In today’s data-driven business environment, organizations are constantly seeking innovative ways to understand, optimize, and improve their operational processes. Process mining has emerged as a powerful analytical technique that bridges the gap between traditional process improvement methodologies and modern data science. This comprehensive guide explores how process mining techniques are applied during the Analyse phase of process improvement initiatives, providing you with actionable insights to transform your business operations.
What is Process Mining?
Process mining is an advanced analytical technique that uses event log data from information systems to create visual representations of actual business processes. Unlike traditional process analysis methods that rely on interviews and observations, process mining leverages real digital footprints left by transactions in enterprise systems such as ERP, CRM, and workflow management tools. You might also enjoy reading about Value-Added vs. Non-Value-Added Analysis: Identifying Waste in Your Process.
The technique allows organizations to discover how processes actually work in reality, rather than how they are supposed to work according to standard operating procedures. This distinction is critical because it reveals the gap between theoretical process designs and actual execution, uncovering inefficiencies, bottlenecks, and compliance issues that might otherwise remain hidden. You might also enjoy reading about Minitab for Analyze Phase: Key Statistical Tests and How to Run Them in Lean Six Sigma.
The Role of Process Mining in the Analyse Phase
The Analyse phase is a crucial component of Lean Six Sigma methodology, positioned between the Measure and Improve phases in the DMAIC (Define, Measure, Analyse, Improve, Control) framework. During this phase, teams investigate the root causes of process problems and identify improvement opportunities. Process mining enhances this phase by providing objective, data-driven insights that complement traditional statistical analysis.
By applying process mining during the Analyse phase, organizations can systematically examine process variations, identify deviation patterns, and quantify the impact of different process paths on overall performance metrics. This level of detailed analysis enables teams to make informed decisions based on factual evidence rather than assumptions or incomplete information.
Core Process Mining Techniques
Process Discovery
Process discovery is the foundational technique that automatically generates process models from event logs. This technique creates visual flowcharts that show all the different paths transactions take through a system, including frequency and timing information.
Consider a practical example from an order fulfillment process. An e-commerce company collected event logs from their order management system over three months, capturing 50,000 order transactions. Each order created digital footprints at various stages: Order Received, Payment Verified, Inventory Checked, Items Picked, Items Packed, Shipment Created, and Order Delivered.
The process discovery algorithm analyzed these event logs and revealed that while the standard process had 7 steps, actual orders followed 23 different pathway variations. The most common path (35% of orders) followed the standard sequence, but 15% of orders required a “Payment Re-verification” step, 12% needed an “Alternative Item Selection” due to inventory issues, and 8% went through a “Return to Warehouse” loop before final delivery.
This discovery immediately highlighted several issues. The payment re-verification step added an average of 18 hours to order processing time, while inventory-related deviations added 36 hours on average. Without process mining, these patterns would have been extremely difficult to detect through manual analysis.
Conformance Checking
Conformance checking compares the discovered actual process against a predefined reference model or standard operating procedure. This technique identifies where and how often processes deviate from the intended design, highlighting compliance issues and unauthorized workarounds.
Using our order fulfillment example, the company had a documented standard operating procedure that specified orders should move directly from “Payment Verified” to “Items Picked” within 4 hours. However, conformance checking revealed that only 58% of orders followed this path within the specified timeframe.
The analysis showed that 22% of orders experienced delays due to system integration issues between the payment gateway and inventory management system, causing manual intervention. Another 20% of orders bypassed the “Inventory Checked” step entirely, proceeding directly to picking, which occasionally resulted in picking failures and rework.
This conformance analysis provided concrete evidence of process violations and their frequency, enabling the team to prioritize improvement efforts based on actual impact rather than perceived problems.
Performance Analysis
Performance analysis focuses on timing aspects of processes, measuring throughput times, waiting times, and processing times for different activities and process variants. This technique helps identify bottlenecks and resource constraints that impact overall process efficiency.
In our ongoing example, performance analysis revealed detailed timing metrics for each process step. The data showed that while “Items Picked” had an average processing time of 45 minutes, this varied significantly based on order size. Orders with 1-3 items averaged 28 minutes, while orders with 10 or more items averaged 97 minutes.
More critically, the analysis identified significant waiting times between certain steps. Orders waited an average of 6.5 hours between “Payment Verified” and “Inventory Checked,” despite the actual inventory checking process taking only 3 minutes. This indicated a resource allocation problem rather than a process complexity issue.
The performance data also revealed that orders processed during shift changes (2 PM to 4 PM) experienced 40% longer cycle times compared to orders processed during peak staffing hours, suggesting scheduling optimization opportunities.
Variant Analysis
Variant analysis examines different paths that transactions take through a process, categorizing them and analyzing the characteristics and performance of each variant. This technique is particularly valuable for understanding process complexity and identifying high-impact improvement opportunities.
The e-commerce company’s variant analysis categorized their 23 process variations into five main groups: Standard Process (35% of cases, average cycle time 48 hours), Payment Issues Variant (15% of cases, average cycle time 72 hours), Inventory Issues Variant (12% of cases, average cycle time 96 hours), Combined Issues Variant (8% of cases, average cycle time 144 hours), and Expedited Process (30% of cases, average cycle time 24 hours).
This categorization immediately revealed that 35% of orders (those with payment, inventory, or combined issues) consumed 60% of total processing resources and generated 75% of customer complaints. Armed with this insight, the improvement team could focus specifically on reducing the frequency and impact of these problematic variants.
Practical Applications and Benefits
Process mining techniques deliver tangible benefits across various business contexts. In healthcare, hospitals use process mining to optimize patient flow through emergency departments, reducing waiting times by identifying bottlenecks in triage, examination, and treatment processes. Manufacturing facilities apply these techniques to analyze production workflows, discovering hidden inefficiencies in machine utilization and material handling.
Financial services organizations leverage process mining to improve loan application processing, identifying steps where applications stall and understanding why approval times vary significantly across different branches or loan officers. The objective, data-driven nature of process mining eliminates speculation and provides concrete evidence for process improvement decisions.
Integrating Process Mining with Lean Six Sigma
Process mining complements traditional Lean Six Sigma tools perfectly. While statistical process control charts and capability analysis provide quantitative measures of process performance, process mining reveals the structural and flow-based aspects of processes. Root cause analysis becomes more effective when supported by process mining insights, as teams can see exactly where and when problems occur in the process flow.
The combination of process mining with Six Sigma’s rigorous analytical framework creates a powerful approach to process improvement. Process mining provides the “what” and “where” of process problems, while Six Sigma’s statistical tools help explain the “why” and “how much.” Together, they enable comprehensive process understanding that drives meaningful improvements.
Getting Started with Process Mining
Implementing process mining requires three essential elements: quality event log data, appropriate analytical tools, and skilled practitioners who understand both process improvement methodologies and data analysis techniques. Organizations should start with well-defined processes that have reliable digital records, allowing teams to build confidence and expertise before tackling more complex scenarios.
Success with process mining depends heavily on data quality. Event logs must contain accurate timestamps, clear activity descriptions, and consistent case identifiers. Many organizations discover data quality issues during their first process mining projects, leading to valuable improvements in their data governance practices.
Conclusion
Process mining techniques represent a significant advancement in how organizations understand and improve their business processes. By leveraging the digital traces left in information systems, these techniques provide unprecedented visibility into process reality, enabling data-driven decisions during the Analyse phase of improvement initiatives. The combination of process discovery, conformance checking, performance analysis, and variant analysis creates a comprehensive toolkit for uncovering improvement opportunities that traditional methods might miss.
As business processes become increasingly complex and digital, the ability to apply process mining techniques becomes a valuable competitive advantage. Organizations that master these techniques can respond more quickly to operational challenges, optimize resource utilization more effectively, and deliver better outcomes for customers and stakeholders.
Whether you are a quality professional, business analyst, operations manager, or process improvement specialist, developing process mining capabilities will enhance your ability to drive meaningful organizational change. The insights generated through these techniques transform the Analyse phase from a time-consuming investigation into an efficient, evidence-based discovery process that accelerates improvement initiatives and delivers measurable results.
Ready to master process mining and other advanced analytical techniques? Enrol in Lean Six Sigma Training Today and gain the skills needed to lead data-driven process improvement initiatives in your organization. Our comprehensive training programs combine theoretical knowledge with practical applications, preparing you to deliver measurable results and advance your career in process excellence.
