Coimbatore Spinning Mills
Spinning & Yarn Manufacturing · Est. 1976
How a 42,000-spindle mill pushed OEE from 68% to 89% in nine months.
A mid-sized spinning group used TextileERP's MES + predictive maintenance stack to unlock 21 points of OEE — equivalent to buying 9,000 additional spindles without adding a single machine.
+21pts
OEE gain
68% → 89%
42%
Less downtime
per shift
₹7.8Cr
Extra revenue
annualized
1.4%
Waste yarn
from 3.1%
The Challenge
Count-change losses, unplanned downtime, and invisible doffing delays were silently bleeding 31% of theoretical capacity every shift.
The Solution
Real-time spindle monitoring, predictive maintenance, and count-change optimization tied into a shop-floor OEE dashboard every supervisor could read.
The Outcome
+21pts oee gain in 9 months.
A mill running at 68% — and nobody could explain why
Every quarterly review showed the same number: 68% OEE. Management knew the benchmark for their machine age should be 82-85%. But when they asked 'where are we losing the 17 points?' — nobody had a concrete answer. Operators blamed the machines. Maintenance blamed the operators. Production blamed procurement for count-change delays.
The real problem: no one had continuous, operator-free data. OEE was calculated weekly, from log sheets, after the fact. By the time a losing trend was visible, a week had already been lost.
Key pain points
- OEE stuck at 68% against an industry benchmark of 82-85%
- 42 breakdowns per week, mostly unplanned
- Count changeovers averaging 4.5 hours — competitors did it in 2
- 3.1% waste yarn rate — ₹38L/month written off
Sensors, software, and a dashboard every supervisor lives in
We instrumented every frame with vibration and power-draw sensors feeding TextileERP's MES module. For the first time, the mill had second-by-second data on what every spindle was doing. We then layered predictive maintenance — training models on three years of historical breakdown data.
The unlock wasn't the technology. It was the 52-inch dashboard we put on every shop-floor wall showing live OEE per line. Every supervisor, every shift, saw exactly where their line stood versus target. Friendly competition did what no management mandate ever had.
What we deployed
- Spindle-level sensors on all 42,000 spindles
- Predictive maintenance model with 87% precision
- Live OEE dashboards on every shop-floor wall
- Count-change SOP automation
9,000 virtual spindles, zero capex
21 OEE points on a 42,000-spindle mill is the throughput of roughly 9,000 additional spindles. At current machine prices, that's ₹45-55 crore of equivalent capex — delivered in 9 months for a fraction of that.
Breakdowns are now predicted, not reacted to. Count changes happen in 1.9 hours on average. And waste has been more than halved. The group has already begun rolling the same stack out to their second unit in Tirupur.
89%
Current OEE (was 68%)
42%
Reduction in unplanned downtime
₹7.8Cr
Annualized incremental revenue
1.4%
Waste yarn rate — down from 3.1%
58%
Faster count changeovers
11 min
Mean time to respond to breakdown
87%
Predictive alerts resolved before failure
₹42L
Saved on spare part inventory
“We went into this thinking we were buying software. We came out having bought a culture change. Every supervisor now owns their line's OEE the way a cricket captain owns a match. The numbers followed.”
Padma Subramanian
COO, Coimbatore Spinning Mills
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