Temporal Process Mining for Relevant Improvement Trajectories
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
It is difficult for entities to identify and implement process improvements based on data from similar entities that have already improved their processes, as existing methods struggle to determine what changes led to higher performance and often require extensive data mining that is time-consuming and resource-intensive.
Innovation Solution
Process mining is used to identify a set of reference entities with similar characteristics to a target entity at an earlier time, analyzing their process changes to suggest improvements based on their performance enhancements, which are then applied to the target entity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If process mining is performed on multiple entities to identify process improvements, then the quality of improvement recommendations improves, but the time and computational resources required increase significantly
Solution Approach 1:
The patent segments the process mining analysis by creating temporal snapshots of entity processes at different points in time. Instead of analyzing all entities continuously, the system divides the analysis into discrete time periods, identifying process changes that occurred between snapshots. This segmentation reduces the computational burden while maintaining the ability to identify meaningful process improvements.
Solution Approach 2:
The system performs preliminary filtering to identify only those entities that actually changed their processes between time periods. By pre-identifying entities with process changes before conducting detailed analysis, the system avoids unnecessary computation on entities that remained static, thereby reducing overall processing time while preserving the quality of recommendations for entities that did change.
2Measurement precision
If process mining analyzes detailed process changes across multiple entities, then the accuracy of identifying effective process changes improves, but the complexity of data processing increases
Solution Approach 1:
The patent extracts and isolates only the specific process changes that occurred between time periods, rather than analyzing entire process flows. By taking out only the changed elements (added, removed, or modified process steps) and analyzing those separately, the system maintains high accuracy in identifying effective changes while reducing processing complexity compared to analyzing complete process datasets.
Solution Approach 2:
The system applies different analysis methods to different types of process changes based on their characteristics. For example, added process steps are analyzed differently from removed steps or modified steps. This localized approach to analysis improves accuracy for each change type while avoiding the overhead of applying a single complex analysis method to all changes uniformly.
3Adaptability or versatility
If the system identifies reference entities with similar characteristics to provide actionable recommendations, then the relevance of recommendations improves, but the difficulty of matching entities increases
Solution Approach 1:
The patent uses parameter-based similarity matching, comparing entities based on key parameters or attributes (such as industry, size, process type) rather than attempting comprehensive entity comparison. By focusing on the most relevant parameters for process similarity, the system achieves high recommendation relevance while avoiding the computational complexity of full entity matching.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
The present disclosure provides techniques and solutions for improving computer-implemented processes. Process mining is performed to identifying processes of a first entity. Characteristics of the first entity at a first time are used to identify a set of reference entities having similar characteristics to the first entity at a second, earlier time. Process mining is performed for the set of reference entities to identify process changes that led to process improvements for the set of reference entities. These process changes are used to suggest process changes to the first entity that may improve performance of the process.