Statistical process control 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 of waste. SPC can be applied
to any process where the "conforming product" output can be measured. Key tools used in
SPC 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 must be practiced in 2 phases: The first phase is the initial establishment of the
process, and the second phase is the regular production use of the process. In the second
phase, we need to decide the period to be examined, depending upon the change in 4-M
conditions and wear rate of parts used in the manufacturing process.
Emphasis on early detection An advantage of SPC over other methods of
quality control, such as "inspection", is that it emphasizes 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 SPC is applied to reduce or eliminate process
waste. This, in turn, eliminates the need for the process step of post-manufacture inspection.
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. History
SPC was pioneered by Walter A. Shewhart at Bell Laboratories in the early 1920s. Shewhart
developed the control chart in 1924 and the concept of a state of statistical control.
Statistical control is equivalent to the concept of exchangeability developed by logician William
Ernest Johnson also in 1924 in his book Logic, Part III: The Logical Foundations of Science.
Along with a gifted team at AT&T that included Harold Dodge and Harry Romig he worked to
put sampling inspection on a rational statistical basis as well. Shewhart consulted with Colonel
Leslie E. Simon in the application of control charts to munitions manufacture at the Army's
Picatinney Arsenal in 1934. That successful application helped convince Army Ordnance
to engage AT&T's George Edwards to consult on the use of statistical quality control
among its divisions and contractors at the outbreak of World War II.
W. Edwards Deming invited Shewhart to speak at the Graduate School of the U.S. Department
of Agriculture, and served as the editor of Shewhart's book Statistical Method from the
Viewpoint of Quality Control which was the result of that lecture. Deming was an important
architect of the quality control short courses that trained American industry in the new
techniques during WWII. The graduates of these wartime courses formed a new professional
society in 1945, the American Society for Quality Control, which elected Edwards as
its first president. Deming traveled to Japan during the Allied Occupation and met with
the Union of Japanese Scientists and Engineers in an effort to introduce SPC methods to Japanese
industry . "Common" and "special" sources of variation
Shewhart read the new statistical theories coming out of Britain, especially the work
of William Sealy Gosset, Karl Pearson, and Ronald Fisher. However, 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 way as data from measurements of
natural phenomena. Shewhart concluded that while every process displays variation, some
processes display variation that is controlled and natural to the process. Other processes
display variation that is not controlled and that is not present in the causal system of
the process at all times. 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. The Level
4 and Level 5 practices of the Capability Maturity Model Integration use 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.
Variation in manufacturing No two products or characteristics are exactly
same, because any process contains many sources of variability. In mass-manufacturing, traditionally,
the quality of a finished article is ensured by post-manufacturing inspection of the product.
Each article 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 detect significant variations before they result in the production of a
sub-standard article. Any source of variation at any point of time in a process will fall
into one of two classes. 1) "Common Causes" - sometimes referred to
as nonassignable, normal sources of variation. It refers to many sources of variation that
consistently acts on process. These types of causes produces a stable and repeatable
distribution over time. 2) "Special Causes" - sometimes referred to
as assignable sources of variation. It refers to any factor causing variation that affects
only some of the process output. They are often intermittent and unpredictable.
Most processes have many sources of variation; most of them are minor and may be ignored.
If the dominant sources of variation are identified, however, resources for change can be focused
on them. If the dominant assignable sources of variation is 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 occurs. 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 the
package weights are measured, the data will demonstrate a distribution of net weights.
If the production process, its inputs, or its environment 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.
Application of SPC The application of SPC involves three main
phases of activity: Understanding the process and the specification
limits. Eliminating assignable sources of variation,
so that the process is stable. Monitoring the ongoing production process,
assisted by the use of control charts, to detect significant changes of mean or variation.
Control charts The data from measurements of variations at
points on the process map is monitored using control charts. Control charts attempt to
differentiate "assignable" 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",, 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 a means of objectively quantifying
the relative importance of sources of variation. Once the sources of variation have been quantified,
actions may be taken to reduce or eliminate them. Methods of eliminating a source of variation
might include: development of standards; staff training; error-proofing and changes to the
process itself or its inputs. 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. See also
Process capability Process capability index
Quality assurance ANOVA Gauge R&R
Stochastic control Electronic design automation
Reliability engineering Six sigma
Process Window Index References
Bibliography Barlow, R. E. & Irony, T. Z. "Foundations
of statistical quality control" in Ghosh, M. & Pathak, P.K. Current Issues in Statistical
Inference: Essays in Honor of D. Basu, Hayward, CA: Institute of Mathematical Statistics,
99-112. Bergman, B. "Conceptualistic Pragmatism: A
framework for Bayesian analysis?", IIE Transactions, 41, 86–93
Deming, W E "On probability as a basis for action", The American Statistician, 29(4),
146–152 — Out of the Crisis: Quality, Productivity
and Competitive Position ISBN 0-521-30553-5 Oakland, J Statistical Process Control ISBN
0-7506-5766-9 Shewhart, W A Economic Control of Quality
of Manufactured Product ISBN 0-87389-076-0 — Statistical Method from the Viewpoint
of Quality Control ISBN 0-486-65232-7 Wheeler, D J Normality and the Process-Behaviour
Chart ISBN 0-945320-56-6 Wheeler, D J & Chambers, D S Understanding
Statistical Process Control ISBN 0-945320-13-2 Wheeler, Donald J.. Understanding Variation:
The Key to Managing Chaos - 2nd Edition. SPC Press, Inc. ISBN 0-945320-53-1.
Wise, Stephen A. & Fair, Douglas C. Innovative Control Charting: Practical SPC Solutions
for Today's Manufacturing Environment. ASQ Quality Press. ISBN 0-87389-385-9
Zabell, S. L.. "Predicting the unpredictable". Synthese 90: 205. doi:10.1007/bf00485351.
External links worked example of the Deming's Red Bead Experiment
The Red Beads: Understanding Variation 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?
Signals from Noise Statistical Process Control Engine