Process Understanding Magnified
To better illustrate the benefits of joining the data, let’s trace a high-value experience. A leading manufacturing organization begins their process improvement journey by using a process mining tool to identify process landmarks, analyze process deficiencies, and get evidence of what is really happening regarding committed data. After the deployment of a task mining solution, its data is combined with the process mining data to gain near complete transparency over current processes. With nowhere to hide, knowledge gaps, poor documentation, and other process inefficiencies become clearer targets for improvement. While current processes are being improved, a prioritization list is forming.
By analyzing the frequency, duration, and cost of production processes, along with the different process variants occurring for similar processes, a list of test automation targets is being built – prioritized by ROI. A test automation product is implemented, showing quick turnaround gains by reusing the same step-by-step process data captured from the task mining solution.
With a broad and comprehensive set of test automation coverage, built from actual production understanding and practices, this manufacturing organization confidently begins building their digital workforce. Utilizing the same ROI metrics for process and task mining that were used to prioritize test automation, the company now incorporates test automation data to ensure that RPA goals are also completely supported by rigorous automated tests.
Since no process optimization effort is ever complete, the organization drives continuous improvement through the insights gained from the shared process data. By continuously tracking process flows, defining and prioritizing new test automation goals, and confidently extending RPA, the company has created a 360-degree world of holistic process knowledge – understood process intelligence. None of which would be possible without the use of AI/ML.