This 6-hour virtual seminar includes a presentation of the steps and techniques used to quantify variability in manufacturing processes, and to assure quality products.
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.
The concepts and information presented will cover statistical process control: obtaining monitoring information (data) that is objective, unbiased, and useful for decision making.
A good system of processing and 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.
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 monitoring tools within the statistical framework.
Process and quality control are constantly evolving. Therefore, historical concepts, current trends and regulatory requirements will be discussed. The presentation of statistical charts and analyses, graphical techniques for planning, trouble-shooting and problem solving will also be presented.
Minitab statistical software will be used to demonstrate data collection, preparation and analysis. Information on how to build and interpret various process control charts for both attributes and variables data will be presented. A handout and dataset will be provided to attendees so they may work hands-on with the information presented in the seminar.
Why should you 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) allows us to control the functions of our processes (input) by providing tangible monitoring tools.
A good system of processing and checks reduces 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 process management system in place to achieve compliance with regulatory authorities.
This seminar will provide attendees with the statistical tools necessary to monitor processes to ensure the quality of manufactured products. Ms. Eisenbeisz will use Minitab software in her presentation.
Who will Benefit:
- Quality assurance (QA) engineers
- Quality control (QC) engineers
- R&D engineers
- Process control personnel
- Manufacturing/Industrial personnel
- Production supervisors
- Management personnel of processing facilities
- Regulatory Affairs Specialists
- Validation Engineers
- Process Improvement Specialists
- Operations Managers
- Lean Six Sigma Practitioners
- Data Analysts in Manufacturing
- Quality Systems Auditors
- Compliance Officers
- Supply Chain Managers
- Production Engineers
- Metrology Technicians
- Technical Writers (for compliance and quality documentation)
- Risk Management Professionals (in manufacturing and quality assurance)
- 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
- Harmonization of regulations with FDA guidance/regulations
- Statistical basics
- Descriptive and Graphical Techniques
- Normal Distribution
- Histograms
- Scatterplots
- Pareto charts
- Cause and effect (fishbone) diagrams
- Defect concentration diagrams
- Descriptive and Graphical Techniques
- 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)
- More Control Charts
- Classical Shewhart control charts
- Cumulative Sum (CUSUM) charts
- Exponentially Weighted Moving Average (EWMA) charts
- Hotelling (multivariate) control charts
Elaine Eisenbeisz
Owner and Principal Statistician, Omega Statistics (since 2006)
Elaine Eisenbeisz is a private practice statistician and the owner of Omega Statistics, a premier statistical consulting firm based in Southern California.
Elaine has a remarkable ability to translate complex statistical concepts into meaningful insights that clients can intuitively understand. She designs and analyzes studies for biotech, pharmaceutical, medical device, and many other industries, collaborating with global companies, start-ups, CROs, and private researchers.
Her love for numbers began in childhood, as she excelled in mathematics competitions at the regional and state levels. She earned a B.S. in Statistics from the University of California, Riverside, as a National Science Foundation scholar, and later obtained a Master’s Certification in Applied Statistics from Texas A&M University. Elaine is a member of the American Statistical Association and a proud member of Mensa High IQ Society. Omega Statistics maintains an A+ rating with the Better Business Bureau.
In addition to her consulting work, Elaine is passionate about statistical education, with a strong emphasis on the application of statistics rather than theory. She frequently presents webinars and workshops on applied statistics, covering diverse range of fields, including clinical research, biotechnology, epidemiology, nutraceuticals, environmental health, quality and process control, and general statistical knowledge and concepts.
Her published work appears in peer-reviewed journals, reflecting her commitment to rigorous statistical practice and practical application.
To learn more about Elaine and her work, visit www.OmegaStatistics.com.
