WHAT IS STATISTICAL PROCESS CONTROL?

Statistical Process Control (SPC) is not new to industry. In 1924, a man at Bell Laboratories developed the control chart and the concept that a process could be in statistical control. His name was William A. Shewart. He eventually published a book titled “Statistical Method from the Viewpoint of Quality Control” (1939). The SPC process gained wide usage during World War II by the military in the munitions and weapons facilities. Statistical process control may be used when a large number of similar items. Every process is subject to variability. The variability present when a process is running well is called the short term or inherent variability. It is usually measured by the standard deviation. Most processes will have a target value. The purpose of statistical process control is to give a signal when the process mean has moved away from the target. A second purpose is to give a signal when item to item variability has increased. In either case appropriate action must then be taken by a machine operator or an engineer. Statistics can only give the signal; the action relies on other skills.

SPC TOOLS

A popular SPC tool is the control chart, originally developed by Walter Shewhart in the early 1920s. A control chart helps one record data and lets you see when an unusual event, such as a very high or low observation compared with “typical” process performance, occurs.

Control charts attempt to distinguish between two types of process variation:

1. Common cause variation, which is intrinsic to the process and will always be present
2. Special cause variation, which stems from external sources and indicates that the process is out of statistical control

SQC VERSUS SPC

Statistical quality control (SQC) is defined as the application of the 14 statistical and analytical tools (7-QC and 7-SUPP) to monitor process outputs (dependent variables). Statistical process control (SPC) is the application of the same 14 tools to control process inputs (independent variables). Although both terms are often used interchangeably, SQC includes acceptance sampling where SPC does not.

THE 7 QUALITY CONTROL (7-QC) TOOLS

In 1974, Dr. Kaoru Ishikawa brought together a collection of process improvement tools in his text Guide to Quality Control. Known around the world as the seven quality control (7-QC) tools, they are:

1. Cause-and-effect diagram(also called Ishikawa diagram or fishbone diagram)
2. Check sheet
3. Control chart
4. Histogram
5. Pareto chart
6. Scatter diagram
7. Stratification

THE 7 SUPPLEMENTAL (7-SUPP) TOOLS

In addition to the basic 7-QC tools, there are also some additional statistical quality tools known as the seven supplemental (7-SUPP) tools:

1. Data stratification
2. Defect maps
3. Events logs
4. Process flowcharts
5. Progress centers
6. Randomization
7. Sample size determination

With real-time SPC you can: 

• Dramatically reduce variability and scrap
• Scientifically improve productivity
• Reduce costs
• Uncover hidden process personalities
• Instantly react to process changes
• Make real-time decisions on the shop floor

Measuring the ROI of a Real-Time SPC Solution

To quantify the return on your SPC investment, start by identifying the main areas of waste and inefficiency at your facility. Common areas of waste include scrap, rework, over inspection, inefficient data collection, incapable machines and/or processes, paper-based quality systems and inefficient lines. You can start to quantify the value of an SPC solution by asking the following questions: 

• Are your quality costs really known?
• Can current data be used to improve your processes, or is it just data for the sake of data?
• Are the right kinds of data being collected in the right areas?
• Are decisions being made based on true data?
• Can you easily determine the cause of quality issues?
• Do you know when to perform preventative maintenance on machines?
• Can you accurately predict yields and output results?

SPC tools

Statistical process control techniques and tools can be employed to monitor process behavior, discover issues in internal systems and develop solutions for production issues. SPC can be applied to any manufacturing or non-manufacturing process in which output that conforms to specifications can be measured. This methodology can be used to determine whether the processes are sufficient to generate products within an acceptable quality level if a project is producing many similar products but not if a project is creating only a small number of customized deliverables.

Control charts

In SPC, process variability is examined, and control charts and other tools can be used to refine a statistical process. Control charts enable users to record data and see the occurrence of an unusual event, such as a very high or low observation. Engineers may use standard deviation equations to streamline or refine results. Those seeking to improve processes that deal with statistical information may examine causes of variations and use logical rules to create algorithms for control.

History of SPC

The concept of statistical process control has a long history. Bell Laboratories’ Walter A. Shewhart, sometimes referred to as the father of statistical quality control, pioneered SPC in the early 1920s to measure variance in production systems. Renowned quality expert W. Edwards Deming, a student of Shewhart, expanded the concept and introduced it to Japanese industry after World War II. Today, organizations around the world have incorporated SPC to improve product quality by reducing process variation. Thanks partly to the propagation of comprehensive quality systems — such as ISO, QS9000, Six Sigma and MSA (Measurement System Analysis) — many companies have been working actively with SPC.