The concepts and information presented will be mainly concerned with statistical process control: obtaining monitoring information (data) that is objective, unbiased, and useful for decision making. An emphasis will be placed on the set-up and use of control charts.
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.
Presented examples will include an emphasis on the manufacturing processes and quality assurance needs of product in the medical device and pharmaceutical industries.
Process control is 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.
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) allows us to control the functions of our processes (input) by providing tangible monitoring tools.
Process control is important for a company’s reputation. 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 make use of Minitab software in her presentation.
- 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)
Lecture 2: Regulatory Requirements in Quality Management
- FDA Quality System Regulation (QSR)
- ISO 13485:2016
- IS 9001:2015
- Harmonization of regulations with FDA guidance/regulations
Lecture 3: Statistical Basics
- Descriptive and Graphical Techniques
- Pareto charts
- Cause and effect (fishbone) diagrams
- Defect concentration diagrams
Lecture 4: 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 Chart
- Classical Shewhart control charts
- Cumulative Sum (CUSUM) charts
- Exponentially Weighted Moving Average (EWMA) charts
- Hotelling (multivariate) control charts
Start Time: 10 AM EDT
Ms Elaine Eisenbeisz,
Owner & Principal ,
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.
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.
- Quality Assurance (QA) Engineers
- Quality Control (QC) Engineers
- R&D Engineers
- Process Control Personnel
- Manufacturing/Industrial Personnel
- Manufacturing/Industrial Personnel
- Production Supervisors
- Management Personnel of Processing Facilities