Definición y significado de Statistical_process_control

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Statistical process control (SPC) is a method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste (rework or trash). SPC can be applied to any process where the "conforming product" (product meeting specifications) output can be measured. Some key tools are used in SPC. These include control charts; a focus on continuous improvement; and the design of experiments. An example of a process where SPC is applied is manufacturing lines.

  Overview

  Objective analysis of variation

SPC is a valuable process because it allows examination of specific parts of a process. In particular, it looks at the parts that may conceal sources of variation in the quality of the product. The examination involves objective analysis rather than subjective opinion. SPC also allows the strength of each source of variation to be determined numerically. If sources of variation are detected and measured, they may be amenable to correction. In turn, correction of variations may reduce waste in production and may improve the quality of the product that reaches the customer.

  Emphasis on early detection

An advantage of SPC over other methods of quality control, such as "inspection" is that it emphasises early detection and prevention of problems, rather than the correction of problems after they have occurred.

  Increasing rate of production

In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product. SPC makes it less likely the finished product will need to be reworked. SPC may also identify bottlenecks, waiting times, and other sources of delays within the process.

  Limitations

The application of SPC to a process aims to result in the elimination of process waste. This, in turn, removes the need for the process step of post-manufacture inspection. However, the success of SPC relies not only on the skill with which it is applied but also on how suitable or amenable the process is to SPC. In some cases, it may be difficult to judge when the application of SPC is appropriate.^{[citation needed]}

  History

SPC 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, to improve 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.^[1][2]Shewhart developed the "control chart" and the concept of a state of statistical control determined by carefully designed experiments.

  "Common" and "special" sources of variation

While Shewhart drew from pure mathematical statistical theories, he understood that data from physical processes seldom produced a "normal distribution curve"; that is, a Gaussian distribution or "bell curve". He discovered that data from measurements of variation in manufacturing did not always behave the same way as did data from measurements of natural phenomena (for example, Brownian motion of particles). Shewhart concluded that while every process displays variation, some processes display variation that is controlled and natural to the process ("common" sources of variation). Other processes display variation that is not controlled and that is not present in the causal system of the process at all times ("special" sources of variation).^[3]

  Application to non-manufacturing processes

In 1988, the Software Engineering Institute suggested that SPC could be applied to non-manufacturing processes, such as software engineering processes, in the Capability Maturity Model (CMM). The Level 4 and Level 5 practices of the Capability Maturity Model Integration (CMMI) uses this concept. The notion that SPC is a useful tool when applied to non-repetitive, knowledge-intensive processes such as engineering has encountered skepticism and remains controversial.^[4][5]

  Variation in manufacturing

In mass-manufacturing, traditionally, the quality of a finished article is ensured by post-manufacturing inspection of the product. Each article (or a sample of articles from a production lot) may be accepted or rejected according to how well it meets its design specifications. In contrast, SPC uses statistical tools to observe the performance of the production process in order to predict significant variations which may result in the production of a sub-standard article. A sources of variation at any one point of a production process will fall into one of two classes.

1) "Common" - sometimes referred to as "normal" or "chance" sources of variation and

2) "Assignable" - sometimes referred to as "special" sources of variation.

Most processes have many sources of variation and most of them are minor and may be ignored. If the dominant sources of variation are identified then resources for change can be focused on them. If the dominant assignable sources of variation can be detected, potentially they can be identified and removed. Once removed, the process is said to be "stable". When a process is stable, its variation should remain within a known set of limits. That is, at least, until another assignable source of variation is introduced. For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of cereal. Some boxes will have slightly more than 500 grams, and some will have slightly less. When package weight is measured, the data will demonstrate a distribution of net weights. If the production process, its inputs, or its environment (for example, the machines on the line) change, the distribution of the data will change. For example, as the cams and pulleys of the machinery wear, the cereal filling machine may put more than the specified amount of cereal into each box. Although this might benefit the customer, from the manufacturer's point of view, this is wasteful and increases the cost of production. If the manufacturer finds the change and its source in a timely manner, the change can be corrected (for example, the cams and pulleys replaced).

  Application of SPC

The application of SPC involves three main sets of activities. The first is understanding of the process. This is achieved by business process mapping. The second is measuring the sources of variation assisted by the use of control charts and the third is eliminating assignable (special) sources of variation.

  Control charts

The data from measurements of variations at points on the process map is monitored using control charts. Control charts can differentiate "assignable" ("special") sources of variation from "common" sources. "Common" sources, because they are an expected part of the process, are of much less concern to the manufacturer than "assignable" sources. Using control charts is a continuous activity, ongoing over time.

  Stable process

When the process does not trigger any of the control chart "detection rules" for the control chart, it is said to be "stable". A process capability analysis may be performed on a stable process to predict the ability of the process to produce "conforming product" in the future.

  Excessive variation

When the process triggers any of the control chart "detection rules",( or alternatively, the process capability is low), other activities may be performed to identify the source of the excessive variation. The tools used in these extra activities include: Ishikawa diagrams, designed experiments and Pareto charts. Designed experiments are critical to this phase of the application of SPC. They are the only means of objectively quantifying the relative importance (strength) of sources of variation. Once the sources of variation have been quantified, those sources that are both statistically and practically significant can be eliminated. (A source that is statistically significant may not be considered cost effect to eliminate. A source that is not statistically significant would not be considered significant in practical terms). Methods of eliminating a source of variation might include: development of standards; staff training; error-proofing and changes to the process itself.

  Mathematics of control charts

Digital control charts use logic based rules that determine "derived values" which signal the need for correction. For example,

derived value = last value + average absolute difference between the last N numbers.

Most SPC charts work best using numeric data with Gaussian assumptions. Recently a new type of control chart, the real-time contrasts chart^[6] was developed in order to handle process data with complex characteristics. Such data might include high-dimensional data; mixed numerical and categorical data; data with missing values; and data with non-Gaussian distributions or with non-linear relationships.

  See also

• Process control
• Process capability
• Process capability index
• Quality assurance
• Quality control
• ANOVA Gauge R&R
• Sampling (statistics)
• Stochastic control
• Electronic design automation
• Reliability engineering
• Six sigma
• Process Window Index

  References

  Bibliography

• Deng, H; Runger, G; Tuv, Eugene (2012). System monitoring with real-time contrasts, Journal of Quality Technology, 44(1), pp. 9-27.

• 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

  External links

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• worked example of the Deming's Red Bead Experiment
• Statistical Process Control
• Manufacturing Systems - Statistical Process Control
• MIT Course - Control of Manufacturing Processes
• NIST Engineering Statistics Handbook
• Is Statistical Process Control Applicable to Software Development Processes?
• Could the Software Engineering Institute be Wrong About Statistical Process Control?