Process Model Benchmarking Using Complexity and Handoff Metrics
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Solution Overview
Problem
The analysis of enterprise-level software processes is computationally expensive and resource-intensive, requiring significant time and effort due to the complexity of process mining algorithms and large volumes of data, making it challenging to efficiently compare and improve business processes across organizations.
Innovation Solution
A method for evaluating and benchmarking process models by analyzing high-level features such as the number of elements, roles, handovers, and complexity metrics, allowing for comparison against reference metrics and aggregated values, and providing user interfaces for model modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If automated process discovery techniques are used to analyze business processes, then process analysis capability is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent extracts only the essential characteristics and metrics from process models (e.g., number of activities, sequence flows, decision points, roles) rather than performing complete process mining analysis. This extraction approach enables benchmarking while significantly reducing computational resource consumption compared to full automated process discovery.
Solution Approach 2:
The patent applies partial action by evaluating only specific metric characteristics of process models (such as complexity metrics, size metrics, and structural metrics) rather than performing exhaustive process analysis. This partial evaluation provides sufficient information for benchmarking while avoiding the excessive computational resources required for complete process mining.
2Measurement precision
If complete process mining analysis is performed on large volumes of event data, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent extracts key metric values directly from process model representations without performing complete process mining on event logs. By working with pre-defined model metrics (number of elements, roles, handoffs, complexity measures), the system achieves sufficient measurement precision for benchmarking while dramatically reducing analysis time.
Solution Approach 2:
The patent assumes process models have already been created and contain necessary metric information. By utilizing pre-computed model characteristics and comparing them against benchmark data, the system avoids the time-consuming preliminary steps of data collection, preprocessing, and complete process discovery that would be required for full process mining analysis.
3Reliability
If detailed process model analysis is performed to ensure data quality and completeness, then data quality is improved, but computational complexity increases
Solution Approach 1:
The patent extracts essential metric information from process model representations without performing complex validation and quality assessment procedures. By focusing on fundamental model characteristics (elements, roles, handoffs, structural metrics), the system maintains adequate data quality for benchmarking while avoiding the computational complexity of comprehensive data quality verification.
Data Source
AI summary
The present disclosure provides techniques and solutions for benchmarking process models by evaluating characteristics of the model, such as those reflecting model complexity. Metrics can include the number of elements in a model, the number of roles, and the number of handoffs between roles, as a few examples. Metrics for a model can be compared with reference metrics, such as those calculated from a set of other models, which can be for the same modeled process or different processes. Collections of process models can be evaluated in a similar manner, including for a set of related models that may be expressed at different levels of specificity. Metrics for individual models in the collection can be evaluated and aggregated, and then compared with aggregated metric values of other model collections, for the same or different modeled processes.