Model Enhancement

In process mining, model enhancement describes the analysis of a data-driven process model for optimization potential. The data-driven process model is based on a log file of the process. Based on this information, improvements or changes are made. For example, bottlenecks or unplanned process sequences identified in this way can be eliminated. The objective of model enhancement is to optimize the process model and thus the process itself.

Model Enhancement - Creation of a New Process Model

Why is model enhancement so important?

The results of the model enhancement reflect the quality of the analysis and are the reference point for future analysis. The results of model enhancement are used in process discovery and conformance checking analysis techniques. The correct implementation of identified optimization potentials is essential: if a company makes unfavorable process changes, it can have costly consequences, for example, higher expenses, rule violations, or quality defects. And until the process issue has been identified and eliminated, it usually takes some time.

 

What exactly does model enhancement do?

Suppose we perform a process mining analysis with the log file of any business process. Using the process discovery and conformance checking methods, we identify various process weaknesses, including bottlenecks, process loops, and unwanted process deviations. This is all valuable information. Now we know where to find which types of optimization potential.

But what do we do with this knowledge? We specifically adapt our target model, which serves as the standard guideline for process implementation. This means that we change the process in such a way that the risk of bottlenecks is reduced or certain process sequences are no longer possible. The success or usefulness of the process changes can only be determined after the new process has been implemented for some time. Therefore, process mining is an excellent way to continuously improve your process. 

In practice, processes are usually subject to step-by-step, marginal changes.

 

Here are the steps to model enhancement:

  1. Analyze the process data.
  2. Identify optimization potentials (process discovery, conformance checking).
  3. Adapt the target process model (model enhancement).
  4. Implement the target process model.
  5. Check the new process implementation on the basis of the analysis of the process data (continuous improvement process).

Related terms: Process Mining, Analysis, Process Model

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Process Mining Glossary

Conformance Checking    |     Cycle Time    |     Event Log    |     Machine Learning    |     Process Cycle    |    Process Discovery   |    Process Engine   |    Process Execution    |    Process Management Life Cycle    |    Wait Time

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