Statistical process control

Statistical process control
Statistical process control (SPC) is an effective method of monitoring a process through the use of control charts. Control charts enable the use of objective criteria for distinguishing background variation from events of significance based on statistical techniques. Much of its power lies in the ability to monitor both process center and its variation about that center, by collecting data from samples at various points within the process. Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over quality methods, such as inspection, that apply resources to detecting and correcting problems in the end product or service.
In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end. This is partially due to a diminished likelihood that the final product will have to be reworked, but it may also result from using SPC data to identify bottlenecks, wait times, and other sources of delays within the process. Process cycle time reductions coupled with improvements in yield have made SPC a valuable tool from both a cost reduction and a customer satisfaction standpoint.

History

Statistical process control was pioneered by Walter A. Shewhart in the early 1920s. W. Edwards Deming later applied SPC methods in the United States during World War II, thereby successfully improving quality in the manufacture of munitions and other strategically important products. Deming was also instrumental in introducing SPC methods to Japanese industry after the war had ended.
Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. While Dr. Shewhart drew from pure mathematical statistical theories, he understood that data from physical processes seldom produces a "normal distribution curve" (a Gaussian distribution, also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing data did not always behave the same way as data in nature (for example, Brownian motion of particles). Dr. Shewhart concluded that while every process displays variation, some processes display controlled variation that is natural to the process (common causes of variation), while others display uncontrolled variation that is not present in the process causal system at all times (special causes of variation).
In 1989, the Software Engineering Institute introduced the notion that SPC can be usefully applied to non-manufacturing processes, such as software engineering processes, in the Capability Maturity Model (CMM). This idea exists today within the Level 4 and Level 5 practices of the Capability Maturity Model Integrated (CMMI). This notion that SPC is a useful tool when applied to non-repetitive, knowledge-intensive processes such as engineering processes has encountered much skepticism, and remains controversial today.

General

The following description relates to manufacturing rather than to the service industry, although the principles of SPC can be successfully applied to either. For a description and example of how SPC applies to a service environment, refer to Roberts (2005).^[7] SPC has also been successfully applied to detecting changes in organizational behavior with Social Network Change DetectionMcCulloh (2007). Selden describes how to use SPC in the fields of sales, marketing, and customer service, using Deming's famous Red Bead Experiment as an easy to follow demonstration introduced by.
In mass-manufacturing, the quality of the finished article was traditionally achieved through post-manufacturing inspection of the product; accepting or rejecting each article (or samples from a production lot) based on how well it met its design specifications. In contrast, Statistical Process Control uses statistical tools to observe the performance of the production process in order to predict significant deviations that may later result in rejected product.
Two kinds of variation occur in all manufacturing processes: both these types of process variation cause subsequent variation in the final product. The first is known as natural or common cause variation and may be variation in temperature, properties of raw materials, strength of an electrical current etc. This variation is small, the observed values generally being quite close to the average value. The pattern of variation will be similar to those found in nature, and the distribution forms the bell-shaped normal distribution curve. The second kind of variation is known as special cause variation, and happens less frequently than the first.
For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of product, but some boxes will have slightly more than 500 grams, and some will have slightly less, in accordance with a distribution of net weights. If the production process, its inputs, or its environment changes (for example, the machines doing the manufacture begin to wear) this distribution can change. For example, as its cams and pulleys wear out, the cereal filling machine may start putting more cereal into each box than specified. If this change is allowed to continue unchecked, more and more product will be produced that fall outside the tolerances of the manufacturer or consumer, resulting in waste. While in this case, the waste is in the form of "free" product for the consumer, typically waste consists of rework or scrap.
By observing at the right time what happened in the process that led to a change, the quality engineer or any member of the team responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem.
SPC indicates when an action should be taken in a process, but it also indicates when NO action should be taken. An example is a person who would like to maintain a constant body weight and takes weight measurements weekly. A person who does not understand SPC concepts might start dieting every time his or her weight increased, or eat more every time his or her weight decreased. This type of action could be harmful and possibly generate even more variation in body weight. SPC would account for normal weight variation and better indicate when the person is in fact gaining or losing weight.

How to use SPC

Initially, one starts with an amount of data from a manufacturing process with a specific metric, i.e. mass, length, surface energy...of a widget. One example may be a manufacturing process of a nanoparticle type and two parameters are key to the process; particle mean-diameter and surface area. So, with the exiting data one would calculate the sample mean and sample standard deviation. The upper control limits of the process would be set to mean plus three standard deviations and the lower control limit would be set to mean minus three standard deviations. The action taken depends on statistic and where each run lands on the SPC chart in order to control but not tamper with the process. The criticalness of the process can be defined by the Westinghouse rules used.
The only way to reduce natural variation is through improvement to the process technology, see Nelson funnel experiment.

References

1. Deming, W. Edwards, Lectures on statistical control of quality., Nippon Kagaku Gijutsu Remmei, 1950
2. Deming, W. Edwards and Dowd, John S. (translator), Lecture to Japanese Management, Deming Electronic Network Web Site, 1950 (from a Japanese transcript of a lecture by Deming to “80% of Japanese top management” given at the Hotel de Yama at Mr. Hakone in August of 1950)
3. "Why SPC?" British Deming Association SPC Press, Inc. 1992
4. Bob Raczynski and Bill Curtis (2008) Software Data Violate SPC’s Underlying Assumptions, IEEE Software, May/June 2008, Vol. 25, No. 3, pp. 49-51
5. Bob Raczynski (2009) Is Statistical Process Control Applicable to Software Development Processes?, StickyMinds.com, Jan 17 2009
6. Robert V. Binder (1997) Can a Manufacturing Quality Model Work for Software?, IEEE Software, September/October 1997, pp. 101-105
7. Roberts, Lon (2005). SPC for Right-Brain Thinkers: Process Control for Non-Statisticians. Quality Press. Milwaukee
8. Paul H. Selden (1997). Sales Process Engineering: A Personal Workshop. Milwaukee, WI: ASQ Quality Press