ASTM E2587-10
(Practice)Standard Practice for Use of Control Charts in Statistical Process Control
Standard Practice for Use of Control Charts in Statistical Process Control
SIGNIFICANCE AND USE
This practice describes the use of control charts as a tool for use in statistical process control (SPC). Control charts were developed by Shewhart (1) in the 1920s and are still in wide use today. SPC is a branch of statistical quality control (2, 3), which also encompasses process capability analysis and acceptance sampling inspection. Process capability analysis, as described in Practice E2281, requires the use of SPC in some of its procedures. Acceptance sampling inspection, described in Practices E1994 and E2234, requires the use of SPC so as to minimize rejection.
Principles of SPC—A process may be defined as a set of interrelated activities that convert inputs into outputs. SPC uses various statistical methodologies to improve the quality of a process by reducing the variability of one or more of its outputs, for example, a quality characteristic of a product or service.
A certain amount of variability will exist in all process outputs regardless of how well the process is designed or maintained. A process operating with only this inherent variability is said to be in a state of statistical control, with its output variability subject only to chance, or common, causes.
Process upsets, said to be due to assignable, or special causes, are manifested by changes in the output level, such as a spike, shift, trend, or by changes in the variability of an output. The control chart is the basic analytical tool in SPC and is used to detect the occurrence of special causes operating on the process.
When the control chart signals the presence of a special cause, other SPC tools, such as flow charts, brainstorming, cause-and-effect diagrams, or Pareto analysis, described in various references (3-7), are used to identify the special cause. Special causes, when identified, are either eliminated or controlled. When special cause variation is eliminated, process variability is reduced to its inherent variability, and control charts then function as a process monitor. ...
SCOPE
1.1 This practice provides guidance for the use of control charts in statistical process control programs, which improve process quality through reducing variation by identifying and eliminating the effect of special causes of variation.
1.2 Control charts are used to continually monitor product or process characteristics to determine whether or not a process is in a state of statistical control. When this state is attained, the process characteristic will, at least approximately, vary within certain limits at a given probability.
1.3 This practice applies to variables data (characteristics measured on a continuous numerical scale) and to attributes data (characteristics measured as percentages, fractions, or counts of occurrences in a defined interval of time or space).
1.4 The system of units for this practice is not specified. Dimensional quantities in the practice are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.
1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.
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Designation: E2587 − 10 AnAmerican National Standard
Standard Practice for
1
Use of Control Charts in Statistical Process Control
This standard is issued under the fixed designation E2587; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope 3. Terminology
1.1 This practice provides guidance for the use of control 3.1 Definitions:
charts in statistical process control programs, which improve 3.1.1 See Terminology E456 for a more extensive listing of
process quality through reducing variation by identifying and
statistical terms.
eliminating the effect of special causes of variation. 3.1.2 assignable cause, n—factor that contributes to varia-
tion in a process or product output that is feasible to detect and
1.2 Control charts are used to continually monitor product
identify (see special cause).
orprocesscharacteristicstodeterminewhetherornotaprocess
3.1.2.1 Discussion—Many factors will contribute to
isinastateofstatisticalcontrol.Whenthisstateisattained,the
variation, but it may not be feasible (economically or other-
process characteristic will, at least approximately, vary within
wise) to identify some of them.
certain limits at a given probability.
3.1.3 attributes data, n—observed values or test results that
1.3 This practice applies to variables data (characteristics
indicate the presence or absence of specific characteristics or
measured on a continuous numerical scale) and to attributes
counts of occurrences of events in time or space.
data (characteristics measured as percentages, fractions, or
3.1.4 average run length (ARL), n—the average number of
counts of occurrences in a defined interval of time or space).
times that a process will have been sampled and evaluated
1.4 The system of units for this practice is not specified.
before a shift in process level is signaled.
Dimensional quantities in the practice are presented only as
3.1.4.1 Discussion—A long ARL is desirable for a process
illustrations of calculation methods. The examples are not
located at its specified level (so as to minimize calling for
binding on products or test methods treated.
unneededinvestigationorcorrectiveaction)andashortARLis
1.5 This standard does not purport to address all of the
desirable for a process shifted to some undesirable level (so
safety concerns, if any, associated with its use. It is the
that corrective action will be called for promptly).ARLcurves
responsibility of the user of this standard to establish appro-
are used to describe the relative quickness in detecting level
priate safety and health practices and determine the applica-
shifts of various control chart systems (see section 5.4). The
bility of regulatory limitations prior to use.
average number of units that will have been produced before a
shift in level is signaled may also be of interest from an
2. Referenced Documents
economic standpoint.
2
2.1 ASTM Standards:
3.1.5 c chart, n—control chart that monitors the count of
E456 Terminology Relating to Quality and Statistics
occurrences of an event in a defined increment of time or space
E1994 Practice for Use of Process Oriented AOQL and
3.1.6 center line, n—line on a control chart depicting the
LTPD Sampling Plans
average level of the statistic being monitored.
E2234 Practice for Sampling a Stream of Product by Attri-
butes Indexed by AQL 3.1.7 chance cause, n—source of inherent random variation
E2281 Practice for Process and Measurement Capability
in a process which is predictable within statistical limits (see
Indices common cause).
3.1.7.1 Discussion—Chance causes may be unidentifiable,
or may have known origins that are not easily controllable or
1
This practice is under the jurisdiction ofASTM Committee E11 on Quality and
cost effective to eliminate.
Statistics and is the direct responsibility of Subcommittee E11.30 on Statistical
Quality Control.
3.1.8 common cause, n—(see chance cause).
Current edition approved Oct. 1, 2010. Published November 2010. Originally
´1
3.1.9 control chart, n—chart on which are plotted a statis-
approved in 2007. last previous edition approved in 2007 as E2587 – 07 . DOI:
10.1520/E2587-10.
ticalmeasureofasubgroupversustimeofsamplingalongwith
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
limits based on the statistical distribution of that measure so as
contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
to indicate how much common, or chance, cause variation is
Standards volumeinformation,refertothestandard’sDocumentSummarypageon
the ASTM website. inherent in the process or product.
Copyright © ASTM Internat
...
This document is not anASTM standard and is intended only to provide the user of anASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
´1
Designation:E2587–07 Designation:E2587–10
Standard Practice for
1
Use of Control Charts in Statistical Process Control
This standard is issued under the fixed designation E2587; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1
´ NOTE—Minor corrections were made editorially in February 2010.
1. Scope
1.1 This practice provides guidance for the use of control charts in statistical process control programs, which improve process
quality through reducing variation by identifying and controllingeliminating the process to a particular target level or historical
average. effect of special causes of variation.
1.2 Control charts are used to continually monitor product or process characteristics to determine whether or not a process is
in a state of statistical control. When this state is attained, the true mean and the true standard deviation of that characteristic are
constant. process characteristic will, at least approximately, vary within certain limits at a given probability.
1.3 This practice applies to variables data (characteristics measured on a continuous numerical scale) and to attributes data
(characteristics measured as percentages, fractions, or counts of occurrences in a defined interval of time or space).
1.4 Thesystemofunitsforthispracticeisnotspecified.Dimensionalquantitiesinthepracticearepresentedonlyasillustrations
of calculation methods. The examples are not binding on products or test methods treated.
1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory
limitations prior to use.
2. Referenced Documents
2
2.1 ASTM Standards:
E456 Terminology Relating to Quality and Statistics
E1994 Practice for Use of Process Oriented AOQL and LTPD Sampling Plans
E2234 Practice for Sampling a Stream of Product by Attributes Indexed by AQL
E2281 Practice for Process and Measurement Capability Indices
3. Terminology
3.1 Definitions—See Terminology E456 for a more extensive listing of statistical terms.
3.1.1 assignablecause,n—factorthatcontributestovariationinaprocessorproductoutputthatisfeasibletodetectandidentify
(see special cause).
3.1.1.1 Discussion—Manyfactorswillcontributetovariation,butitmaynotbefeasible(economicallyorotherwise)toidentify
some of them.
3.1.2 attributes data, n—observedvaluesortestresultsthatindicatethepresenceorabsenceofspecificcharacteristicsorcounts
of occurrences of events in time or space.
3.1.3 average run length (ARL), n—the average number of times that a process will have been sampled and evaluated before
a shift in process level is signaled.
3.1.3.1 Discussion—AlongARLis desirable for a process located at its specified level (so as to minimize calling for unneeded
investigation or corrective action) and a shortARL is desirable for a process shifted to some undesirable level (so that corrective
action will be called for promptly). ARL curves are used to describe the relative quickness in detecting level shifts of various
control chart systems (see section 5.4).The average number of units that will have been produced before a shift in level is signaled
may also be of interest from an economic standpoint.
1
This practice is under the jurisdiction ofASTM Committee E11 on Quality and Statistics and is the direct responsibility of Subcommittee E11.30 on Statistical Quality
Control.
Current edition approved Oct. 1, 2007. Published November 2007. DOI: 10.1520/E2587-07E01.
´1
Current edition approved Oct. 1, 2010. Published November 2010. Originally approved in 2007. last previous edition approved in 2007 as E2587–07 . DOI:
10.1520/E2587-10.
2
For referencedASTM standards, visit theASTM website, www.astm.org, or contactASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
1
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E2587–10
3.1.4 c chart, n—control chart that monitors the count of occurrences of an event in a defined increment of time or space
3.1.5 center line, n—line
...
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