DHU Formula & Calculation with Examples — Plus Garment Factory Benchmarks 2026
Quick answer. DHU stands for Defects per Hundred Units. The formula is:
DHU = (total defects found ÷ total units inspected) × 100
Worked example (illustrative): a checker finds 46 defects across 800 inspected pieces → DHU = 46 ÷ 800 × 100 = 5.75. DHU counts defects, not defective garments — one garment with three defects adds three to the count — so DHU can exceed 100.
"What's your factory's DHU?" is the second question every buyer asks after "what's your monthly capacity?" The expected answer is a single number. If you say 12, they go quiet and start asking different questions. If you say 4, they ask when you can ship a sample order.
This guide answers the question buyers do not ask but should: what is a reasonable DHU for a CMT factory in 2026, by garment type, by country, by line maturity?
DHU formula and worked example
DHU = (Total defects found ÷ Total units inspected) × 100.
That is the whole calculation. Count every defect your checkers record, divide by the number of garments they actually inspected (not the lot size), and multiply by 100. The result reads as "defects per hundred units" — a DHU of 5.75 means that for every 100 garments checked, inspectors logged roughly 5.75 defects.
Three worked examples (illustrative numbers, matching the chart above):
| Units inspected | Defects found | DHU calculation | DHU |
|---|---|---|---|
| 800 | 46 | 46 ÷ 800 × 100 | 5.75 — Excellent/Good boundary |
| 250 | 35 | 35 ÷ 250 × 100 | 14 — Acceptable |
| 100 | 30 | 30 ÷ 100 × 100 | 30 — Critical |
DHU is not the defect rate
Critical distinction that trips up new QC supervisors: DHU counts defects, not defective pieces. A T-shirt with a broken stitch, a wrong size label, and a fabric hole is three defects in one garment. All three count. Because one unit can carry multiple defects, DHU can exceed 100 — the defect (or rejection) rate never can.
Take the middle row above: a checker inspects 250 garments and finds 20 defective pieces carrying 35 total defects. DHU = 35/250 × 100 = 14. Not 20/250 = 8. Twenty out of 250 is the rejection rate — a separate metric. Report both, but never mix them: a buyer who asks for DHU and gets your defective-piece rate will assume you do not track DHU at all.
What counts as an inspection unit
The "unit" in Defects per Hundred Units is one finished garment presented for inspection — one T-shirt, one trouser, one jacket. It is not a bundle, not a carton, and not a lot. If you inspect at end-of-line, the unit is each garment the checker physically handles; garments that skip the checking table are not in the denominator. A set (e.g. a two-piece pyjama sold together) should be counted the way the buyer counts it on the PO — agree on this before production starts, because switching the unit definition mid-order silently moves your DHU.
The universal benchmark scale
| DHU range | Classification | What it means |
|---|---|---|
| ≤ 5% | EXCELLENT | World-class. Most lots ship straight to brand without rework. You earn buyer preference and repeat orders. |
| ≤ 10% | GOOD | Industry standard for established CMT factories with mature QC. Buyer inspections pass routinely. |
| ≤ 15% | ACCEPTABLE | Common for newer factories, fashion knits, complex garments. Buyer inspections pass but margin is thin after rework. |
| ≤ 25% | POOR | Rework eats your margin. Buyers complain. You are at risk of failed inspections. |
| > 25% | CRITICAL | Lot rejection becomes routine. Margin is negative after sorting and discount penalties. |
These thresholds are what we use in our garment-dhu-calculator npm package and what most published industry references converge on. Some sources use slightly different bands (e.g. ≤7% Excellent, ≤12% Good) but the structure is the same.
DHU by garment category
"Good DHU" depends heavily on what you are making. A 10% DHU on a complex tailored jacket is excellent. A 10% DHU on a basic T-shirt is unacceptable. Approximate 2026 benchmarks from CMT factories we cross-checked with:
| Garment type | Excellent | Good | Acceptable |
|---|---|---|---|
| Basic T-shirt (cotton, no print) | ≤ 3% | ≤ 6% | ≤ 10% |
| Printed T-shirt / polo | ≤ 5% | ≤ 8% | ≤ 12% |
| Hoodie / sweatshirt | ≤ 6% | ≤ 10% | ≤ 14% |
| Formal shirt (woven, button-front) | ≤ 5% | ≤ 9% | ≤ 13% |
| Trouser (5-pocket denim) | ≤ 8% | ≤ 13% | ≤ 18% |
| Tailored jacket / blazer | ≤ 10% | ≤ 16% | ≤ 22% |
| Outerwear (3-layer technical) | ≤ 12% | ≤ 20% | ≤ 28% |
| Lingerie / swimwear | ≤ 6% | ≤ 10% | ≤ 15% |
| Activewear (performance knit) | ≤ 5% | ≤ 9% | ≤ 14% |
| Children's wear | ≤ 4% | ≤ 7% | ≤ 11% |
The pattern: more operations = higher DHU. A jacket has 60+ operations vs a T-shirt's 8-12 operations. If each operation has a 0.5% defect rate, the cumulative DHU is much higher on the jacket. This is math, not a quality problem — but it means you cannot directly compare DHU between garment types.
DHU by country / region
Country averages are very rough because factory-to-factory variation within any country is enormous. A top Bangladesh factory beats a poor Sri Lanka factory easily. But for orientation:
| Country / region | Typical DHU range (basic knit) | Notes |
|---|---|---|
| Vietnam | 4–8% | Strong process discipline, mature workforce |
| Bangladesh (RMG, top tier) | 5–9% | Compliance-driven, well-trained at top factories |
| Bangladesh (mid-tier) | 10–15% | Most volume; variable QC investment |
| India (Tirupur knit) | 6–10% | Strong knit cluster, lower for tees, higher for fashion |
| India (Delhi/Noida woven) | 10–18% | Complex garments, more variation |
| Cambodia | 8–14% | Skilled but workforce turnover affects consistency |
| Nepal | 10–15% | Younger industry, growing |
| Sri Lanka | 4–8% | Premium positioning, strong process |
| Ethiopia | 15–25% | New workforce, training-curve heavy |
| China (mid-grade) | 3–7% | Strong process culture, declining workforce |
These are not official numbers — they are based on our cross-checks with buyer QA teams and other factory owners over the last two years. Treat as orientation, not gospel.
DHU by line maturity
The single biggest variable inside one factory is line maturity. A line that has been running the same style for three months produces dramatically lower DHU than a line that just started a new style yesterday.
| Line stage | DHU multiplier vs steady state |
|---|---|
| Day 1 of new style | 3–5× higher than steady state |
| Week 1 of new style | 2–3× higher |
| Week 2-3 of new style | 1.5× higher |
| Week 4+ (steady state) | Baseline |
This is why changeover frequency matters more than absolute DHU. A factory with 10 styles per month and 12% average DHU is probably running better operations than a factory with 2 styles per month at 8% DHU — because the first factory's steady-state DHU after the learning curve is likely 4-5%, while the second's is 6-7%.
If you are a buyer evaluating a factory, ask for DHU broken out by week-of-style. If a factory does not track this, that itself tells you something about their QC maturity.
What drives DHU — in order of impact
From our own data (see the full DHU reduction case study):
1. Machine condition (single biggest factor)
Of our 35 machines, 14 had issues we did not see until we audited: bad timing, dull needles, oil leaks. After fixing all 14, our skip-stitch rate dropped 60% and puckering dropped 45%. Machine maintenance is the highest-ROI DHU intervention.
2. Operator skill matrix
Putting an overlock-skilled operator on a single-needle machine produces 3-4× the DHU of someone trained on that machine. Skill matrix discipline is invisible until you measure it.
3. Pre-production sample issues that did not get fixed
If the sample had a measurement issue and you "fixed it in production", that issue is going to show up at scale. Every PP sample defect is a future DHU contribution.
4. Fabric inspection gate
Fabric defects (slubs, weaving issues, shading) account for 15-25% of our DHU. Investing in a 4-point fabric inspection system (separate post — guide here) catches these before they hit the line.
5. Inspector calibration
Two inspectors looking at the same 100 garments will find different DHUs unless you calibrate them. We run a monthly calibration where 5 inspectors check the same lot — the spread reveals whose count to trust.
How to collect DHU without a data-entry clerk
The reason most factories cannot answer "what was DHU on line 2 last week?" is not the formula — it is that defect counts live on paper tally sheets that someone has to type up later, and nobody does. The fix is to make checking itself a scan point: the checker scans the bundle QR, taps the defect type (or "pass"), and the defect count lands in the database the moment it happens. Numerator and denominator both accumulate automatically — units inspected from the scans, defects from the taps — so DHU is live, per line, per style, per operator, with zero end-of-day data entry. We covered how to set this up in building an apparel quality dashboard and what to look for in garment QC software.
Free online DHU calculator
The DHU calculator on our tools page runs in your browser — enter defects + garments inspected, get DHU + classification + benchmark badge instantly.
For factory ERP integration, we open-sourced the math:
- npm:
npm install garment-dhu-calculator - PyPI:
pip install garment-dhu-calculator
Both packages support breakdown by defect type, multi-checker daily aggregation, and benchmark classification. MIT licensed, 27 inline tests cross-verified against OnlineClothingStudy's worked example.
FAQ
What is the DHU formula?
DHU = (total defects found ÷ total units inspected) × 100. Illustrative example: 46 defects found across 800 inspected pieces gives DHU = 46 ÷ 800 × 100 = 5.75. Count every defect, not every defective garment — one garment with three defects contributes three to the count.
What is the full form of DHU in garments?
DHU stands for Defects per Hundred Units. It expresses how many defects inspectors record for every 100 garments checked. Because a single garment can carry more than one defect, DHU can exceed 100, unlike the defective-piece (rejection) rate.