Automated Root Cause Analysis in '24: 4 process mining benefits

Process mining vendors include root cause analysis as an additional process analytics feature to diagnose errors, problems and deviations. Some vendors distinguish their root cause analytics by emphasizing machine learning capabilities. However, business leaders and process analysis teams are often confused about root cause analysis and its applications in process mining.  

This research explains root cause analysis and its use cases in process mining. 

What is root cause analysis?

Root cause analysis is a method to identify the root causes of problems, deviations or errors and analyze them. This method has been used in various industries and business functions such as IT operations, healthcare systems, accident analysis and industrial process checks.

The method determines the underlying cause of an issue in a process and clusters process problems that have the same reason. Then, it dives into impact factors causing problems. 

How does root cause analysis work?

Process mining software leverages machine learning to automate root cause analysis. Therefore, vendors refer to root cause analytics as automated or AI-powered root cause analysis. 

ML algorithms cluster the problems to perform root cause analysis. Then, algorithms diagnose the factors impacting these problems to determine which factors correlate with which issues and which can cause the issue. These algorithms look for conspicuous structures and correlations in the data. 

For example, suppose a P2P process where “invoice check” is detected in 20% of the cases. The root cause analysis reveals that all these cases are related to suppliers. Therefore, the cause and problem correlation is defined as strong 100%.

Process mining applies root cause analysis to any activity, idle time, and process stage for conformance checking and performance analysis. 

The following are the top use cases of root cause analysis in process mining:

1. Detect long durations and delays in processes

In root-cause analytics, users can investigate a particular vulnerability like processes that take longer times than they should. The algorithm searches the defined time deviation and sorts out the processes that do not last very long. Then, the algorithm evaluates the attributes of these cases with long cycle times to identify which factors might prolong the processes.  

Example:

In a process mining case study, VGZ, an insurance company in the Netherlands, employed process mining to analyze dental care processes. By visualizing the dental care process, the company detected long waiting periods between meeting the medical advisor and the follow-up steps. By combining process mining with the lean process improvement tools to understand the root causes of the problems, VGZ were able to reduce the throughput time by 40%. 

2. Standardize operations

Process mining helps businesses detect their high-performance process activities and risk factors leading to problems. By implementing root cause analysis, business analysts can identify the factors that bring success or cause issues and detect trends in processes. These insights enable organizations to standardize their operations.

Example:

A service provider applied process mining with root cause analysis to identify SLAs in accounts payable processes. The company identified 5,000 process variants. All these variants were clustered, and the company was able to identify the top 3 variants, which negatively affected the time and quality of 90% of the documents. Also, the company discovered the differences among approval processes causing these variations. As a result, they were able to standardize their invoice approval process. 

3. Enhance compliance check

Process mining can be a helpful tool for automatic and data-driven operational compliance since it can monitor processes and indicate the deviations and violations of rules in the activities. Root cause analysis digs into the violations to show when and why they occur. By doing so, it empowers process mining compliance checking. 

Example:

For example, an organization deployed process mining to monitor and improve the performance of their procure-to-pay processes. Integrating root cause analysis with process mining enabled the organization to identify the cause of compliance violations and analyze their spending under management.

4. Discover the costly processes 

Businesses can apply root cause analysis to understand the underlying causes of high-cost processes. Companies can optimize their strategies to cut costs once they discover the main factors behind the costly processes.   

Example:

As a real life example, A utility company, Essent NC (Netherlands), leveraged process mining integrated with root cause analysis features to discover why the firm loses money in the payment collection process. As a result, the analysts identified the time management of the termination of the contract as the main problem. 

Further Reading

To discover more on how ML and AI is used in process mining, feel free to read our relevant articles:

• Top 6 Applications of Machine Learning in Process Mining
• Process Mining and Automated Process Discovery

If you believe your business can leverage process mining, you can check our data-driven software list.

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