi-functional system can be applied to various business processes and can be easily reconfigured to focus on different process areas, providing both rapid analysis and long-term adaptability across different contexts.
3Measurement precision
If traditional process mining requires hard-to-find expertise and weeks of customization, then accurate process insights are generated, but the cost and complexity of implementation increase
Solution Approach 1:
The patent implements a self-service system where the automated milestone and blocker identification algorithms perform the analysis without requiring extensive manual customization or expert intervention. The system automatically extracts relevant process information, identifies milestones and blockers, and generates insights, thereby maintaining accuracy while dramatically reducing implementation complexity and eliminating the need for specialized process mining expertise.
Solution Approach 2:
The patent performs preliminary identification and classification of milestones and blockers automatically during the initial setup phase, creating a reusable framework for ongoing analysis. This preliminary action establishes the analytical structure once, enabling subsequent rapid analyses without requiring repeated expert intervention or complex customization, thus reducing both implementation complexity and ongoing operational complexity.
Data Source
AI summary
The example embodiments are directed to systems and methods which can generate process insights in a manner that is significantly faster than process mining. In one example, a method may include loading a schema of a data system into memory, identifying a plurality of events that occur within a process supported by data from the data system and a plurality of blockers that prevent the plurality of events from completing, defining a query that comprises instructions for extracting data of the plurality of blockers from the data system and for analyzing the process based on the extracted data and the plurality of blockers, and generating a script for executing the defined query based on the schema of the data system and transmit the script to a software application.