Benchmarks
The operational numbers behind unit cost, quality, and delivery, each compared to where apparel factories typically land, so you can see whether it is strong or weak.
The operational numbers behind unit cost, quality, and delivery, measured against apparel benchmarks.
Of every hour you pay a sewing operator, the share that becomes a finished garment good enough to ship.
Availability × Performance × Quality
77%
hours actually at the machine
75%
sewing speed vs a fair standard
90%
made right the first time
TEEP (every machine, staffed or idle)
38% of machines staffed × 52% OLE
OLE counts only the machines that have an operator. Across everything you own, including the idle machines, just a fifth is producing.
Overall Labor Effectiveness
OLEOf every hour you pay a sewing operator, the share that becomes a finished garment good enough to ship.
Sewing efficiency
Of the time your operators spend at their machines, the share that is real sewing at a fair pace, not waiting, reworking, or running slow.
Machine utilization
Of the sewing machines you own, the share actually running and producing at any given moment.
First-pass yield
The share of garments that clear inspection with no rework.
Defects per hundred garments
DHUFaults found per hundred garments. It runs above the plain defect rate because one garment can carry several faults.
On-time delivery
The share of orders that ship by the date promised to the customer.
Lead time, cut to shipped
How long a garment takes from the moment its cloth is cut to the moment it ships.
Line balance
How evenly the work is spread across stations. Low means some operators sit idle while garments pile up at others.
Metric detail
Throughput
live400/day
Good garments finished per day.
112,000 a year ÷ 280 working days
Balanced ceiling
live880/day
today runs at 45% of it
What the floor could ship a day if every step ran at the pace of the fastest, with nothing held back.
The constraint
est.~500/day
pressing caps the line
The slowest step sets the whole floor’s pace. Here it is pressing: two hand irons.
Takt vs cycle
live1.2 min
a garment every ~1.2 min needed
Takt is the pace demand sets: a finished garment needed every so-many minutes. The irons cannot keep that pace.
Work in progress
live~6,800 pcs
~1,000 visible; the rest wait for pressing
Garments part-made and waiting between steps. A 17-day lead time at this pace means far more are stuck in the building than the ~1,000 visible on the sewing floor.
400/day × 17 days in the building
Flow efficiency
est.~0.3%
under 2%
Of all the days a garment sits in the building, the sliver that is actual work. The rest is waiting in piles.
Changeover
est.12 min
Time lost switching a machine from one style to the next. Kept low here by dedicating machines to a product.
First-pass yield
live90%
Share of garments that pass with no rework.
In-line defect rate
live10%
Share of garments caught with a fault on the sewing line.
Defects per million
DPMOlive100,000
The same defect rate on the scale buyers use to score quality. Lower is better; world-class sewing sits near four sigma.
Where faults are caught
est.end-of-line
Almost everything is caught at the end, not in-line, so a fault travels the whole floor before anyone stops it.
Standard minutes
SAMest.~14 min
volume-weighted across the mix
The average sewing work-content per garment, weighted by how many of each you make. Simple bulk uniform pieces run near 12 to 15 minutes and make up most of the count; a dress shirt is ~22, trousers ~30, a bespoke jacket ~100.
Sewing labor per garment
live$1.60
The sewing wage that goes into one garment. It climbs as efficiency falls, so fixing the floor lowers unit cost.
14 min × $0.067/min ÷ 58% efficiency
Output per operator
live~277 min
of 480 paid
Standard minutes each operator earns in a day, out of 480 paid. A like-for-like measure across garment types.
Sewing vs support
est.75%
~1 supervisor per 8 operators
Share of the payroll that is sewing operators rather than supervisors, helpers, pressers, and cutters.
Absenteeism
est.3%
Share of scheduled shifts lost to absence. Low here versus the regional norm, but read as a soft estimate.
Operator attrition
est.20%
15-30%
How fast operators leave and must be replaced each year.