AT A GLANCE
- DMAIC is a structured, step-by-step framework that drills down to the root cause of an issue through data use.·
- Lean Six Sigma, based on DMAIC, allows organizations to continuously improve and achieve permanent benefits throughout the project life cycle.
- Research shows that Process Mining, a technique to help visualize and analyze variation within a process, can accelerate the Six Sigma DMAIC
DMAIC is a data-driven approach for improving and optimizing business processes. It is one of the main tools that drive Six Sigma projects.
However, most people find it challenging to apply the full DMAIC cycle to achieve improvements when theory comes to practice. The success of the framework heavily depends on collecting appropriate data. That said, given that such data can only be collected manually, DMAIC proves to be timely and costly. Moreover, such an outdated data collection approach raises questions over the accuracy and the quality of the data, resulting in unreliable conclusions.
That is where Process Mining comes into play. As an easy and effective way of analyzing the data, it could accelerate the implementation of improvements.
SO, WHERE DOES PROCESS MINING FIT IN THE DMAIC FRAMEWORK?
The blue section in the figure below summarizes the DMAIC framework (Define, Measure, Analyze, Improve and Control), while the red section describes how Process Mining helps accelerate each step. Let’s address them one by one.
While process mining is not usually applied at this phase, it is critical to define a clear objective and scope at each project’s start. Customer’s problems should be outlined and translated into practical issues to be analyzed later.
While process mining is not usually applied at the Define phase, it is critical to define a clear objective and scope at each project’s start. Customer’s problems should be outlined and translated into practical issues to be analyzed later.
The Measure phase is one of the most critical steps in the framework, given that this is where the data is collected. Before introducing digital solutions, organizations used to collect data by manually timing each step of the process. That approach raised concerns over the quality and accuracy of the collected data. That, in return, led to inconclusive insights and results.
Process Mining has remarkably facilitated this step by extracting data already available in the information/ERP systems. Each step within the processes now would have its timestamp. That makes it easier to measure each action against its set SLAs.
Thanks to Process Mining, one needs not to worry about where and how to find the data. Instead, the concerned team would be able to focus on what the data is saying.
At the Analyze phase, one analyzes the collected data to generate insights. To begin with, Process Mining enables the team to identify where the bottlenecks, reworks, and delays are in their processes. A detailed analysis ensures to determine the root causes of the variations.
The examples below highlight how a Process Mining tool could extract data from the information system to help the team visualize the processes and their variations. For instance, it helps visualize the ‘ping pong’ behaviors between members of a team, which increases the overall cycle time of the process.
Also, Process Mining enables concerned team members to respond faster. Thanks to the visualization and analysis technique, one could quickly zoom in and focus on specific areas of the processes. That allows everyone to be involved in the early stages of the analysis, identifying the root causes of the variations together.
In the Improve phase, the previously identified root causes are translated into practical measures. We recommend linking the statistical problem (root causes) directly to a suitable solution to ensure successful implementation during the Process Mining workshops.
It is also recommended to draw conclusions based on the visuals. That would help formulate action plans to accelerate the implementation of the improvements.
The Control phase is about sustaining the benefits of the implemented improvements. Monitoring the implemented changes and their impact on the organization could have tremendous value. That’s why we recommend running regular Process Mining initiatives to keep track of the improvement measures. Constant monitoring also supports the continuous improvement culture by returning to the Measure phase and collecting data once again. However, the challenging part would be to keep the team members engaged long after the improvement plan has been implemented. One way of doing that is by running random Process Mining audits to evaluate the implemented changes’ efficiency.
For each phase of the Six Sigma DMAIC framework, Process Mining can be used to accelerate the completion of the entire cycle. One key takeaway is the advantage Process Mining provides while extracting data from the information systems. It speeds up the data collection process and the direct implementation of the improvements, resulting in quick wins for the project.