Process enhancement, sometimes referred to as model enhancement, is a process mining technique that’s used to extend or enhance a target model or reference model using discovered information about the actual process. For example, analysis may uncover bottlenecks or unplanned process sequences that can be eliminated from the model to make it a better representation of the ideal process.
A data-driven process model is created using a log file of the actual 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 process enhancement is to optimize the process model and the process itself.
The results of the process enhancement serve as the reference point for future analysis with process discovery and conformance checking. Correctly implementing identified optimization potential is critical. Making the wrong changes, or unfavorable changes, can be costly, resulting in higher expenses, rule violations, or quality defects.
Suppose you perform a process mining analysis with the log file of a business process. Using the process discovery and conformance checking methods, you identify various process weaknesses, including bottlenecks, process loops, and unwanted process deviations. This is all valuable information. Now you know where to find which types of optimization potential.
But what do you do with this knowledge? You specifically adapt your target model, which serves as the standard guideline for process implementation. This means that you 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.
Related terms: Process Mining, Analysis, Process Model
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