# Statistical Process Control

## Mechanical Skills

### Course 1 – Introduction to Statistical Process Control

**Prerequisites**: This lesson is designed so that no prior knowledge is required. However, a knowledge of basic mathematical skills is recommended.

**Description**: This lesson introduces statistical process control (SPC) as a prevention method used to reduce quality costs. The lesson explains variability and shows how to construct a histogram to represent it. The normal curve is described as well as its probability and relevance to the philosophy of never-ending improvement.

**Objectives**:

- Explain the difference between prevention and detection systems
- Define statistical process control and quality as it relates to SPC
- Describe the various classifications of quality costs and discuss their relevance to SPC
- Describe the concept of variation
- Generate frequency charts and histograms
- Apply basic SPC terminology such as variable, attribute, class width, and frequency
- Describe what is meant by probability
- Show how normal curves can represent measured quantities

### Course 2 – Introduction to Control Charts

**Prerequisites**: This lesson is designed for participants familiar with the basics of statistical process control. A knowledge of basic mathematical skills is recommended.

**Description**: This lesson introduces control charts and shows how to plot specific values on the control chart. The lesson also demonstrates how to determine and plot the mean, median, and range on a control chart.

**Objectives**:

- Explain the use of common elements of a control chart
- Explain the two ways in which a process curve can change and describe the measures that are used to monitor those changes
- Calculate and plot the mean using values with decimal points, both positive and negative numbers, and a scale marked either with actual values or increments above and below zero
- Determine and plot the median
- Calculate and plot the range using values with decimal points, and positive and negative numbers
- Explain the difference between common and special causes of variations

### Course 3 – Control Charts for Variables

**Prerequisites**: This lesson is designed for participants familiar with the principles of statistical process control, the basic components of control charts, and the characteristics of a normal curve. A knowledge of basic mathematical skills is recommended.

**Description**: This lesson explains how to interpret variable control charts in order to determine whether or not a process is in statistical control. The lesson introduces the concept of performance-based limits for process control and presents basic guidelines for proper sampling. In addition, the principles for interpreting control charts are presented.

**Objectives**:

- Identify variables
- Explain how the control limits for a process are related to a normal curve
- Explain how the central line may be identified for mean, median, and range charts
- Identify the criteria of good sampling practice
- Identify a random pattern of values using the 2/3 rule
- Identify three different types of non-random patterns and interpret their meaning
- Explain when a non-random pattern may be a sign of process improvement

### Course 4 – Control Charts for Attributes

**Prerequisites**: This lesson is designed for participants who are familiar with the basics of statistical process control, the basic components of control charts, the characteristics of a normal curve, and control charts for variables. A knowledge of basic mathematical skills is recommended.

**Description**: This lesson discusses principles of attribute control charts and shows how to plot and interpret various control charts for attributes, including p, np, u, c, and multiple characteristic charts.

**Objectives**:

- Identify nonconformities
- Recognize the different sections of an attribute control chart and explain their purpose
- Describe the information that is contained in each section of an attribute control chart
- Describe proper sampling procedures for attributes using assigned data
- Complete p, np, u, c, and characteristic charts
- Interpret an attribute control chart for statistical control
- Identify three different non-random patterns and interpret their meaning

### Course 5 – Machine and Process Capability Studies

**Prerequisites**: This lesson is designed for participants familiar with the principles of statistical process control, the function and components of a control chart, and procedures for collecting sample process data. An understanding of the concepts of control limits, specification limits, standard deviation, and the characteristics of a normal curve is also required. A knowledge of basic mathematical skills is recommended.

**Description**: This lesson explains the purpose and procedures for performing machine and process capability studies. The lesson shows how to complete and interpret machine capability charts and how to perform a process capability study as well as how to calculate a capability index for a machine and a process.

**Objectives**:

- Able to define capability for a machine and a process
- Explain the purpose of machine and process capability studies
- Perform a machine capability study using a capability chart
- Interpret a machine capability graph using the specification limits as guidelines
- Perform a process capability study
- Calculate two different process capability indices for machines and processes

### Course 6 – Problem Solving Techniques

**Prerequisites**: This lesson is designed for participants familiar with the principles of statistical process control as well as the interpretation and extraction of data from tally charts, plant records, and control charts. Basic mathematical skills and graphing techniques are also required.

**Description**: This lesson covers several problem solving techniques including brainstorming, Pareto diagrams, cause and effect diagrams, and scatter diagrams. The lesson focuses on how to collect data and display it graphically as well as how to apply graphic tools to problem solving.

**Objectives**:

- Construct a Pareto diagram from data provided by tally charts, plant records, and control charts
- Define the Pareto principle of 80/20 rule
- Calculate the cumulative frequencies and percentages of defect categories and plot them on a Pareto diagram
- Construct a cause and effect diagram
- Extend a fishbone diagram through logical backward analysis of a cause
- Distinguish between dependent and independent variables
- Construct a scatter diagram as well as interpret it for relationships between variables