ISO 7870-4:2021

FINAL
INTERNATIONAL ISO/FDIS
DRAFT
STANDARD 7870-4
ISO/TC 69/SC 4
Control charts —
Secretariat: DIN
Voting begins on:
Part 4:
2021-06-18
Cumulative sum charts
Voting terminates on:
2021-08-13
Cartes de contrôle —
Partie 4: Cartes de contrôle de l'ajustement de processus
RECIPIENTS OF THIS DRAFT ARE INVITED TO
SUBMIT, WITH THEIR COMMENTS, NOTIFICATION
OF ANY RELEVANT PATENT RIGHTS OF WHICH
THEY ARE AWARE AND TO PROVIDE SUPPOR TING
DOCUMENTATION.
IN ADDITION TO THEIR EVALUATION AS
Reference number
BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO-
ISO/FDIS 7870-4:2021(E)
LOGICAL, COMMERCIAL AND USER PURPOSES,
DRAFT INTERNATIONAL STANDARDS MAY ON
OCCASION HAVE TO BE CONSIDERED IN THE
LIGHT OF THEIR POTENTIAL TO BECOME STAN-
DARDS TO WHICH REFERENCE MAY BE MADE IN
©
NATIONAL REGULATIONS. ISO 2021

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ISO/FDIS 7870-4:2021(E)

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© ISO 2021
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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ISO/FDIS 7870-4:2021(E)

Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions, abbreviated terms and symbols . 1
3.1 Terms and definitions . 1
3.2 Abbreviated terms . 2
3.3 Symbols . 2
4 Principal features of cumulative sum (CUSUM) charts . 3
5 Basic steps in the construction of CUSUM charts — Graphical representation .4
6 Example of a CUSUM plot — Motor voltages . 5
6.1 Process . 5
6.2 Simple plot of results . 5
6.3 Standard control chart for individual results . 6
6.4 CUSUM chart construction . 7
7 Fundamentals of making CUSUM-based decisions . 8
7.1 Need for decision rules . 8
7.2 Basis for making decisions . 8
7.3 Measuring the effectiveness of a decision rule . 9
7.3.1 Basic concepts . 9
7.3.2 Example of calculation of ARL .10
8 Types of CUSUM decision schemes .10
8.1 V-mask .10
8.1.1 Configuration and dimensions .10
8.1.2 Application of the V-mask .11
8.1.3 Average run lengths .14
8.1.4 General comments on average run lengths .15
8.2 Fast-initial response (FIR) CUSUM .16
8.3 Tabular CUSUM .16
8.3.1 Rationale .16
8.3.2 Deployment .17
9 CUSUM methods for process and quality control .19
9.1 Nature of the changes to be detected .19
9.1.1 Size of the changes to be detected .19
9.1.2 ‘Step’ changes .19
9.1.3 Drifting .19
9.1.4 Cyclic.19
9.1.5 Hunting.19
9.2 Selecting target values .19
9.2.1 General.19
9.2.2 Standard (given) value as target .20
9.2.3 Performance-based target .20
9.3 CUSUM schemes for monitoring location .20
9.3.1 Standard schemes .20
9.3.2 Standard schemes — Limitations .27
9.3.3 ‘Tailored’ CUSUM schemes .27
9.4 CUSUM schemes for monitoring variation .28
9.4.1 General.28
9.4.2 CUSUM schemes for subgroup ranges .29
9.4.3 CUSUM schemes for subgroup standard deviations .32
9.5 Special situations .36
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ISO/FDIS 7870-4:2021(E)

9.5.1 Large between-subgroup variation .36
9.5.2 ‘One-at-a-time’ data .36
9.5.3 Serial dependence between observations .36
9.5.4 Outliers .37
9.6 CUSUM schemes for discrete data .38
9.6.1 Event count — Poisson data .38
9.6.2 Two classes data — Binomial data .40
Annex A (informative) Example of tabular CUSUM .44
Annex B (informative) Estimation of the change point when a step change occurs .48
Bibliography .50
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ISO/FDIS 7870-4:2021(E)

Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
through ISO technical committees. Each member body interested in a subject for which a technical
committee has been established has the right to be represented on that committee. International
organizations, governmental and non-governmental, in liaison with ISO, also take part in the work.
ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/ directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of
any patent rights identified during the development of the document will be in the Introduction and/or
on the ISO list of patent declarations received (see www .iso .org/ patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and
expressions related to conformity assessment, as well as information about ISO's adherence to the
World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www .iso .org/
iso/ foreword .html.
This document was prepared by Technical Committee ISO/TC 69, Applications of statistical methods,
Subcommittee SC 4, Applications of statistical methods in process management.
This second edition of ISO 7870-4 cancels and replaces the first edition (ISO 7870-4: 2011), which has
been technical revised.
The main changes compared to the previous edition are as follows:
— Manhattan diagram removed (former 6.7);
— V-mask types in Types of CUSUM decision schemes reduced to one V-mask;
— von Neumann method removed (former Annex A).
A list of all parts in the ISO 7870 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www .iso .org/ members .html.
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ISO/FDIS 7870-4:2021(E)

Introduction
This document demonstrates the versatility and usefulness of a very simple, yet powerful, pictorial
method of interpreting data arranged in any meaningful sequence. These data can range from overall
business figures such as turnover, profit or overheads to detailed operational data such as stock outs
and absenteeism to the control of individual process parameters and product characteristics. The data
can either be expressed sequentially as individual values on a continuous scale (e.g. 24, 60, 31, 21, 18,
97.), in ’yes’/‘no’, ‘good’/‘bad’, ‘success’/‘failure’ format, or as summary measures (e.g. mean, range,
counts of events).
The method has a rather unusual name, cumulative sum, or CUSUM. This name relates to the process
of subtracting a predetermined value, e.g. a target, preferred or reference value from each observation
in a sequence and progressively cumulating (i.e. adding) the differences. The graph of the series of
cumulative differences is known as a CUSUM chart. Such a simple arithmetical process has a remarkable
effect on the visual interpretation of the data.
The CUSUM method is already used unwittingly by golfers throughout the world. By scoring a round
as ‘plus’ 4, or perhaps even ‘minus’ 2, golfers are using the CUSUM method in a numerical sense. They
subtract the ‘par’ value from their actual score and add (cumulate) the resulting differences. This is the
CUSUM method in action. However, it remains largely unknown and hence is a grossly underused tool
throughout business, industry, commerce and public service. This is probably due to CUSUM methods
generally being presented in statistical language rather than in the language of the workplace.
The intention of this document is, thus, to be readily comprehensible to the extensive range of
prospective users and so facilitate widespread communication and understanding of the method. The
method offers advantages over the more commonly found Shewhart charts in as much as the CUSUM
method detects a change of an important amount up to three times faster. Further, as in golf, when
the target changes per hole, a CUSUM plot is unaffected, unlike a standard Shewhart chart where the
control lines require constant adjustment.
In addition to Shewhart charts, an EWMA (exponentially weighted moving average) chart can be used.
Each plotted point on an EWMA chart incorporates information from all the previous subgroups or
observations but gives less weight to process data as they get ‘older’ according to an exponentially
decaying weight. In a similar manner to a CUSUM chart, an EWMA chart can be sensitized to detect any
size of shift in a process. This subject is discussed further in 7870-6.
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FINAL DRAFT INTERNATIONAL STANDARD ISO/FDIS 7870-4:2021(E)
Control charts —
Part 4:
Cumulative sum charts
1 Scope
This document describes statistical procedures for setting up cumulative sum (CUSUM) schemes for
process and quality control using variables (measured) and attribute data. It describes general-purpose
methods of decision-making using cumulative sum (CUSUM) techniques for monitoring, control and
retrospective analysis.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO 3534-1, Statistics — Vocabulary and symbols — Part 1: General statistical terms and terms used in
probability
ISO 3534-2, Statistics — Vocabulary and symbols — Part 2: Applied statistics
3 Terms and definitions, abbreviated terms and symbols
For the purposes of this document, the terms and definitions given in ISO 3534-1 and ISO 3534-2 and
the following apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1 Terms and definitions
3.1.1
target value
Τ
value for which a departure from an average level is required to be detected
Note 1 to entry: With a charted CUSUM, the deviations from the target value are cumulated.
Note 2 to entry: Using a ‘V’ mask, the target value is often referred to as the reference value or the nominal
control value. If so, it needs be acknowledged that it is not necessarily the most desirable or preferred value, as
can appear in other standards. It is simply a convenient target value for constructing a CUSUM chart.
3.1.2
representative out of control value
〈tabulated CUSUM〉 value which controls the sensitivity of the procedure
Note 1 to entry: The upper out of control value is T + fσ , for monitoring an upward shift. The lower control value
e
is T − fσ , for monitoring a downward shift.
e
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3.1.3
reference shift
F, f
〈tabulated CUSUM〉 difference between the target value (3.1.1) and the representative out of control value
(3.1.2)
Note 1 to entry: It is necessary to distinguish between f, that relates to a standardized reference shift, and F,
that relates to an observed reference shift; F = fσ . It plays a crucial role for constructing the tabular form of the
e
CUSUM chart.
3.1.4
decision interval
H, h
〈tabulated CUSUM〉 cumulative sum of deviations from a representative out of control value (3.1.2)
required to yield a signal
Note 1 to entry: It is necessary to distinguish between h, that relates to a standardized decision interval, and H,
that relates to an observed decision interval; H = hσ .
e
3.1.5
average run length
ARL
average number of samples taken up to the point at which a signal occurs
Note 1 to entry: The average run length (ARL) is usually related to a process level, in which case it carries an
appropriate subscript as, for example, ARL , meaning the average run length when the process is at target level,
0
i.e. zero shift.
3.2 Abbreviated terms
ARL average run length
CS1 CUSUM scheme with a long ARL at zero shift
CS2 CUSUM scheme with a shorter ARL at zero shift
FIR fast initial response
LCL lower control limit
RL run length
SPC statistical process control
UCL upper control limit
3.3 Symbols
a scale coefficient
C CUSUM value
c factor for estimating the within-subgroup standard deviation
4
δ amount of change to be detected
Δ standardized amount of change to be detected
d lead distance
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ISO/FDIS 7870-4:2021(E)

d factor for estimating the within-subgroup standard deviation from within-subgroup range
2
F observed reference shift
f standardized reference shift
K reference value, equal to sum of target T and observed reference shift F
H observed decision interval
h standardized decision interval
ARL average run length at δ shift
δ
ARL average run length at no shift μ population mean value
0
n subgroup size
m number of subgroups within a preliminary study
p probability of ‘success’
mean subgroup range
R
σ process standard deviation
σ within-subgroup standard deviation
0
estimated within-subgroup standard deviation
σˆ
0
σ standard error
e
s observed within-subgroup standard deviation
s
average subgroup standard deviation
s realized standard error of the mean from m subgroups
x
T target value
τ true change point
τˆ
estimated change point
x individual result
x
arithmetic mean value (of a subgroup)
mean of subgroup means
x
4 Principal features of cumulative sum (CUSUM) charts
A CUSUM chart is essentially a running total of deviations from some preselected reference value. The
mean of any group of consecutive values is represented visually by the current slope of the graph. The
principal features of a CUSUM chart are the following.
a) It is sensitive in detecting changes in the mean.
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ISO/FDIS 7870-4:2021(E)

b) Any change in the mean, and the extent of the change, is indicated visually by a change in the slope
of the graph:
1) a horizontal graph indicates an ‘on-target’ or at reference value;
2) a downward slope indicates a mean less than the reference or target value: the steeper the
slope, the bigger the difference;
3) an upward slope indicates a mean more than the reference or target value: the steeper the
slope, the bigger the difference.
c) It can be used retrospectively for investigative purposes, on a running basis for control, and for
prediction of performance in the immediate future.
Referring to point b) above, a CUSUM chart has the capacity to clearly indicate points of change; these
are clearly indicated by the change in gradient of the CUSUM plot. This has enormous benefit for process
management: to be able to quickly and accurately pinpoint the moment when a process altered so that
the appropriate corrective action can be taken.
A further very useful feature of a CUSUM system is that it can be handled without plotting, i.e. in tabular
form. This is very helpful if the system is to be used to monitor a highly technical process, e.g. plastic
film manufacture, where the number of process parameters and product characteristics is large. Data
from such a process can be captured automatically, downloaded into CUSUM software to produce an
automated CUSUM analysis. A process manager can then be alerted to changes on many characteristics
on a simultaneous basis. Annex A contains an example of the method.
5 Basic steps in the construction of CUSUM charts — Graphical representation
The following steps are used to set up a CUSUM chart for individual values.
Step 1 — Choose a reference, target, control or preferred value. The average of past results generally
provides good discrimination.
Step 2 — Tabulate the results in a meaningful (e.g. chronological) sequence. Subtract the reference
value from each result.
Step 3 — Progressively sum the values obtained in Step 2. These sums are then plotted as a CUSUM
chart.
Step 4 — For reasonable discrimination, without undue sensitivity, the following options are
recommended:
a) choose a convenient plotting interval for the horizontal axis and make the same interval on the
vertical axis equal to 2σ (or 2σ if a CUSUM of means is to be charted), rounding off as appropriate;
e
or
b) where it is required to detect a known change, say δ, choose a vertical scale such that the ratio of
the scale unit on the vertical scale divided by the scale unit on the horizontal scale is between δ and
2δ, rounding off as appropriate.
NOTE The scale selection is visually very important since an inappropriate scale gives either the impression
of impending disaster due to the volatile nature of the plot, or a view that nothing is changing. The schemes
described in a) and b) above can give a scale that shows changes in a reasonable manner, neither too sensitive nor
too suppressed.
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6 Example of a CUSUM plot — Motor voltages
6.1 Process
Suppose a set of 40 values in chronological sequence is obtained of a characteristic. These happen to be
voltages, taken in order of production, on fractional horsepower motors at an early stage of production.
But they can be any individual values taken in a meaningful sequence and expressed on a continuous
scale. These are now shown:
9, 16, 11, 12, 16, 7, 13, 12, 13, 11, 12, 8, 8, 11, 14, 8, 6, 14, 4, 13, 3, 9, 7, 14, 2, 6, 4, 12, 8, 8, 12, 6, 14, 13, 12,
14, 13, 10, 13, 13.
The reference or target voltage value is 10 V.
6.2 Simple plot of results
To gain a better understanding of the underlying behaviour of the process, by determining patterns and
trends, a standard approach is simply to plot these values in their natural order as shown in Figure 1 a).
Apart from indicating a general drop away in the middle portion from a high start and with an equally
high finish, Figure 1 a) is not very revealing because of the extremely noisy, or spiky, data throughout.
a)  Simple plot of motor voltages
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b) Standard control chart for individuals
c)  CUSUM chart
Key
X motor number
Y1 voltage
Y2 cumulative sum
Figure 1 — Motor voltage example
6.3 Standard control chart for individual results
The next level of sophistication is to establish a standard control chart for individuals as in Figure 1 b).
Figure 1 b) is even less revealing than the previous figure. It is, in fact, quite misleading. The standard
statistical process control criterion to test for process stability and control is just “no points lying above
the upper control limit (UCL) or below the lower control limit (LCL)”. All points reside within these
limits. Hence, one can be led to the conclusion that this is a stable process, one that is ‘in control’ around
its overall average value of about 10 V, which is the target value. Further standard analysis would
reveal that although the process is stable, it is not capable of meeting the specification requirements.
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However, this analysis would not in itself provide any further clues as to why it is incapable of meeting
the requirements.
The reason for the inability of the standard control chart for individuals to be of value is that the
control limits are based on actual process performance and not on desired or specified requirements.
Consequently, if the process naturally exhibits a large variation the control limits are correspondingly
wide. What is required is a method that is better at indicating patterns and trends, or even pinpointing
points of change, to help determine and remove primary sources of variation.
NOTE By using additional tools, such as an individual and moving range chart, the practitioner can study
other process variation issues.
6.4 CUSUM chart construction
The construction of a CUSUM cha
...