The Pareto Principle
On Your Shop Floor
80% of your losses come from 20% of your downtime reasons.
They repeat every shift. They are already logged. They are just never ranked and never fixed
““The Pareto Principle doesn’t ask you to work harder. It asks you to work on the right 20%. On most shop floors, that conversation has never happened.”“
– Srihari, Leanworx Founder
On a visit to a CNC components plant in Coimbatore, a production head walked us through 14 different downtime reason codes his team had logged that month. He pointed at the report with quiet pride — every stoppage was captured, every shift accounted for.
Then we ran a Pareto analysis on those 14 codes.
Three reasons — tool change delays, operator unavailability, and fixture setup time — accounted for 79% of all downtime logged. The remaining 11 codes together made up 21%. His team had been spending equal energy tracking all 14, firefighting all 14, holding review meetings about all 14. Nobody had simply ranked them.
What 1 invisible idle minute
really costs you across a year
THE FLAP -
1 min
One downtime reason repeats quietly. A spindle overheating. A fixture misalignment. Small. Recurring. Never prioritised. Never fixed. Just logged and moved on.
SCALE IT -
20 machines
That same top 3 downtime reasons repeat across 20 machines, every shift, every day. The 80% of your losses-still buried inside a list of 40 downtime codes
OVER A YEAR -
2,400 hrs
20 machines x 1 recurring loss x 2 shifts x 300 working days = 2,400 hours lost annually. All from causes your team already knew about. Just never ranked.
THE STORM -
₹60-90 L
At 72,500-3,750/hr machine cost, that's 260-90 lakhs lost every year. Not from 40 problems. From 3, Fix the vital fow. The trivial many take care of themselves.
Hidden losses caught.
Profitability restored.
A mid-sized industrial components manufacturer with 100+ CNC machines was facing frequent breakdowns, reactive repairs, and unstable production. Paper-based maintenance schedules meant machines were serviced only after failure, causing long downtime and rising costs. Management believed breakdowns were unavoidable due to heavy utilization. However, deeper analysis showed the real issue was the lack of a structured preventive maintenance system. By implementing automated, usage-based and time-based maintenance with real-time alerts, the company reduced unscheduled breakdowns by 28%, improved OEE, and stabilized throughput — without investing in new machines.
A leading automobile components manufacturer was under constant pressure to invest in new machines due to perceived capacity shortages. Across their 8 plants — operating CNC lathes, VMCs, HMCs, and other critical equipment — management believed production demand could only be met through additional CapEx. However, deeper analysis revealed that machine utilization was inconsistent and significant hidden capacity existed within the current setup, making new investments unnecessary.
A leading automobile components manufacturer was under constant pressure to invest in new machines due to perceived capacity shortages. Across their 8 plants — operating CNC lathes, VMCs, HMCs, and other critical equipment — management believed production demand could only be met through additional CapEx. However, deeper analysis revealed that machine utilization was inconsistent and significant hidden capacity existed within the current setup, making new investments unnecessary.
A leading automobile parts manufacturer was struggling with unbalanced and inefficient machine usage. Across their 72 machines — including CNC lathes, VMCs, HMCs, sand moulding systems, and fettling equipment — OEE (Overall Equipment Effectiveness) was inconsistent and underwhelming. For CNC machines, OEE was as low as 32%, while other machines reached only 65%.
A leading springs manufacturer was facing an invisible yet costly problem: the absence of accurate, real-time production quantity data. Without knowing how many parts were being produced during each shift, they frequently overproduced, leading to inventory build-up and rising holding costs. On other days, they underproduced, causing delivery delays and reactive scheduling.
An aerospace components manufacturer operating 40 CNC machines across three 8-hour shifts faced a puzzling issue: despite running 24 hours a day, output remained far below expectations. Spindle run times, a critical indicator of productivity, averaged only 30%. Yet machines were booked around the clock.
The Manufacturing Principle
Series
Eight big ideas from science, philosophy, and management – explained through the language of your shop floor.
01. Butterfly Effect
READING NOW
02. Domino Effect
COMING SOON
03. Pareto Principle
READING NOW
04. Iceberg Effect
READING NOW
05. Low Hanging Fruit
COMING SOON
06. Banana Principle
READ MORE
07. Jidoka
COMING SOON
08. Hansei
COMING SOON
09. The Goldilock Principle
READ MORE
Ready to Improve Uptime and Traceability in Aerospace Manufacturing?
- Start with 1 machine free
- AS9100-compliant reporting tools built-in
- Mobile alerts, dashboards, and traceable logs — from Day 1
