This 6-hour online seminar for Statistical Process Control Training contains a performance of the steps and techniques used to count erraticism in manufacturing methods and to promise excellent products.
The ideas and info presented in this course for Statistical Process Control Training will be most anxious to numerical procedure control: obtaining monitoring information (data) that is impartial, impartial, and beneficial for result making. Stress will be placed on the set-up and use of control charts.
The objective of the seminar for Statistical Process Control Training is to deliver data that can be used directly by the personnel involved in production operations, and by administrators and management in decision-making. Although the presentation involves the use of statistical techniques, the presentation of arithmetical concepts will be imperfect to only what is needed by the attendees to comprehend and device processes and monitoring tools within the statistical framework.
Presented examples will include an emphasis on the manufacturing processes and quality assurance needs of products in the medical device and pharmaceutical industries.
Reasons for You to Attend Statistical Process Control Training Session
All the processes for Statistical Process Control Training show the basic differences. However, sometimes the disparity is unnecessary and this delays the aptitude to attain unswerving capacities and desired results. Numerical process control (SPC) allows us to control the functions of our processes (input) by providing palpable nursing gears.
The control of procedures is important for the reputation of the company. A good and reliable system of processing examines and reduces the costs associated with manufacturing waste and re-work due to flaws, and permits 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 acquiescence with supervisory authorities.
This seminar on Statistical Process Control Training will provide trainees with the numerical tools that are mandatory to monitor procedures to assure the quality of manufactured products. Ms. Eisenbeisz will make use of Minitab software in her presentation.
Course Content
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)
Supervisory Necessities in Quality Management
- FDA Quality System Regulation (QSR)
- ISO 13485:2016
- IS 9001:2015
- Harmonization of regulations with FDA guidance/regulations
Statistical fundamentals
- Descriptive and Graphical Techniques
- Histograms
- Scatterplots
- Pareto charts
- Cause and effect (fishbone) diagrams
- Defect concentration diagrams
Statistical Process Control: The Basics 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
- Traditional Shewhart regulator charts
- Increasing Sum (CUSUM) charts
- Exponentially Weighted Moving Average (EWMA) charts
- Hotelling (multivariate) control charts
Course Provider
Ms Elaine Eisenbeisz,
Owner & Principal ,
Omega StatisticsElaine 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 the 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 Statistics from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology.
