Alternatives to AQL sampling plans do exist, but companies need to be aware of them and explore them. Acceptance Quality Limit, or AQL, is applied as a benchmark in most manufacturing organizations to inspect the quality of products they purchase. It is only when the product meets AQL that the receipt is acknowledged and the payment made.
So, what is AQL?
What is AQL? In simple terms, AQL, which expands to Acceptance Quality Limit, is what may be termed as the least or the worst or lowest level of tolerable process means that can be accepted for the quality of product. It is the ratio or percentage level below which quality cannot be lowered to be termed acceptable.
Acceptance Quality Level is accepted as a standard business practice by most medical device companies. Attribute sampling based on ANSI/ASQ Z1.4 and Zero Acceptance Number Sampling Plans by Nicholas L. Squeglia continue to be the most common applications used by companies.
Considering a viable alternative to AQL sampling plans
Although popular, these common methods are not always the best approaches. This is not to belittle the effectiveness of this method, but to just point out that these are in themselves insufficient. Medical devices need to be aware of a variety of methods and when and how to use them.
Establishing “processes needed to demonstrate [product] conformity” is a requirement from ISO 9001 and ISO 13485. Similarly, the FDA’s GMP (21CFR820) requires that “sampling methods are adequate for their use”. Further, an FDA guideline states that “A manufacturer shall be prepared to demonstrate the statistical rationale for any sampling plan used”.
However, an AQL sampling plan does not provide what is needed to meet either of those requirements. Using only Attribute sampling based on ANSI/ASQ Z1.4 and Squeglia’s Zero Acceptance Number Sampling Plans, it is not possible to actually “demonstrate” that an AQL sampling plan ensures product quality.
This is where “Confidence/reliability” calculations come in as alternatives to AQL sampling plans. They are a better way to assess the quality of purchased parts. It is easy to make such calculations using tables and/or an electronic spreadsheet. It is also easy to use confidence/reliability calculations to provide evidence of product quality. The statistical rationale for such calculations is easy to explain and demonstrate, which is why these calculations constitute strong and reliable alternatives to AQL sampling plans.
A learning session on the alternatives to AQL sampling plans
These alternatives to AQL sampling plans will be the core of a learning session that Compliance4All, a leading provider of professional trainings for all the areas of regulatory compliance, will be organizing. John N. Zorich, a senior consultant for the medical device manufacturing industry, will be the Speaker at this webinar, to enroll for which, all that is needed is to visit http://www.compliance4all.com/control/w_product/~product_id=501099LIVE/~sel=LIVE/~John_N.%20Zorich/~Better_Alternatives_to_AQL_Sampling_Plans_for_Risk_Management_in_Incoming_QC
A complete heads-up on the alternatives to AQL sampling plans
At this webinar on the alternatives to AQL sampling plans, the speaker will explain the pros and cons of ANSI Z1.4, and Squeglia’s C=0 in detail. He will highlight the weaknesses of such plans vis-à-vis meeting regulatory requirements. John will offer real-world examples of how using such sampling plans leads to production of non-conforming product to fortify the learning on the alternatives to AQL sampling plans.
He will also examine ISO and FDA regulations and guidelines regarding the use of statistics, especially in regards to Sampling Plans. As part of alternatives to AQL sampling plans, John will explain the advantages of “confidence/reliability” calculations are explained. Such calculations are demonstrated for Attribute data (pass/fail, yes/no data) as well as for variables data (i.e., measurements). If variables data is “Normally distributed”, the calculations are extremely simple. The webinar explains how to handle “non-Normal” data, and provides the methods, formulas, and tools to handle such situations.
The webinar on alternatives to AQL sampling plans ends with a discussion of how one OEM manufacturer has implemented “confidence/reliability” calculations instead of AQL sampling plans for all of its clients. The speaker will offer suggestions for how to use “confidence/reliability” QC specifications instead of “AQL” QC specifications. The use of “reliability plotting” for assessing product reliability during R&D is also discussed.
The speaker will talk on the following topics during this session:
· AQL and LQL sampling plans
· OC Curves
· ANSI Z1.4
· Squeglia’s C=0
· Confidence/Reliability calculations for
o Attribute data
o Normally-distributed variables data
o Non-Normal data
· Transformations to Normality
· Normal Probability Plot
· Reliability Plotting