Process Mining
Process Mining is the discipline of reconstructing how a business process actually runs by analyzing event logs from the underlying systems (ERP, CRM, ITSM, etc.). Where traditional process documentation captures how people think the process works, process mining shows the truth: every variant, every loop, every handoff delay, every exception. The output is a data-driven process map with cycle times, conformance rates, and bottleneck attribution. It is the prerequisite for any serious automation program because it answers the only question that matters before you automate: 'what is the process actually doing right now?'
The Trap
The trap is treating process mining as a one-time discovery tool, then automating against a snapshot. Real processes change continuously: new exception types appear, system upgrades shift handoffs, organizational changes redirect work. Without continuous monitoring, the automation you build against last quarter's process map will diverge from reality within 6-12 months. The other trap: pretty process maps with no action. Mining a process and producing a 47-page conformance report that nobody reads is a $400K dashboard, not an improvement program.
What to Do
Run process mining as a closed loop: (1) Discovery — extract event logs and produce the as-is map; (2) Diagnosis — identify the top 3-5 high-impact deviations (rework loops, manual workarounds, slow handoffs); (3) Intervention — fix process design first, automate second, change systems last; (4) Monitoring — run continuous conformance checks and alert when key metrics drift more than 15% from target. Tie every mining engagement to a named owner with a 90-day intervention timeline. No timeline, no project.
Formula
In Practice
Siemens has been one of the most public references for Celonis (the leading process mining platform), running it across order-to-cash, procure-to-pay, and accounts payable processes globally. Siemens publicly reported reducing days payable outstanding and improving on-time delivery materially after using process mining to find root causes of cycle-time variance. The decisive insight in their case was that 'the process' was actually 600+ variants — and 80% of the cycle-time problems came from 12 variants that nobody knew were happening at scale.
Pro Tips
- 01
Start with one process, not five. The first mining engagement is a learning curve for the team and the technology. Picking one well-bounded process (procure-to-pay is a popular starter) builds muscle before scaling.
- 02
Insist on event log quality before going deep. Garbage logs produce garbage maps. Most mining engagements spend 30-50% of effort on data extraction and cleansing — budget accordingly.
- 03
The most actionable metric is usually rework loop frequency, not cycle time. Rework loops point directly at process defects you can fix; cycle time is a downstream symptom.
Myth vs Reality
Myth
“Process mining will tell us what to automate”
Reality
It tells you what is happening — not what to automate. The decision of what to automate vs redesign vs leave alone is a strategic judgment that requires business context. Mining tools that promise 'automated automation recommendations' typically produce a list of expensive bots for processes that should have been redesigned.
Myth
“We need a full mining platform like Celonis to start”
Reality
Many useful process mining engagements start with SQL against a transaction log, exported to a tool like Disco or PM4Py. Enterprise platforms become necessary at scale (continuous monitoring across many processes), not for initial discovery.
Try it
Run the numbers.
Pressure-test the concept against your own knowledge — answer the challenge or try the live scenario.
Knowledge Check
A process mining engagement on procure-to-pay reveals 1,400 unique process variants across 45,000 cases over 12 months. The standard documented process represents only 38% of cases. What is the most appropriate first action?
Industry benchmarks
Is your number good?
Calibrate against real-world tiers. Use these ranges as targets — not absolutes.
Process Variant Concentration (% of cases in top 20 variants)
Enterprise transactional processes (P2P, O2C, IT service mgmt)Highly Standardized
> 90%
Healthy
75-90%
Drift
50-75%
Severe Variance
< 50%
Source: Celonis Process Excellence Report
Real-world cases
Companies that lived this.
Verified narratives with the numbers that prove (or break) the concept.
Siemens
2018-present
Siemens deployed Celonis process mining across order-to-cash, procure-to-pay, and accounts payable processes globally. The program reportedly identified hundreds of process variants where standard documentation showed one path. Material improvements were reported in days payable outstanding, on-time delivery, and working capital efficiency. The decisive insight was that fixing 12 specific variants out of 600+ accounted for the majority of cycle-time problems.
Processes Mined
P2P, O2C, AP globally
Variants Discovered
600+ in single processes
Working Capital Impact
Material (publicly disclosed)
Approach
Continuous monitoring, not one-off discovery
Real processes are wildly more variant than documented. The value of mining is finding the small number of variants that drive most of the dysfunction.
Hypothetical: B2B Distributor Order Process
2023-2024
A B2B industrial distributor with $1.4B revenue ran process mining on its order-to-cash flow. The engagement found 28% of orders had a 'credit hold release' rework loop adding an average of 6.2 days to cycle time. Root cause: the credit policy thresholds had been set in 2014 and not updated for inflation, triggering manual reviews on routine orders. Updating thresholds dropped the rework rate to 4% within one quarter. Annual working capital improvement: $4.1M.
Cases Affected
28% of orders
Extra Days per Case
6.2 days
Root Cause
Stale credit threshold
Annual Impact
$4.1M working capital
The most valuable mining findings are usually 'a policy from 2014 that nobody remembered to update.' These don't need new technology — they need attention.
Decision scenario
From Mining Output to Action Plan
You're 6 weeks into a process mining engagement on procurement. The output: 1,800 variants, 41% conformance to the standard path, 23% rework rate driven primarily by PO-invoice mismatches. The mining team wants to extend scope; the COO wants to see business impact in the next quarter.
Variants Found
1,800
Conformance
41%
Rework Rate
23%
Time Pressure
Impact this quarter
Decision 1
You have to choose where to focus the next 12 weeks. Three credible paths emerge.
Extend mining scope to AP and inventory before acting — get the full pictureReveal
Pick the top 3 PO-invoice mismatch root causes and ship fixes (system rule changes + process redesign) in 12 weeks; defer additional discovery✓ OptimalReveal
Build a Celonis dashboard showing all the variants and present it to the executive teamReveal
Related concepts
Keep connecting.
The concepts that orbit this one — each one sharpens the others.
Beyond the concept
Turn Process Mining into a live operating decision.
Use this concept as the framing layer, then move into a diagnostic if it maps directly to a current bottleneck.
Typical response time: 24h · No retainer required
Turn Process Mining into a live operating decision.
Use Process Mining as the framing layer, then move into diagnostics or advisory if this maps directly to a current business bottleneck.