The objective of the seminar is to provide information that can be used immediately by personnel involved in production operations, and by supervisors and management in decision making. Although the presentation involves use of statistical techniques, presentation of statistical theory will be limited to only what is needed by the attendees to understand and implement processes and testing within the statistical framework.
Presented examples will include an emphasis on the manufacturing processes and quality assurance needs of personnel in the medical device and pharmaceutical industries.
Why You Should Attend:
All processes exhibit intrinsic variation. However, sometimes the variation is excessive and this hinders the ability to achieve reliable measurements and desired results. Statistical process control (SPC) and statistical quality control (SQC) allow us to control the functions of our processes (input) and the quality of our product (output) by providing tangible tools for monitoring and testing.
Process and quality control is important for a company’s reputation. A good system of processing and quality checks reduce costs associated with production waste and re-work due to defects, and allows a company to deliver products that are high in quality. Many industries are also required to have a good quality management system in place to achieve compliance with regulatory authorities.
This seminar will provide attendees with the statistical tools necessary to monitor processes and test the quality of manufactured product. Ms. Eisenbeisz will make use of Minitab software in her presentation.
Areas Covered in the Session:
- FDA QSR, ISO 13485:2016, ISO 9001:2015 regulations and harmonization
- Overview of statistical theory and nomenclature for processes and quality control
- Variability and risk assessment in practice
- Control before testing!
- Which chart for which process?
- Presentation of control charts for discrete and continuous data
- Setting statistical specifications, control limits, and signals
- Attribute and Variables sampling plans
- C = 0/Zero acceptance sampling plans
Who Should Attend :
- Quality Assurance Department Management and Staff
- Quality Control Department Management and Staff
- Research and Development Department Management and Staff
- Manufacturing Department Management and Staff
- Engineering Department Management and Staff
- Operations Department Management and Staff
- Production Department Management and Staff
- Processing Facilities Management
- Process Control Personnel
DAY 1 : (8.30 AM – 4.30 PM)
It’s a System! Elements of Quality Management
- Deming 14 points for total quality management
- Dr. Ishikawa, seven quality control tools (7-QC) and supplementals (7-SUPP)
- Pareto principle (80/20 rule)
- Shewhart (Plan, Do, Study, Act)
Regulatory Requirements in Quality Management
- FDA Quality System Regulation (QSR)
- ISO 13485:2016
- ISO 9001:2015
- Check sheets/tally sheets
- Descriptive statistics
- Binomial Distribution (discrete data)
- Hypergeometric Distribution (sampling without replacement)
- Normal Distribution (continuous data)
Statistical Basics (cont’d)
- Graphical Techniques:
- Pareto charts
- Cause and effect (fishbone) diagrams
- Defect concentration diagrams
Q&A and Wrap-up
DAY 2 : (9 AM – 4.30 PM)
Statistical Process Control: The ABC’s of Control Charts
- Elements of a control chart
- Control Charts for Discrete Data
- c chart
- u chart
- p chart
- np chart
- Control Charts for Continuous Data
- X-bar chart
- R chart
- I chart
- MR chart
- Combined charts (Xbar-R, I-MR)
Statistical Process Control (cont’d)
- More Control Charts
- Classical Shewhart control charts
- Cumulative Sum (CUSUM) charts
- Exponentially Weighted Moving Average (EWMA) charts
- Hotelling (multivariate) control charts
Statistical Quality Control – Attribute Sampling Plans
- C= 0 /Zero Acceptance
- Single sample plan
- Double-sampling plan
- Multiple sampling plan
- Sequential sampling plan
- Skip-lot sampling plan
- Using ANSI Z1.4
Statistical Quality Control – Variables Sampling Plans
- Sampling size and critical distance
- Known vs. unknown standard deviation
- One or two specification limits
- Using ANSI Z1.9
Final Thoughts, Q&A and Wrap-up
Owner, Omega Statistics
Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.
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