If you’ve spent any time manufacturing you’ve probably conducted a quality inspection on your order. And, in the process you most likely use AQL standards to determine the defect limit you can accept for your order.

We’ve covered AQL extensively here and here. But for the sake of understanding this article more clearly you can see the definition of AQL below from the Insight Quality glossary.

Acceptance quality limit (AQL): In a continuing series of lots, a quality level that, for the purpose of sampling inspection, is the limit of a satisfactory process average.

Put simply, every order is going to have defects that are allowed, how many is too many – what is acceptable to you?

For the purpose of determining whether an order’s quality meets your requirements, the use of  AQL for inspections is the most effective. Certainly, if you’re just getting into good quality practices you’ll want to start with AQL inspections and ensuring your orders have a passing result.

Once you have established a history of AQL inspections and achieving consistently passed inspections then the next step is understanding the actual quality level of each order AND your suppliers ability to provide consistent high quality with the least amount of defects.

There is a chance that some of your current suppliers are passing inspections, within AQL, with flying colors but delivering a higher percentage of defects than a comparative supplier.

The good news is there’s a method that the garment industry has been using for a while but hasn’t been widely used in other industries.

But it should!

What’s this method?


It’s called OQL.

Observed Quality Level


Observed Quality Level is the percentage of defects that are actually found during an inspection.

In a standard AQL application the quality inspector thinks in terms of PASS/FAIL.

Did the inspection pass?

If yes then the lot is acceptable and it should be shipped to the consumer.

However, not all PASS results were created equal.

Some lots will pass with a larger percentage of defects than the same lot size from a comparable supplier.

But AQL doesn’t account for this. It’s function is only to determine PASS/FAIL based on the amount of defects that have been deemed acceptable.

With OQL all of the defects are used and it becomes clear which suppliers are serving up passing AQL marks but still delivering higher percentages of defects.

Let’s look at an example.


Two factories can have a 100% pass rate but different levels of quality.

Assuming only 1 inspection


Order size is 1000 pieces of one style.  

General Level 2

AQL Major is 2.5

AQL Minor is 4.0

Sample Size will be 80


Allowed Majors is 5, Reject if 6

Allowed Minors is 7, Reject if 8

(You can double check these numbers on our AQL chart found here.)

The formula for determining OQL is OQL = DEFECTS ÷ SAMPLE SIZE

With this in mind the results for two hypothetical factories could differ substantially.

Factory 1: 4 majors, 7 minors = OQLs of Major: 4/80 or .05%; Minor 7/80 =.0875%

Factory 2: 0 majors, 2 minors = OQLs of Major: 0/80 or 0%; Minor 2/80= .025

This means that even with a “PASSED” result Factory 1 could expect 50 major defects and 87.5 (88) minor defects in their 1000 piece order.

Remember AQL = Acceptable Quality Limit. As these are consumer goods, there is going to be a level of defects that buyer must deem acceptable – this level of defects wouldn’t be acceptable in a higher risk/critical component. (Think airbags in cars.)

Factory 2 would expect 0 majors and 25 minors (of course this is a sampling defect so possible there are going to be some majors found in a 1000 piece order even though none were found during an AQL inspection).

Factory 1 would have 50 more major defects than Factory 2 and 58 more minor defects.

Yet, they would both have a “PASSED” result.

Why it’s important for you to start using OQL


It makes sense to look at other metrics/data beyond just pass/fail rates.

If you already have an established inspection program, using OQL data can allow for an increased ROI on quality spend by reducing the number of inspections at factories with low OQLs. (Factory 2 in our example)

Simultaneously you would increase the number of inspections at factories with high OQLs / high pass rate (or low failure). (Factory 1 in our example above)

Increasing inspections at factories with high OQL and reducing inspections by the same amount at factories with low OQLs will keep the total spend the same but allow you to increase the amount of issues “caught” during inspection.

In the long run this approach will help you to spend your quality inspection dollars at factories with lower quality.

Putting the money to use where it’s most needed.

OQL is the Smart Choice.


In summary, using OQL data gives you the opportunity for a more COMPLETE picture. You can put your quality dollars to good use by making sure you’re spending where it makes the most sense and yields a greater return on your investment in conducting inspections.


Let me help you


Go over to this LinkedIn post and share how you use your quality data. And ask any questions about how to assess the data for your purposes.

I’m answering every question and responding to every comment on the post.

Let me help you out.

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