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Pack of SEVEN: Best Selling Webinars by GlobalCompliancePanel
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Pack of SEVEN: Best Selling Webinars by GlobalCompliancePanel
Product Id
:
30129PACK
Modules:
Understanding Attribute Acceptance Sampling including Z1.4 and c=0 Plans
Statistical Concepts of Process Validation
The FDA Inspection Process: From SOP to 483
Good Documentation Practices for GMP Operations
21 CFR Part 11 - Compliance for Electronic Records and Signatures
Excel Spreadsheets and FDA Device Regulations
Design Inputs - Design Outputs Traceability Matrix - Principles of Lean Documents and Lean Configuration
Instructor :
Dan OLeary
Product Id : 30129PACK
Webinar# 1
Understanding Attribute Acceptance Sampling including Z1.4 and c=0 Plans
Overview
: This course provides the attendees with the tools needed to understand and implement acceptance sampling.
We explain the basis for sampling plans, the binomial distribution, and show how it helps us understand the sampling plan’s performance using the operating characteristic (OC) curve. Participants will gain a solid understanding of how the OC curve is built, how to use it, and how to identify some of the most important points on the curve, including the AQL and RQL points.
The course also provides complete descriptions of three other important curves that help you understand a sampling plan. The average sample number (ASN) helps you predict the number of samples you will take. The average outgoing quality (AOQ) helps you foresee the results if you inspect rejected lots. The average total inspected (ATI) helps you calculate how many items you will inspect including rejected lots. Users of Z1.4 will want to understand how to set up sampling and select parameters such as AQL and Level. The course provides a complete description of Z1.4, showing the process from receiving the lot to selecting the sample size to making the accept/reject decision. We will discuss the following issues:
How to use the sampling tables to determine the sampling plan
Ways to avoid common errors and misunderstandings with the sampling tables
The difference between single, double, and multiple sampling plans
Why double sampling plans are the most economical choice
The reasons for the switching rules between normal, reduced, and tightened
The use of the switching rules to help improve your supplier management program
How the switching rules can help you reduce inspection cost
The c=0 plans are very popular, since they are based on the notion that everything in the sample should pass inspection. The course examines these plans using the curves described above. The OC curve, in these plans, has a different shape that can lead to problems. We will discuss the following issues:
How to use the c=0 plans instead of Z1.4 plans
The basis for the plans using the RQL point
The differences in the OC curves and why they can cause problems
How a change from Z1.4 to c=0 can impact your inventory and disrupt your suppliers
Why should you attend:
Imagine this! Your company uses acceptance sampling in your manufacturing process and your manager asked to make sure it is cost effective. She also knows there is some risk associated with sampling, but she admits she doesn’t completely understand it. You now have a new assignment; assure your manager that you have good balance between risk and cost. The person who set up the system retired a few years ago and isn’t available to help. You have also heard about some new methods called c=0 or zero based acceptance.
How do you know how much your inspection system costs?
Are you inspecting too much, and wasting money?
Are you inspecting too little and incurring risk?
Do your current managers and supervisors understand how the system works?
Will your ISO 9001 registrar ask for justification of these statistical methods?
Should you start to use these c=0 plans you have heard about?
Can you improve the process?
Dan
is the President of Ombu Enterprises, LLC, a company offering training and execution in Operational Excellence, focused on analytic skills and a systems approach to operations management. Dan has more than 30 years experience in quality, operations, and program management in regulated industries including aviation, defense, medical devices, and clinical labs. He has a Masters Degree in Mathematics; is an ASQ certified Biomedical Auditor, Quality Auditor, Quality Engineer, Reliability Engineer, and Six Sigma Black Belt; and is certified by APICS in Resource Management.
Instructor :
Dan OLeary
Product Id : 30129PACK
Webinar# 2
Statistical Concepts of Process Validation
Overview
: The FDA QSR requires device manufacturers to validate processes when the manufacturer cannot "fully verify the output".
The manufacturer must validate these processes with a "high degree of assurance". The presentation explores the statistical underpinnings of these two phrases. To "fully verify the output" relates to the use of statistical sampling plans, while "high degree of precision" relates to process capability. The presentation illustrates the statistical concepts. The Global Harmonization Guidance document on process validation shows the application of statistical techniques. In particular, the guidance explains the use of design experiments (DOE) to establish process parameters and develop challenge points for the Operational Qualification (OQ) phase. The guidance explains the concepts of process capability showing the relationship between the process statistical characterization and the engineering specification transferred from design. Statistical Process Control (SPC) is an important technique in the Performance Qualification (PQ) phase of validation. The presentation concludes by showing the strong relationship between validated processes and Risk Management. ISO 14971:2007 requires that production (and post-production) information go back to Risk Assessment to help complete the life cycle. Validated processes, where the manufacturer cannot fully verify the output, present a risk of product “escape”. Statistical information of each lot from a validated process should be part of the Risk Management File.
Why you should attend
:
If you conduct process validation, you need to ensure that your results are valid. Beyond the regulatory requirements, statistical approaches will help you achieve the desired result - processes that produce only conforming material. This is the essence of the statistical approach. This webinar presents the statistical approach that will help you validate your processes. Your team should attend this webinar if you cannot easily answer these questions.
Can you give the statistical rational for you verification sampling plans?
Can you state the desired and actual process capability you need to achieve?
Can you list the worst-case input parameter combination for your process?
Do you know how to determine challenge points for your process?
Have you set action limits for your process inputs?
Areas Covered In the Seminar:
QMS Requirements for Process Validation
FDA’s QSR (21 CFR §820.75)
ISO 13485:2003
The Statistical Process Model
Relating input to output
The Process Output
Sampling Inspection
Process Capability
The Process Input Parameters
Design of Experiments
The Challenge Points
Risk Management
Production Information
Validated Processes as High Risk
Who will benefit:
People in the following roles can especially benefit from the knowledge in this webinar:
Quality Managers
Quality Engineers
Production Managers
Production Supervisors
Manufacturing Engineers
Production Engineers
Design Engineers
Process Owners