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Seminar Acceptance Sampling - The Total Picture Seminar

Presented by Compliance Online

Compliance Online Training

This two-day hands-on course provides a clear understanding of the underlying statistics used in acceptance sampling. The course uses exercises to solidify understanding

Course Description/Agenda


By:Dan O'Leary, President at Ombu Enterprises, LLC

How well do you understand sampling plans and their application in your company? Many companies have procedures and program that fall short of the regulatory requirements and good statistical practice. In some cases, FDA may give the firm a 483 or even a Warning Letter. In other cases, the firm may not use the best methods, resulting in waste of resources.

This unique hands-on course will provide attendees with a solid understanding of acceptance sampling methods and their application to medical device manufacturing. The course delivers tools, templates, and insight that will allow participants to implement sampling methods within their firm.

This two-day hands-on course provides a clear understanding of the underlying statistics used in acceptance sampling. The course uses exercises to solidify understanding. As part of the practical implementation, the course explains common standards such as Z1.4 for attribute sampling and Z1.9 for variables sampling. In addition, the course covers other, less well known, methods such as sequential sampling, continuous sampling, and chain sampling.

The course also covers specific applications of sampling including incoming inspection, design validation, design verification, process validation, and quality audits.

Course Outline:

Day One (8:30 AM – 4:30 PM)

Registration Process: 8:30 AM – 9:00 AM

Session Start Time: 9:00 AM

Introduction/Fundamentals – Regulatory Requirements
Statistical techniques in FDA QSR
Statistical techniques in ISO 13485
Validated processes
Process and product monitoring
Design verification and validation
FDA Warning Letters
Introduction/Fundamentals – Statistics
Descriptive Statistics
Graphical Techniques
The binomial distribution
The hypergeometric distribution
The normal distribution
Tests for normality
Sampling Concepts
Understanding Acceptable Quality Level (AQL)
Defining the Operating Characteristic (OC) curve
Risk (producer and consumer) and the Sampling Plan
Attribute Sampling using Z1.4
Setting the sampling parameters (AQL and lot size)
Single, double, or multiple sampling
Acceptance history (normal, reduced, or tightened sampling)
Describing the sampling plan
OC curve
Average sample number (ASN)
Average total inspected (ATI)
Average outgoing quality (AOQ)
Using Accept on Zero (AOZ) plans instead
Attribute Sampling Using the Dodge-Romig System
Average Outgoing Quality Limit (AOQL)
Limiting Quality Level (LQL)
Using the system

Day Two (8:30 AM – 4:30 PM)

Variables Sampling Using Z1.9
The methods (variability known and variability unknown)
Variability unknown methods (range and standard deviation)
Calculating the process parameters – the modern approach
Comparative sample sizes
Combining attributes and variables plans
Sequential Sampling Plans
The sequential probability ratio
Calculating the accept and reject regions
The fan chart as a special case
Continuous Sampling Plans (CSP)
Processes that don’t produce lots
Clearing interval
Sampling fraction
OC curve and Average Outgoing Quality Limit (AOQL)
CSP-1, CSP-2, and CSP-3
Mil-Std-1235
Skip-Lot Sampling Plans (SkSP)
Defining the parameters
The OC curve
An alternate to Z1.4 reduced inspection (ANSI/ASQC S1)
Chain Sampling Plans (ChSP)
Defining the parameters
The OC curve
Applications
Design verification and validation
Process validation
Incoming inspection

 

More Seminar Information

Compliance Online
Compliance Online Training

Course Summary

This two-day hands-on course provides a clear understanding of the underlying statistics used in acceptance sampling. The course uses exercises to solidify understanding

Delivery Method

Seminar Seminar

Who Should Attend

Quality Managers, Quality Engineers, Supplier Quality Engineers, Quality Analysts, Production and Process Engineers, Design and Development Engineers, Verification and Validation Specialists

 
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