7FM Statistical Process Control (SPC) and Process Capability with Minitab
Training Lenth - 2 Days

Statistical Process Control is a passive designed experiment.  Potential causes are determined before the study.  Event based sampling of these causes produce sampling triggers.  The operator, inspector, or quality engineer samples the process and adds a new point onto the control chart.  If the process goes out of control, the cause is known.  When a process goes out of control, has gone through a statistically significant change.  Event based sampling is based upon 7FM techniques and include tool setup, new material, new lot of parts, tool temperature, and so on.

If an event has not occurred and the process shifts or variation changes, the process has experienced internal degradation.  

All process degradations are identified through the 7FM PFMEA before SPC is carried out.  All events that can cause a shift in the process average or its variation are identified by 7FM PFMEA.

SPC Overview

  • Variation over time of a process
  • Shewhart’s Control Charts
  • Selecting an Appropriate Control Chart

Sampling the Process

  • The Rational Sampling
  • Sample Size Considerations
  • Sampling Frequency
  • Common Sampling Problems

Assessing Process Variation

  • Understanding Common and Special Causes
  • Understanding Process Data
  • Shape, Location, and Spread
  • Measures of Central Tendency and Variability
  • The Central Limit Theorem

Common Charts

  • XBar-R Chart
  • X and Rm Charts
  • Median and R Charts
  • Attribute Charts (p, np, c, and u)

Building and evaluating each chart

  • Assessing the Control Chart for Stability
  • Understanding Basic Statistical Probability
  • Assessing Statistical Changes (Out-of-Control)
  • Basic Rules for Out-of-Control Conditions

Specialty Charts

Trend Charts

  • Calculating the Start/Stop points
  • Assessing Capability
  • CUSUM Charts
  • EWMA Charts

7 FM PFMEA and Process Control Plan

Process Capability Assessment

  • The Normal Distribution Process
  • The Non-normal Distribution Process