Advanced Statistical Tools Training

Background:

This two-day Training is designed to provide participants with an understanding of advanced topics of statistical process control through presentations, illustrations and examples of the analysis of data. Participants learn how to implement SPC within industries that have set up variation, slow and rapid tool wear, and chemical processes where concentration adjustments occur on a daily basis. Emphasis on analysis of the data and effects on the processes using eight types of control charts expands the users’ ability to address non-normal, multivariate, and unilateral variables.

The purpose of this course is to clearly explain all of the basic principles of Statistical Process Control (SPC), teach the student how to implement SPC, and explain how to interpret SPC Control Charts in order to better monitor the production line and diagnose problems in manufacturing. This Training is consistent with the DCX, Ford GM manual Statistical Process Control, 2nd edition.

Duration

2 Days

Content

  •   Introduction – Six Points
  •   Refresher
    •   Prevention Versus Detection
    •   The Fundamentals of Process Control
    •   Understanding Process Behavior
    •   Common and Special Causes
    •   Control vs Capability
    •   Process Control (Behavior) Charts
    •   Charts for Variables Data
    •   Charts for Attribute Data
  •   The Process Improvement Cycle and Process Control
  •   Capability Analysis
  •   Use of Process Measures

Benefits

  •   To present a hands-on approach to learning the principals and practices of advanced SPC and process analysis
  •   Explain optional statistical methods when traditional SPC practices have failed or are inadequate
  •   Understand the uses and benefits of advanced control charts and be able to construct and interpret them
  •   Understand the role that SPC plays in the overall Control Strategy for a process and company

Who should attend?

  •   Individuals who have direct responsibility for defining and developing an Organization’s measuring, monitoring and analytical practices using data collection, charts and statistical tools appropriate for its products, processes and business goals and objectives.