Statistical Quality Control

3 Days | 2.1 CEUs


Statistical process control (SPC) involves using statistical techniques to measure and analyze the variation in processes. Most often used for manufacturing processes, the intent of SPC is to monitor process quality and maintain processes to fixed targets. SPC is used to monitor the consistency of processes used to manufacture a product as designed. It aims to get and keep processes under control. No matter how good or bad the design, SPC can ensure that the product or service is being produced as designed and intended. Thus, SPC will not improve a poorly designed product's reliability, but can be used to maintain the consistency of how the product is made and, therefore, of the manufactured product itself and its as-designed reliability.

A primary tool used for SPC is the control chart, a graphical representation of certain descriptive statistics for specific quantitative measurements of the manufacturing process. These descriptive statistics are displayed in the control chart in comparison to their "in-control" sampling distributions. The comparison detects any unusual variation in the manufacturing process, which could indicate a problem with the process. Several different descriptive statistics can be used in control charts and there are several different types of control charts that can test for different causes, such as how quickly major vs. minor shifts in process means are detected. Control charts are also used with product measurements to analyze process capability and for continuous process improvement efforts.

Acceptance sampling refers to the process of randomly inspecting a certain number of items from a lot or batch in order to decide whether to accept or reject the entire batch. What makes acceptance sampling different from statistical process control is that acceptance sampling is performed either before or after the process, rather than during the process. Acceptance sampling before the process involves sampling materials received from a supplier, such as randomly inspecting crates of fruit that will be used in a restaurant, boxes of glass dishes that will be sold in a department store, or metal castings that will be used in a machine shop. Sampling after the process involves sampling finished items that are to be shipped either to a customer or to a distribution center. Examples include randomly testing a certain number of computers from a batch to make sure they meet operational requirements, and randomly inspecting snowboards to make sure that they are not defective.

What You Will Learn:

  • Calculate basic statistical measures
  • Construct control charts
  • Develop sampling plans
  • Explain process capability 

Course Content

  • Descriptive statistics
    • Location
    • Spread
    • Shape
    • Histograms
  • Variation
    • Common Cause
    • Special Cause
  • Control Charts
    • Average and Range
    • Individual and Moving Range
    • P chart
    • C chart
    • Short run
  • Process Capability
    • Proportion non-conforming
    • Cpk Index
    • Cp Index
  • Acceptance Sampling
    • Risks
    • OC Curves
    • AOQ
    • AOQL
    • ASN
    • ATI
    • ANSI Standard

IISE reserves the right to cancel a class up to 15 business days prior to the scheduled start date

Registration Fee

Member: $1,595 Non-Member: $1,945

Course Schedule

No courses scheduled, contact James Swisher for availability