Statistical process control
Statistical process control (SPC) is the use of statistical methods in the monitoring and control of a process, by repeatedly sampling measurements, or counts, to predict results. Under SPC, a process behaves predictably to produce results with the least possible waste. Once a process has become stable and responds as predicted, then it is said to be in a state of "statistical control". While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process which has measurable factors. Key tools in SPC are control charts, a focus on continuous improvement and designed experiments.
Much of the power of SPC lies in the ability to examine a process, for the sources of variation in that process, by using tools which give weight to objective analysis over subjective opinions and which allow the strength of each source to be determined numerically. Variations in the process, which might 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 other quality methods, such as inspection, which apply resources to detecting and correcting problems after they have occurred.
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 reduced 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.
Statistical process control was pioneered by Walter A. Shewhart in the early 1920s. W. Edwards Deming later applied SPC methods in the U.S. 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 how the 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 Integration (CMMI). However, 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. The problem lies in numerous areas of software which are not repetitive, but instead are one-time or one-off aspects of quality, rather than being observed for repeated performance over a long-term view. In those cases, having a test-first method of built-in performance tests can help detect problems in one-time sections of software coding. The overall result is that software can be developed with hundreds, or thousands, of built-in performance tests, in the theory that if any anomaly arises, or errors are made, then problems can be pinpointed by the numerous built-in tests, before causing an expensive problem only detected later in the software system.
An analogy, of built-in tests, would be having a car with thousands of electronic-sensors attached to each part of the car. Hence, if a rock in the road, or a vandal, were to damage part of the car, then the sensors would report unusual levels in various parts, such as spark plug wire #5 losing charge, a weakness in an axle, or low pressure in a brake line, before a tire falls off, or the brakes fail. The reason such extensive software tests are cost-effective is because software components can be written quickly and might be used by thousands or millions of people, analogous to a car which seated a million passengers as a mega-risk if problems are not detected early. Of course, with thousands of built-in tests, the problem becomes trying to judge which unusual test results indicate a significant problem, rather than just being many "noise-level" anomalies. Hence, more computer software is needed to help prioritize the warning issues within various components of the system, and determine the overall severity, analogous to warning, "Stop the car now" or "Check parts A/B/C at next monthly servicing". Because computers can run continuously, they can even record the times at which anomalies occur, such as the exact times when someone let the air out of 2 car tires.
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). 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.
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 consists of the variation inherent in the process as it is designed. Common cause variation may include variations in temperature, properties of raw materials, strength of an electrical current etc. The second kind of variation is known as special cause variation, or assignable-cause variation, and happens less frequently than the first. With sufficient investigation, a specific cause, such as abnormal raw material or incorrect set-up parameters, can be found for special cause variations.
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 [change]
Statistical Process Control may be broadly broken down into three sets of activities: understanding the process; understanding the causes of variation; and elimination of the sources of special cause variation.
In understanding a process, the process is typically mapped out and the process is monitored using control charts. Control charts are used to identify variation that may be due to special causes, and to free the user from concern over variation due to common causes. This is a continuous, ongoing activity. When a process is stable and does not trigger any of the detection rules for a control chart, a process-capability analysis may also be performed to predict the ability of the current process to produce conforming (i.e. within specification) product in the future.
When excessive variation is identified by the control-chart detection rules, or the process capability is found lacking, additional effort is exerted to determine causes of that variance. The tools used include Ishikawa diagrams, designed experiments and Pareto charts. Designed experiments are critical to this phase of SPC, as they are the only means of objectively quantifying the relative importance of the many potential causes of variation.
Once the causes of variation have been quantified, effort is spent in eliminating those causes that are both statistically and practically significant (i.e. a cause that has only a small but statistically significant effect may not be considered cost-effective to fix; however, a cause that is not statistically significant can never be considered practically significant). Generally, this includes development of standard work, error-proofing and training. Additional process changes may be required to reduce variation or align the process with the desired target, especially if there is a problem with process capability.
Related pages [change]
- Deming, W. Edwards, Lectures on statistical control of quality., Nippon Kagaku Gijutsu Remmei, 1950.
- 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).
- "Why SPC?", British Deming Association, SPC Press, Inc. 1992.
- 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.
- Robert V. Binder (1997). "Can a Manufacturing Quality Model Work for Software?", IEEE Software, September/October 1997, pp. 101-105.
- Roberts, Lon (2005). SPC for Right-Brain Thinkers: Process Control for Non-Statisticians. Quality Press, Milwaukee. ISBN 0873896637
- Paul H. Selden (1997). Sales Process Engineering: A Personal Workshop. Milwaukee, WI: ASQ Quality Press. ISBN 0873894189.
- Deming, W E (1975) On probability as a basis for action, The American Statistician, 29(4), pp146–152
- Deming, W E (1982) Out of the Crisis: Quality, Productivity and Competitive Position ISBN 0-521-30553-5
- Oakland, J (2002) Statistical Process Control ISBN 0-7506-5766-9
- Shewhart, W A (1931) Economic Control of Quality of Manufactured Product ISBN 0-87389-076-0
- Shewhart, W A (1939) Statistical Method from the Viewpoint of Quality Control ISBN 0-486-65232-7
- Wheeler, D J (2000) Normality and the Process-Behaviour Chart ISBN 0-945320-56-6
- Wheeler, D J & Chambers, D S (1992) Understanding Statistical Process Control ISBN 0-945320-13-2
- Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0-945320-53-1.
- Wise, Stephen A. & Fair, Douglas C (1998). Innovative Control Charting: Practical SPC Solutions for Today's Manufacturing Environment. ASQ Quality Press. ISBN 0-87389-385-9
Other websites [change]
- Note: Before adding your company's link, please read WP:Spam#External_link_spamming.
- Working example of Deming's Red Bead Experiment
- Statistical Process Control
- Manufacturing Systems - Statistical Process Control
- MIT Course - Control of Manufacturing Processes
- NIST Engineering Statistics Handbook