Process Mining: Find Bottlenecks Before They Cost You Money
Every business has hidden losses: waiting between steps, “ping-pong” rework, duplicated effort, avoidable errors. Process mining shows how work really flows in your systems (ERP, CRM, service desk, etc.), based on actual event data. It builds a live map of the process—where it slows down, why it happens, and what to change—so you can fix issues before they turn into missed SLAs, write-offs, or churn.
What it really is (no theory)
Your systems already log events: which case/order, what step, when it happened. Process mining stitches those events together and reveals the real path a document or customer takes—where queues form, where rework loops appear, and where steps are out of order. It’s not “another system to implement,” it’s a way to get value from data you already have.
Where it pays off right now
Best first candidates for small and mid-sized businesses:
• Procure-to-Pay (P2P): see why invoices sit for weeks, where early-payment discounts are lost, and which approvals are unnecessary.
• Order-to-Cash (O2C): uncover slow credit checks, shipment delays, and invoicing bottlenecks that slow cash collection.
• Service & Support: find queues between tiers, high “bounce-back” rates, and long approvals that hurt response times.
• Finance / Operational control: spot steps out of sequence, skipped controls, and off-policy detours immediately.
Outcome: shorter wait times, fewer reworks, better SLA discipline, and a clear picture of what actually hurts performance.
A fast start in 4 steps
1. Pick one process with volume and pain (e.g., invoice approvals).
2. Export the basics from your system: case/order ID, activity name, timestamp. That’s enough to see the traffic jams.
3. Find the bottlenecks: queues, rework loops, and costly path variants.
4. Make simple fixes: auto-routing, auto-approval thresholds, reminders, and small policy changes. Then re-measure to prove impact.
The metrics that matter
• Cycle time (how long it takes from start to “money/closure”).
• Rework rate (how many cases bounce back for fixes).
• Path variants (how many “off-track” routes and how costly they are).
• SLA breaches / aging (where the “red zones” occur most).
• First-pass yield (finished right the first time).
Most tools (Microsoft, Celonis, SAP, etc.) track these out of the box, so you can show clear before/after without heavy math.
A simple P2P example
After the first pass you see: supplier X with amount >Y almost always stalls with approver Z. Lightweight changes—clear thresholds, auto-escalations, reminders—typically cut cycle time by double digits and reduce returns. P2P often hides the quickest savings (discounts, working capital, fewer manual errors).
Risks—and how to avoid them
• Automating chaos: fix the process you have before you automate it.
• Bad data: agree on basic fields and time zones; add a simple export-quality check.
• Boiling the ocean: start with one process for 4–8 weeks, prove the economics, then scale.
• Too academic: focus on “where it slows” and “what we change tomorrow,” not jargon.
A 6–8 week roadmap
• Weeks 1–2: connect data, build the map, surface the top 3 bottlenecks.
• Weeks 3–5: implement quick wins (rules, thresholds, reminders, small automations).
• Weeks 6–8: re-measure KPIs; codify changes in the playbook; choose the next process.
What you walk away with
• A clear picture of how work really flows.
• A short, quantified list of blockers with business impact.
• A top-down action plan: policy changes, what to automate, and what to monitor.
• A simple dashboard that shows what got faster and cheaper.