Just a few years ago, pen and paper were the tools of choice for mapping processes and seeking areas for improvement. However, this approach had its limitations, as once a report was completed, the process might have undergone significant changes, rendering the report obsolete.
Enter process mining technology, a game-changer in the realm of process improvement. Utilising Artificial Intelligence (AI) and Machine Learning (ML), process mining automatically extracts, visualises, and interprets operational data from organisations’ IT systems. This eliminates the pitfalls of static methods like pen and paper.
Specifically, tools like QPR ProcessAnalyzer leverage process mining to provide a dynamic visualisation, often referred to as a “digital twin,” of your organisation. This digital representation enables you to uncover valuable insights, such as areas for cost savings, opportunities for process simplification, and areas where automation can be enhanced.
What sets process mining apart is its ability to not only identify root causes of existing issues but also predict potential problems. Moreover, it offers automatic alerts, allowing for proactive intervention before problems escalate. This proactive approach can significantly impact organisational efficiency.
This blog sheds light on the integration of Artificial Intelligence and Machine Learning in process mining. It explores how these technologies cluster cases, predict problems, and contribute to a more intelligent and efficient understanding of organisational processes.
What is Artificial Intelligence-driven Intelligent Process Mining?
When employees or software robots engage with IT systems like SAP, Salesforce, or Oracle, their activities generate a trace of data known as an event log. Process mining utilises pre-built connectors to extract this data from information systems and creates visualisations of your company’s real-life processes. These visualisations, based on event logs, offer valuable insights.
It points out what you should focus on in order to improve your efficiency, for instance:
AI-driven process mining helped a major European bank shift focus from seeking new automation opportunities to improving existing initiatives.
Within two months, they boosted their zero-touch rate from 5% to 40%, resulting in €2.6 million cost savings.
Large companies, particularly those engaged in internal audit and risk management, utilise process mining to enhance efficiency and transparency in their complex, geographically dispersed operations. Intelligent process mining enables analysis and issue identification within 2-3 hours, a significant improvement from the three weeks it took in the past.
Stewart Wallace, Risk Analytics Manager at EY UK, stated, “We now embed process mining in real-time, identify bottlenecks instantly, and take actions much earlier.”
Process Mining and Machine Learning (ML): the Technical Explanation
Intelligent Process Mining combines machine learning with traditional process mining.
Its capabilities fall into four categories:
1. Descriptive Process Mining
Originally, process mining emerged as a descriptive method for pattern discovery and understanding real business operations. Unlike traditional BI, process mining not only monitors KPIs but also enables quick real-time analyses. These analyses unveil optimisation areas, such as bottlenecks, compliance issues, and process deviations, based on actual operations.
“We gave the data of the system, and right away, in 5 minutes, we saw the bottlenecks of the process.”
– Piraeus Bank
The following three analyses turn the traditional process discovery to intelligent process discovery:
- Process Mining Clustering – ML naturally groups similar cases into the same clusters
- Process Mining Anomaly Detection – ML detects outliers
- Process Mining Similarity – ML finds similar cases based on an example
2. Diagnostic Process Mining
Diagnostic process mining goes beyond identifying “what” and “when” in process issues; it answers “why.” By showcasing problem areas in flowcharts and ranking their impact on business outcomes, process mining guides priorities for improving business operations.
The following functionalities show you why your problems occurred:
- Root Cause analysis – Find the root causes for any identified process problems
- Problem classification – Use machine learning algorithms to classify problems
- Trend analysis – Understand how the process has changed over time
3. Predictive Process Mining
In the third step, predictive process mining anticipates future problems. It uses data from past and ongoing cases, allowing the machine learning system to predict outcomes for each case. More data enhances prediction accuracy.
Different predictive process mining scenarios include:
- Predict KPI outcome – ML predicts the outcome of any KPI for all ongoing cases
- Predict the next event – ML predicts the next event for all ongoing cases
- Predict final outcome – ML predicts all future events and KPIs for all ongoing cases
4. Prescriptive Process Mining
The final step, prescriptive process mining using QPR ProcessAnalyzer, introduces an ML-based Intelligent Orchestrator to enhance your operations. This Orchestrator learns and improves over time, becoming an even better companion.
Jobs for the Intelligent Orchestrator include:
- Sending email notifications
- For instance, notify people about situations where an existing business rule is about to be violated
- Activating RPA bots
- Activating new business workflows
- Updating data in ERP systems
Artificial Intelligence and Machine Learning features in QPR ProcessAnalyzer
Over the past years, QPR heavily invested in enhancing QPR ProcessAnalyzer with AI-powered features, positioning it as a leading solution in process mining. The Predict & Act functionality goes beyond standard KPI monitoring, employing AI/ML-based predictions. It enables proactive actions before potential issues, shifting organisations from late fixes to preventive measures.
With the new Predict & Act functionality, process mining can:
- Trigger an RPA bot to fix your faulty master data on the ERP system
- Let you know when deliveries are predicted to be late or SLAs are about to be broken
- Start a workflow for the escalation team to fix the vital information before any major business challenges take place.