Intermediate Statistical Process Control Two-day Workshop

Continue building a strong foundation in the fundamentals of statistical process control

Course Description

Course Number SPC-002: Intermediate Statistical Process Control: Building the fundamentals of statistical process control

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This 2-day workshop is the second of a two-part series on Statistical Process Control and builds on the concepts from the introductory course on SPC. Participants will leave with the knowledge and skills to apply the required SPC tools to diverse process situations to effectively control and manage them. They will learn about mean (Xbar) charts for variable data and P, nP, C, and U charts for attribute data, as well as moving average (MA), and exponentially weighted moving averages (EWMA) charts. Students will then learn how to conduct studies to assess the capability of a process to meet requirements for attribute data as the number of defects, errors, and the proportion defective or acceptable to a particular process.

Course Topics:

  • How to apply the fundamental SPC concepts required to take processes to the next level.
  • The advantages and disadvantages of Individuals and Moving Range charts and how the use of averages improves the effectiveness of statistical control charts.
  • How to design sampling methods and construct and interpret Means (Xbar) and Range (R) charts, Moving Average (MA) and Exponentially Moving Average (EWMA) charts for variable data.
  • How to distinguish between variable and attribute data – determine the value of each – and how to use the information to both interpret and communicate process behavior.
  • How to select, construct, and interpret attribute control charts — P, nP, U and C and alternatives — for non-conforming process outputs, as well as rate and number the non-conformances.
  • How to conduct and interpret process capability studies for non-conforming process outputs.
  • How to design and conduct effective process control plans as well as continual improvement follow-up.

Day 1: Process Control Charts for Means and Ranges

  • Review of basic stats and statistical process control concepts.
  • The importance of assumptions, on the distributions and patterns of common cause variations, and how to identify them.
  • Benefits of working with averages for process control — the central limit theorem.
  • Control charts for plotting averages (means) and ranges.

Day 2: Choosing the Right Charts and Sampling Procedures, Attribute Charts

  • When to use and not use means charts and solutions.
  • Data types — variable data vs. discrete or attribute data.
  • Control charts for attribute data as well as when and why to use the four types — P, nP for defectives and U, C for defects or errors.
  • Process capability for attribute data.

Course Objectives:

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

What career paths benefit the most from Intermediate Statistical Process Control 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 workshop is comprehensive. The application of SPC is often more challenging that initially meets the eye. The depth of the topics covered in this workshop will prepare you to not only manage the challenge but also look forward to it.

Prerequisites:

A background in basic statistical concepts, as covered in the workshop “Intro to Statistical Methods”, is important to the understanding and application of concepts covered in this workshop.

Credit and Follow-up:

Suggested follow-up workshops include: “Design and Analysis of Experiments for Continual Improvement”, “Failure Modes and Effects (FMEA)”, and “Measurement System Analysis (MSA)”.

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