ASTM E2587-14

Standard Practice for Use of Control Charts in Statistical Process Control

SIGNIFICANCE AND USE
4.1 This practice describes the use of control charts as a tool for use in statistical process control (SPC). Control charts were developed by Shewhart (2) in the 1920s and are still in wide use today. SPC is a branch of statistical quality control (3, 4), 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, E2234, and E2762, requires the use of SPC so as to minimize rejection of product.  
4.2 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.  
4.2.1 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.  
4.2.2 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.  
4.2.3 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 (4-8), 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 co...
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.

General Information

• Status: Historical
• Publication Date: 30-Sep-2014
• ICS: 03.120.30 - Application of statistical methods
• Technical Committee: E11 - Quality and Statistics
• Drafting Committee: E11.30 - Statistical Quality Control

Relations

• Replaces: ASTM E2587-12 - Standard Practice for Use of Control Charts in Statistical Process Control
• Effective Date: 01-Oct-2014
• Replaced By: ASTM E2587-14e1 - Standard Practice for Use of Control Charts in Statistical Process Control
• Effective Date: 01-Oct-2014
• Referred By: ASTM E456-13a(2022) - Standard Terminology Relating to Quality and Statistics
• Effective Date: 01-Oct-2014
• Referred By: ASTM E3353-22 - Standard Guide for In-Process Monitoring Using Optical and Thermal Methods for Laser Powder Bed Fusion
• Effective Date: 01-Oct-2014
• Referred By: ASTM E1323-15(2020) - Standard Guide for Evaluating Laboratory Measurement Practices and the Statistical Analysis of the Resulting Data
• Effective Date: 01-Oct-2014
• Referred By: ASTM D7669-20 - Standard Guide for Practical Lubricant Condition Data Trend Analysis
• Effective Date: 01-Oct-2014
• Referred By: ASTM E2691-20 - Standard Practice for Job Productivity Measurement
• Effective Date: 01-Oct-2014
• Referred By: ASTM E2554-18e1 - Standard Practice for Estimating and Monitoring the Uncertainty of Test Results of a Test Method Using Control Chart Techniques
• Effective Date: 01-Oct-2014
• Referred By: ASTM E3372-23 - Standard Guide for Schedule Performance Index, Schedule Beta (β<inf>s</inf >)
• Effective Date: 01-Oct-2014
• Referred By: ASTM E2859-11(2023) - Standard Guide for Size Measurement of Nanoparticles Using Atomic Force Microscopy
• Effective Date: 01-Oct-2014
• Referred By: ASTM E177-20 - Standard Practice for Use of the Terms Precision and Bias in ASTM Test Methods
• Effective Date: 01-Oct-2014
• Referred By: ASTM E1488-12(2023) - Standard Guide for Statistical Procedures to Use in Developing and Applying Test Methods
• Effective Date: 01-Oct-2014
• Referred By: ASTM E384-22 - Standard Test Method for Microindentation Hardness of Materials
• Effective Date: 01-Oct-2014
• Referred By: ASTM E3239-21 - Standard Guide for Using Statistical Process Control Principles for Routine Dosimetry in Radiation Processing
• Effective Date: 01-Oct-2014
• Referred By: ASTM D7310-21 - Standard Practice for Defect Detection and Rating of Plastic Films Using Optical Sensors
• Effective Date: 01-Oct-2014

Standards Content (Sample)

NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: E2587 − 14 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 E2762 Practice for Sampling a Stream of Product by Vari-
ables Indexed by AQL
1.1 This practice provides guidance for the use of control
charts in statistical process control programs, which improve
3. Terminology
process quality through reducing variation by identifying and
eliminating the effect of special causes of variation. 3.1 Definitions:
3.1.1 See Terminology E456 for a more extensive listing of
1.2 Control charts are used to continually monitor product
statistical terms.
orprocesscharacteristicstodeterminewhetherornotaprocess
isinastateofstatisticalcontrol.Whenthisstateisattained,the 3.1.2 assignable cause, n—factor that contributes to varia-
tion in a process or product output that is feasible to detect and
process characteristic will, at least approximately, vary within
certain limits at a given probability. identify (see special cause).
3.1.2.1 Discussion—Many factors will contribute to
1.3 This practice applies to variables data (characteristics
variation, but it may not be feasible (economically or other-
measured on a continuous numerical scale) and to attributes
wise) to identify some of them.
data (characteristics measured as percentages, fractions, or
counts of occurrences in a defined interval of time or space).
3.1.3 attributes data, n—observed values or test results that
indicate the presence or absence of specific characteristics or
1.4 The system of units for this practice is not specified.
counts of occurrences of events in time or space.
Dimensional quantities in the practice are presented only as
illustrations of calculation methods. The examples are not
3.1.4 average run length (ARL), n—the average number of
binding on products or test methods treated.
times that a process will have been sampled and evaluated
before a shift in process level is signaled.
1.5 This standard does not purport to address all of the
safety concerns, if any, associated with its use. It is the 3.1.4.1 Discussion—A long ARL is desirable for a process
responsibility of the user of this standard to establish appro- located at its specified level (so as to minimize calling for
priate safety and health practices and determine the applica- unneededinvestigationorcorrectiveaction)andashortARLis
bility of regulatory limitations prior to use.
desirable for a process shifted to some undesirable level (so
that corrective action will be called for promptly).ARLcurves
2. Referenced Documents
are used to describe the relative quickness in detecting level
2
shifts of various control chart systems (see 5.1.4). The average
2.1 ASTM Standards:
number of units that will have been produced before a shift in
E456 Terminology Relating to Quality and Statistics
level is signaled may also be of interest from an economic
E1994 Practice for Use of Process Oriented AOQL and
standpoint.
LTPD Sampling Plans
E2234 Practice for Sampling a Stream of Product by Attri-
3.1.5 c chart, n—control chart that monitors the count of
butes Indexed by AQL
occurrences of an event in a defined increment of time or
E2281 Practice for Process and Measurement Capability
space.
Indices
3.1.6 center line, n—line on a control chart depicting the
average level of the statistic being monitored.
1
This practice is under the jurisdiction ofASTM Committee E11 on Quality and
3.1.7 chance cause, n—source of inherent random variation
Statistics and is the direct responsibility of Subcommittee E11.30 on Statistical
Quality Control. in a process which is predictable within statistical limits (see
Current edition approved Oct. 1, 2014. Published November 2014. Originally
common cause).
approved in 2007. last previous edition approved in 2012 as E2587 – 12. DOI:
3.1.7.1 Discussion—Chance causes may be unidentifiable,
10.1520/E2587-12.
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or or may have known origins that are not easily controllable or
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
cost effective to eliminate.
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. 3.1.8 common cause, n—(see chance cause).
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
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E2587 − 14
3.1.9 control chart, n—chart on whi
...

This document is not an ASTM standard and is intended only to provide the user of an ASTM 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.
Designation: E2587 − 12 E2587 − 14 An American 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
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.
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
E2762 Practice for Sampling a Stream of Product by Variables Indexed by AQL
3. Terminology
3.1 Definitions:
3.1.1 See Terminology E456 for a more extensive listing of statistical terms.
3.1.2 assignable cause, n—factor that contributes to variation in a process or product output that is feasible to detect and identify
(see special cause).
1
This practice is under the jurisdiction of ASTM Committee E11 on Quality and Statistics and is the direct responsibility of Subcommittee E11.30 on Statistical Quality
Control.
Current edition approved Dec. 1, 2012Oct. 1, 2014. Published February 2013November 2014. Originally approved in 2007. last previous edition approved in 20102012
as E2587 – 10.E2587 – 12. DOI: 10.1520/E2587-12.
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
3.1.2.1 Discussion—
Many factors will contribute to variation, but it may not be feasible (economically or otherwise) to identify some of them.
3.1.3 attributes data, n—observed values or test results that indicate the presence or absence of specific characteristics or counts
of occurrences of events in time or space.
3.1.4 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.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
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E2587 − 14
3.1.4.1 Discussion—
A long ARL is desirable for a process located at its specified level (so as to minimize calling for unneeded investigation or
corrective action) and a short ARL 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 5.45.1.4). 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.
3.1.5 c chart, n—control chart that monitors the count of occurrences of an event in a defined increment of time or spacespace.
3.1.6 center line, n—line on a control chart depicting the average level of the statistic being mo
...