Statistical Process Control (SPC) Training in Utah

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Salt Lake City	UT

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About Statistical Process Control (SPC) Training

Who is Statistical Process Control (SPC) Training for?

Statistical Process Control (SPC) is for anybody hoping to retain the desired performance level of an important service, product, or process. Traditionally, its been most-often used by Manufacturing, Procurement, and Quality Professionals. Usually, Inspectors and machine Operators collect data using Gage R&R-validated measurement systems, and plot the data on their control charts for analysis and action by Manufacturing or Quality Engineers. Managers of these functions must also understand relevant terminology, plus how to read and interpret control charts. So, if you are in or want to be in one of those roles, then SPC Training is for you. In our course, Utah students learn:

Statistical Process Control (SPC) Training Course Overview:

Not only can SPC detect changes in supplier processes, of course, it can also detect changes within your own factories. When a process launch is done correctly, Manufacturing Engineers document the process in its approved launch state. Documentation includes an SPC snapshot and calculation of control limits. After new processes are handed-off, local Production teams can refer on-going performance to the launch status control limits thereby knowing if any process inputs have changed resulting in an out of control condition.

		Terminology. Our course begins with a review of SPC-related terminology including clearing up common confusion between capability and control.
		Data Types. We review difference between attribute and variable data plus advantages and disadvantages of each.
		Control Charts. All of the different types of control charts are reviewed including how to choose charts appropriate to the data type. Exercises are conducted allowing Utah students to build several control charts themselves in class. Being the most prevalent in industry, focus is given to Xbar & R Charts.
		Interpretation. Two systems of rules for detecting out of control conditions are presented with reasoning for why one is favored followed by several exercises detecting them.