Basic Statistical Process Control
Statistical Process Control (SPC) is a proven and effective technique that helps teams and organizations monitor critical process outputs and drive continuous performance. Learning how to correctly collect process data and then how to properly construct and interpret control charts is required to compete in today’s worldwide marketplace.
The objective of this one‑day course is to introduce the formulas, statistical symbols, terminology and interpretation of basic SPC. This is a good course for those who have never been exposed to control charts before, or have had training in SPC but have not used it for some time. The use and interpretation of control charts are emphasized while formulas and statistical theory are kept to a minimum.
Who Should Attend
Everyone who needs to develop process monitoring methods using data collection, control charts, and data interpretation in order to drive process improvement and meet customers’ increasingly higher demands for top-notch quality. This includes quality managers, quality engineers, manufacturing engineers, product engineers, supervisors, inspectors, maintenance personnel, and machine operators.
Benefits and Learning Outcomes
- Correctly apply control charts to most manufacturing processes
- Accurately interpret these charts to quickly detect any changes in process output
- Stabilize a process to produce consistent parts that satisfy customers
Topics covered in this overview include the following:
- Concept of variation in manufacturing
- Histograms and the normal distribution
- Calculating process parameters; population average and standard deviation
- Calculating subgroup statistics; subgroup average, range, and standard deviation
- Plotting subgroup statistics on a control chart
- Estimating process parameters from subgroup statistics
- The power of control charts and why they’re better than inspection
- Developing control limits for the R chart and the IX & MR chart
- Interpreting charts for stability; points outside of a control limit, runs, trends, and cycles
- Determining rational subgroups, selecting an appropriate subgroup size, finding the suitable time between collecting subgroups, and knowing when to update control limits
- Difference between specification limits and control limits
- The difference between attribute data and variable data
- c, u, np, and p charts for attribute data