Introduction to Statistical Process Control

Gain a strong foundation in the fundamentals of statistical process control

This two-day workshop is the first of a two-part series on Statistical Process Control (SPC) that grounds students in the fundamentals of statistical process control (SPC). This series of two courses prepares students for success and for realizing the full potential of SPC. Participants will leave the workshop with an intuitive understanding of fundamental concepts of SPC and control chart theory, as well as a practical knowledge of the mechanics of selecting control chart types, designing effective and efficient sampling methods, and how to apply them to improving processes and products. Students will also understand how to use the same fundamental approach to manufacturing and non-manufacturing processes. SPC provides the foundation for solving and sustaining process improvement.

Course Topics

  • The fundamental SPC concepts of common and special cause variation, how to recognize them, and how to use a control chart to alert you when something is affecting the process.
  • Quantitatively and graphically characterize, describe, and assess their process performance with using histograms, box and whisker plots, control charts, and capability analysis.
  • Plan, construct, interpret, and follow-up on the application of Individuals (Xi) and Moving Range (MR) charts and process capability Cpk analysis.
  • Exploit SPC methodology to assure the effectiveness of day to day operations, project management, and improvement methods such as Lean and Six Sigma.
  • The importance and applicability of the topics in the course that follows.
Day 1

Basic Statistics for Control Charts

  • The meaning and significance of common, special, and assignable cause variation and how their analysis helps us know when to act and when not to act.
  • Histograms, Box and Whisker plots, distributions, the measures of a distribution – parameters and statistics.
  • The normal distribution and why it is so important to us.
  • Using what we know about the normal distribution to signal action is required.
  • Verifying if a distribution is normal.
  • Plotting data in order of occurrence and then spotting possible trends or changes – run charts.

Process Capability & Performance & Sustaining Process Improvement

  • Turning a run chart into a statistical control chart and the advantages — individual and range control charts are used to determine whether or not a process change has occurred, and when to intervene.
  • Process Capability — assessing the capability of a process to meet requirements.
  • Process Performance – assessing how a process has performed.
  • Developing effective process management and control plans.

Course Objectives

Upon completion of this three-day course, participants will leave with a strong foundation in the fundamentals of statistical process control.

What career paths benefit the most from Design and Analysis of Experiments for Continual Improvement of Processes and Products Workshop?

  • People working with manufacturing or non-manufacturing processes.
  • Manufacturing, process and quality managers, and engineers.
  • Product and process development and design engineers.
  • Continual improvement and process excellence program managers.
  • Participants in process and quality improvement teams.
  • SPC coordinators.
  • Six Sigma practitioners.

Additional Course Notes

This is an introductory course in SPC however, the core methodologies that the participants learn will go a long way to ensuring their ability to significantly up their ability to effectively analyze and improve processes.


There are no prerequisites for this workshop. However, participants will maximize the value of their immersion in the workshop if they are able to arrive at the workshop with data from their own processes, which will significantly enhance their recognition of the relevance of the methodologies.

Credit & Follow-Up

“Intermediate Statistical Process Control”, “Design and Analysis of Experiments for Continual Improvement”, “Failure Modes and Effects (FMEA)”, and “Measurement System Analysis (MSA)”.