Optimization potential refers to activities within a process that can be changed to improve process performance. For example, bottlenecks, process loops, or inefficient process flows can all be areas for optimization potential. You can realize optimization opportunities by eliminating identified weak points in a process to achieve greater effectiveness, efficiency, and/or conformity. Optimization potential is often discovered during process analysis.
How do I identify optimization potential with the help of process mining?
One of the main goals of process mining is to uncover optimization potential. Here are three approaches to identifying those opportunities:
1. Identify optimization potential through process discovery.
During process discovery, process data is visualized in an as-is process model. Using this model, you can identify performance weak points, such as excessive idle times, lengthy activities, or process loops. Process loops are obvious in a visual process model: if an activity that has already been performed is repeated for a process variant, a process loop exists.
You can examine the performance of activities and idle times in a couple ways. For example, in process mining, the longest idle time and the most time-consuming activity are automatically marked in red. In addition, you can define certain filter settings so that only all idle times, activities, or cases that exceed a certain time value are displayed.
2. Identify optimization potential through conformance checking.
To uncover unplanned process sequences with conformance checking, compare the actual process with a target process. You can generate the target process model automatically or define it based on the actual process. This way, you can define which process sequences you don’t want and display them as process deviations.
3. Identify and realize optimization potential with automated root cause analysis.
During root cause analysis, examine the weak points you found in process discovery or conformance checking. Identified attributes or attribute combinations provide specific information about the circumstances and causes of optimization potential. Using these clues, you can deploy concrete improvement measures to optimize the process.