Statistical Process Control (SPC) Training

When effectively applied, Statistical Process Control (SPC) enables better tracking and evaluation of output data, timely identification of process issues, and drives continuous quality improvement. This course presents both the concepts and practical application of SPC techniques, focusing on recognizing out-of-control conditions and taking appropriate actions to restore statistical control. Attendees will learn how to develop and interpret various control charts to enhance their understanding of SPC.

Target Audience

This training is ideal for:

• Quality Managers
• Engineers and Technicians
• Department Managers/Supervisors
• Production personnel responsible for managing or controlling production or administrative processes

Course Duration

The course typically runs for 7.5 hours.

Course Materials

Participants will receive a handout of presentation materials, and a Certification of Attendance will be provided upon completion.

Statistical Process Control Course Outline

Participants will be introduced to the basic concepts of process and product variation, how variation impacts process management and product quality, and traditional statistical methods for monitoring process performance. Key topics include:

• Introduction to SPC Concepts
• Types of Variation
• Frequency Distributions, Histograms, Pareto Charts
  • Instructor-led exercise on frequency distribution/probability analysis (dice roll)
  • Hands-on creation of a Histogram
• Effectiveness of 100% Inspection (individual/group assessments)
• Measures of Central Tendency and Dispersion
• Normal Distribution and the Bell Curve
• Types of Data
• Control Charts for Variables:
  • Xbar and Range Charts
  • Moving Range Charts
• Control Charts for Attributes:
  • p, np, c, and u-charts
  • Hands-on creation of an np chart (Bead Box exercise)
• Interpretation of Control Charts
• Process Capability:
  • Cp and Cpk

Learning Outcomes

By the end of the session, participants will be able to:

• Understand the concepts of probability and variation
• Identify appropriate statistical tools for analyzing variable and attribute data
• Monitor and control processes using statistically valid methods