PROCESS ROOT CAUSE ANALYSIS
Process root cause analysis
Most process fixes treat symptoms. A queue grows, so a team adds people. Invoices are late, so a deadline is pushed. The delay returns because the real driver, a specific approval rule, a system handoff, a supplier segment, was never identified. Process root cause analysis breaks that cycle. Using process mining to interrogate what actually happened, it traces a symptom back to the conditions that reliably produce it, so you fix the cause once instead of managing the effect forever.
- Symptoms traced to the condition that reliably produces them
- Evidence from process mining, not the loudest opinion in the room
- Recommendations specific enough to hold up to finance and audit
Guesswork blames the loudest step. Evidence follows a disciplined path.
A structured route from symptom to cause, so the fix targets the driver, not the noise. The framework separates a step that is merely slow from the upstream condition that makes it slow, which is where most improvement effort is wasted. It is the diagnostic core of our Intelligent Process Mining practice, and it sits within Intelligent Automation. It combines the discipline of structured problem-solving with the evidence base of process mining. The framework mirrors proven root-cause methods; the data comes from your systems, so conclusions are complete rather than representative.
Symptom
What outcome is failing? Cycle time, cost per case, SLA breaches, and rework rate. Name the failure in business terms.
Localisation
Where in the flow does it originate? Bottleneck and wait-time analysis on the mined model. Find where the problem actually lives.
Correlation
What conditions travel with it? Variant, attribute, and segment analysis across case type, region, system, and actor. See what the failure clusters around.
Causation
Which conditions actually drive it? Comparative and root-cause analysis across cohorts. Separate the driver from the coincidence.
Verification
Does removing the cause remove the effect? Post-change conformance and performance comparison. Prove the fix, do not assume it.
Fix the cause once, instead of managing the effect forever.
A diagnosis you can act on, not a report you file.
- A framed symptom and the single metric that defines success.
- A process reconstructed from event data, so analysis rests on fact rather than perception.
- Localisation and segmentation that pinpoint where the problem lives and which case attributes it clusters around.
- Cause testing across cohorts that separates correlation from the real driver.
- A specific, named recommendation: change this rule, re-route this handoff, re-segment this supplier group.
- Before-and-after verification that confirms the effect is gone once the cause is removed.
Five steps, from a failing outcome to a verified cause.
Frame the symptom
Describe the failing outcome in business terms and agree the metric that defines success.
Reconstruct the process
Rebuild the flow from event data so the analysis rests on fact, not perception.
Localise and segment
Isolate where the problem lives and which case attributes it clusters around.
Test causes
Compare cohorts to separate correlation from the condition that really drives the outcome.
Recommend and verify
Propose the specific change, then confirm the effect using before-and-after mining. Implementation runs through Process Automation and Optimization.
Evidence from the systems where work already leaves a trail.
A representative diagnostic stack by layer. We work with the process mining platform you already run rather than replacing it.
Because mining follows the case across systems, it exposes cross-functional causes that a single team could never see on its own. Figures are placeholders; Softobiz to verify against your environment.
From a KPI that kept regressing to a cause finally named.
Challenge: A [global enterprise client] kept adding people to a [process] whose SLA breaches returned within [X weeks] of every fix, with no clear source.
Result: A single upstream [approval rule] identified as the driver, [XX%] of the delay removed, and the recommendation verified by before-and-after mining. (Softobiz to verify.)
Where diagnosis sits in the mining practice.
Process Discovery and Mapping
Reconstruct the real process from event data, the model root cause analysis interrogates.
Process Performance Monitoring
Flags the degradation; root cause analysis explains the why behind it.
Process Automation and Optimization
Turns the named cause into a targeted redesign and automation.
Intelligent Process Mining
The practice this diagnostic core belongs to.
Intelligent Automation
The wider practice mining and automation sit within.
Agentic AI
For the judgement-heavy exceptions the analysis surfaces.
What teams ask before they diagnose.
It combines the discipline of structured problem-solving with the evidence base of process mining. The framework mirrors proven root-cause methods; the data comes from your systems rather than from sampled observations, so conclusions are complete rather than representative.
That is common, and it is exactly what event-log analysis reveals. Because mining follows the case across systems, it exposes cross-functional causes that a single team could never see on its own.
Diagnosis is the deliverable here. Implementation runs through Process Automation and Optimization and Workflow Design and Modeling.
Take a process that keeps disappointing, and trace it to the condition that drives it.
Evidence from your own systems, a named cause, and a change you can verify.