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 BRITISH STANDARD BS 5701-1:2003

Guide to quality control

and performance
improvement using
qualitative (attribute)
data —
Part 1: Uses and value of attribute
charts in business, industry, commerce
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and public service

ICS 03.120.30

12&23<,1*:,7+287%6,3(50,66,21(;&(37$63(50,77('%<&23<5,*+7/$:
 BS 5701-1:2003

Committees responsible for this

British Standard
The preparation of this British Standard was entrusted to Technical
Committee, SS/4, Statistical process control, upon which the following bodies
were represented:

Association for Road Traffic Safety and Management (ARTSM)

BAE Systems
British Standards Society (BSS)
Clay Pipe Development Association (CPDA)
Federation of Small Businesses (FSB)
Gauge and Tool Makers Association (GTMA)
General Domestic Appliances Ltd.
Institute of Quality Assurance
National Physical Laboratory
Royal Statistical Society
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This British Standard, having

been prepared under the
direction of the Standards
Policy and Strategy Committee,
was published on
31 October 2003

© BSI 31 October 2003

Amendment issued since publication

First published as BS 5701
February 1980 Amd. No. Date Comments

The following BSI references

relate to the work on this
British Standard:
Committee reference SS/4
Draft for comment 02/400887 DC

ISBN 0 580 42734 X

 BS 5701-1:2003

Contents
Page
Committees responsible Inside front cover
Foreword ii
1 Scope 1
2 Normative references 1
3 Terms, definitions and symbols 1
4 Qualitative (attribute) data fundamentals 1
5 Business process focus and management 7
6 Supporting techniques for effective process control and
performance improvement 9
7 Relationship with Six Sigma initiatives 15
8 Typical case study 16
Annex A (informative) Base data for case study of Clause 8 25
Bibliography 27
Figure 1 — Different classes of data 3
Figure 2 — Process control, capability estimation and improvement
sequence 4
Figure 3 — Control chart for assembly of men’s briefs 6
Figure 4 — Basic process model 10
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Figure 5 — Application of the basic process model to the actioning of a

product design change 10
Figure 6 — Business is made up of integrated multistage processes 11
Figure 7 — Video disc pressing: flow diagram of integrated multistage
process 11
Figure 8a) — Illustration of the calculation of logistic capability and
first run capability of a process stage 12
Figure 8b) — Logistic and first run capabilities of the video disc
manufacturing process 13
Figure 9 — Example of Pareto diagram for paint process faults 14
Figure 10 — Process cause and effect diagram for cracks in a brake
disc casting 15
Figure 11 — First time quality profile of trouser line 17
Figure 12 — Pareto analysis by process operation 18
Figure 13 — Pareto analysis by machinist/operator 18
Figure 14 — Doreen lapping side seams (5.3 faults per day);
Total 275 faults (15 % of total faults) 19
Figure 15 — Jane lapping side seams (2.1 faults per day) 19
Figure 16 — Sue and Rita lapping side seams 20
Figure 17 — Su barring loops 20
Figure 18 — Brenda barring loops 21
Figure 19 — Mandy putting the bands on 21
Figure 20 — Rosalie machining inside legs 22
Figure 21 — Delia and Dorry machining inside legs 22
Figure 22 — Shirley and Dawn closing the fly 23
Figure 23 — Janet closing the fly 23
Figure 24 — Jean closing the fly 24
Table 1 — Typical business applicable characteristics 2
Table 2 — Relationship between Sigma value, non-conformities
per million opportunities and percentage yield 15
Table A.1 — Base data for case study of Clause 8 25

© BSI 31 October 2003 i

 BS 5701-1:2003

Foreword

BS 5701-1 demonstrates the business benefits, and the versatility and usefulness
of a very simple, yet powerful, pictorial control chart method for monitoring and
interpreting qualitative data. This is done in a practical and largely
non-statistical manner. BS 5701-1:2003 partially supersedes BS 5701:1980 and
BS 2564:1955 and all four parts of BS 5701 together supersede BS 5701:1980 and
BS 2564:1955, which are withdrawn.
This qualitative data can range from overall business figures such as percentage
profit to detailed operational data, such as percentage absenteeism, individual
process parameters and product/service features. The data can either be
expressed sequentially in yes/no, good/bad, present/absent, success/failure
format, or as summary measures (e.g. counts of events and proportions). For
measured data control charting, refer to BS 5702-1.
The focus is on the application of control charts to monitoring, control and
improvement. The roles of associated diagnostic, presentation and performance
improvement tools, such as priority (Pareto) analysis, cause and effect diagrams
and flow charts are also shown.
Its aim is to be readily comprehensible to the very extensive range of prospective
users and so facilitate widespread communication, and understanding, of the
method. As such, it focuses on a practical non-statistical treatment of the charting
of qualitative data, presenting examples of construction and application using a
simple pictorial approach.
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Whilst the treatment of charting of qualitative data in this part of BS 5701 is

essentially at appreciation level, it is intended to provide adequate information
for a gainful first application, by a typical less statistically inclined user, in many
everyday situations. BS 5701-2 and BS 5701-3 provide a more rigorous,
statistical approach to process control and improvement using qualitative data.
BS 5701-4 deals with measuring and improving the quality of decision making in
the classification process itself.
A British Standard does not purport to include all the necessary provisions of a
contract. Users of British Standards are responsible for their correct application.
Compliance with a British Standard does not of itself confer immunity
from legal obligations.

Summary of pages
This document comprises a front cover, an inside front cover, pages i and ii,
pages 1 to 27 and a back cover.
The BSI copyright notice displayed in this document indicates when the
document was last issued.

ii © BSI 31 October 2003

 BS 5701-1:2003

1 Scope
BS 5701-1 describes, in lay terms, the uses and value of pictorial control chart methods for enhancing the
presentation and general understanding of qualitative (attribute) data arranged in a meaningful sequence.
It also illustrates the supporting roles of associated business improvement tools. These include process
modelling, flow charting, prioritizing (Pareto analysis) and cause and effect diagrams.

2 Normative references
The following referenced documents are indispensable for the application 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.
BS EN ISO 9000:2000, Quality management systems — Fundamentals and vocabulary.
BS ISO 3534-1, Statistics — Vocabulary and symbols — Part 1: Probability and general statistical terms.
BS ISO 3534-2, Statistics — Vocabulary and symbols — Part 2: Applied statistics.

3 Terms, definitions and symbols

For the purposes of this part of BS 5701, the terms, definitions and symbols given in BS ISO 3534-1,
BS ISO 3534-2 and BS EN ISO 9000:2000, Clause 3 apply.
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4 Qualitative (attribute) data fundamentals

4.1 General
In this technological age, we are awash with data. The challenge is to transform such data into meaningful
information on a characteristic. There is a wide range of classes of characteristic, including:
a) physical (e.g. mechanical, electrical, chemical or biological);
b) sensory (e.g. relating to smell, touch, taste, sight or hearing);
c) behavioural (e.g. courtesy, honesty or veracity);
d) temporal (e.g. punctuality, reliability or availability);
e) ergonomic (e.g. linguistic, physiological or relating to safety);
f) functional (e.g. range, speed, or rate of climb of an aircraft).
Typical business application characteristics, shown in Table 1, that are the subject of control and
progressive improvement further indicate the wide scope of the subject matter of this standard.

© BSI 31 October 2003 1

 BS 5701-1:2003

Table 1 — Typical business applicable characteristics

Business application Characteristic
business profitability
marketing market share
staff absenteeism
personnel turnover
sales enquiries results
organization overall errors/faults
product non-conformities
service complaints
incoming phone calls response delays
invoicing accuracy
deliveries lateness
accounts overdue
vehicles, machines status
warranty costs over-run
software errors/103 lines
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computers status
communications clarity, timeliness

4.2 Types of data

Two general classes of data are distinguished here, measured data and attribute data. These classes can be
otherwise termed continuous data and discrete data or, even, quantitative data as opposed to
qualitative data. Whereas continuous data is measured on a numerical scale with a continuum of possible
values, discrete data is referenced on a scale with only a set or sequence of distinct values.
This standard focuses on attribute data. Attribute data are divided for convenience, into two categories,
classified data and count data.
With classified data, each item of data is classified as being one of a number of categories. Frequently the
number of categories is two, namely a binary situation where, for instance, results are usually expressed
as 0 and 1, or as, good/bad, success/failure, profit/loss, in/out, or presence/absence of a particular
characteristic or feature.
Data having two classes are termed “binomial” (binomial = “two names”) data. A measure can be inherently
binomial, e.g. where a profit or loss is made, or if someone is in or out. Sometimes it is arrived at indirectly
by categorizing some other numerical measure. Take, for instance, the case where telephone calls are
classified as to whether or not they last more than ten minutes or, perhaps whether or not they are
answered within six rings.
Count data relates to counts of events where each item of data is the count of the number of particular
events per given time period or quantity of product. Instances are:
— number of accidents or absentees per month;
— number of operations or sorties per day;
— number of incoming telephone calls per minute; or
— number of non-conformities per unit or batch.
A pictorial representation of the different types of data is shown in Figure 1.

2 © BSI 31 October 2003

 BS 5701-1:2003

Type of data

discrete continuous
(attribute) (measured)
(qualitative) (quantitative)

See BS 5702
count of classified into
events (e.g. categories
nonconformities) (e.g. good/bad)
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constant variable constant variable

sample sample sample sample
size size size size

Figure 1 — Different classes of data

4.3 Principles, objectives and rationale of the control charting of qualitative data
4.3.1 Overview
Mention of the word statistics invokes a feeling of apprehension in many people. However, everyone should
positively respond to, understand and adopt the primary concepts of “statistical thinking”. These are as
follows:
1) All work occurs in a system of interconnected processes.
2) Variation exists in all processes.
3) Elimination of special cause variation produces process stability and predictability.
4) Reduction in common cause variation is the key to continual improvement.
The first concept highlights that “statistical thinking” should not be confined to a specific function of an
organization. It is applicable across the whole spectrum of business activity.
The second concept brings out that variation is present in almost everything. Its existence provides
opportunities for better process control and for process performance improvement.
The third and fourth concepts stress the need to clearly distinguish between the variation due to
“special causes” and that due to “common causes”. The reason for this is that they give rise to two quite
different types of action. A special cause is a sporadic source of variation that demands specific action to
restore the status quo. A common cause, on the other hand, is an endemic source of variation that is always
present, as it is inherent to the process. A reduction demands fundamental changes to the process.
The elimination of special causes brings a process back under control. The process performance or
capability is then stable and predictable. A stable process might, or might not, provide the desired
performance. A reduction in common cause variation improves process capability or performance.
A control chart is the tool used to differentiate between “special causes” and “common causes”. Hence the
control chart is a key operational tool in the application of “statistical thinking”.

© BSI 31 October 2003 3

 BS 5701-1:2003

By its very name, a primary role of a control chart, in an operational sense, is to control; namely to inhibit
change. The removal of adverse special cause variation to bring a process back into control does not actually
improve the process: it only returns it to its original state.
It should be borne in mind, too, that a special cause can be adverse or beneficial. Suppose a supervisor
stands in momentarily for an operator in a process and a special cause is indicated on a fault chart. The
special cause could be a single reading above the upper control limit indicating an adverse change.
Alternatively, it could be a single reading below the lower control limit indicating a beneficial change. The
reaction to the two types of special causes would be quite different.
This focus on control, however, should not blind one to the fact that often the objective, in an overall sense,
is to improve process performance by inducing change. Such betterment, through common cause reduction
does not necessarily have to await special cause removal. However, the prior removal of special causes gives
rise to a stable process and so permits a quantitative prediction of process capability. The generic control
and improvement sequence is shown in Figure 2.
A significant improvement in process performance is evidenced in a control chart by an “out of control”
situation, as is a significant deterioration. Hence the control chart has an in-built statistical test of
significance for both improvement and deterioration.

Select
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quality
characteristic

Set-up and run

appropriate
control chart

yes Is process no
stable?

Estimate Seek reason

process for
capability special cause

Priority Is special
no for no
cause
improvement? adverse?

yes yes
Reduce Eliminate
common special
causes cause

Figure 2 — Process control, capability estimation and improvement sequence

4 © BSI 31 October 2003

 BS 5701-1:2003

4.3.2 Over-control, under-control and control of processes

A process monitoring system can give rise to:
a) over-control: action is taken when it should not be;
b) under-control: action is not taken when it should be;
c) control: action is taken when it should be and not taken when it should not be. A process is said to be
under a state of (statistical) control when no special causes of variation are present. Variation can then
be attributed purely to “common causes”. Control is not a natural state but it is an achievement, arrived
at by elimination, one by one, by determined effort, of special cause variation. To achieve this it is
essential to use statistical process control (SPC) charts that set out to provide a signal when a special
cause of variation is present, and to avoid giving false signals when a special cause is not present.
Sometimes “assignable cause” is taken to be synonymous with “special cause”. However, a distinction
should be recognized. In practice, not all special causes are assignable. A state of control does not imply
that the common cause variation is large or small, within or outside of specification, but rather that it is
predictable using statistical techniques.
4.3.3 The control chart
The control chart is a simple graphical tool that distinguishes between two types of variation:
— special cause variation: a source of variation that is not present all the time but which arises from
specific circumstances;
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— common cause variation: a source of variation that is inherent in a process over time.
When no special cause is present, the process is considered to be stable. It is then said to be in a state of
statistical control. The capability of a stable attribute process can be calculated directly from the centre-line
of the control chart. When a special cause is present, the process is said to be “out-of-control” and needs to
be brought back under control.
These process states are readily determined from an attribute control chart. The attribute control chart is
essentially a simple run chart with two added features, a centre-line and one or two control limits. The
centre-line on an attribute control chart is normally the historical mean of the feature plotted. The control
limit, or limits are lines on a control chart placed about the centre-line to evaluate whether, or not, the
process is in a state of control. Typical criteria for assessing whether a control chart indicates an
“out-of-control” situation are:
a) any point outside of the control limits;
b) any run of seven consecutive points above or below the centre-line;
c) any run of seven consecutive points up or down;
d) any obvious non-random patterns (based on technical and operational knowledge of the process).
4.3.4 Case study
Figure 3 shows a specific example of the use of an attribute control chart in process improvement. It relates
to an underwear making-up (assembly) process.

© BSI 31 October 2003 5

 BS 5701-1:2003

Crotch stitch (broken needle)

30
25 Shop reorganization
UCL
Fault rate %

20
15 Oil leak
10
5 UCL
0

Production sequence of mens briefs

Figure 3 — Control chart for assembly of men’s briefs

NOTE Out of control criteria are:

— any point outside the upper control limit (UCL);
— any run of seven consecutive points above or below the centre-line;
— any run of seven consecutive points up or down.
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Figure 3 shows the following:

a) There are two quite different stages of performance.
b) The process was running initially at an overall fault rate of around 10 % (indicated by the dashed
centre-line). This percentage provides a measure of overall process capability or performance. This is due
to common causes endemic to the system of operation of the process: the accepted state of the art as
presently performed. The value of 10 % was, and always had been, the expected level of performance and
had been budgeted into the system. Hence, it was not considered a concern to operational management.
The system had adapted to the situation by introducing aerospace standards of garment inspection with
its attendant internal reject levels. As such, it was considered a premier division quality supplier to its
major customer, a quality conscious retailer. However, the adverse effect of this state of affairs on
company profit margin and product price was considerable.
c) During this period two points contravene the upper control limit (UCL). This indicates two undesirable
“out-of-control” situations due to the presence of special causes. These causes were investigated and
assigned to a broken needle and a sewing shop re-organization, as indicated on the control chart.
Management got very uptight about the broken needle situation, as a single machinist had worked with
it during a whole period of overtime causing perforated crotches on many dozens of briefs. Whilst it is
seen that the elimination of these special causes brought the process back into control it had no material
effect on the reduction of overall faults.
d) Towards the end of the 10 % run, seven consecutive plotting points are below the original centre-line.
This indicates a desirable out-of-control situation. It indicates that the process overall performance has
changed significantly for the better. This was due to a management led shake-up and major training and
personnel development initiative which gave rise to a reduction in common cause variation from a
nearly 10 % fault rate to less than 1 %.
e) This significant improvement in process performance is reflected by a lowering of the upper control
limit (UCL) resulting from the decrease in fault rate from 10 % to less than 1 %.
f) An out-of-control situation that causes a blip to nearly 5 % at the new level was caused by an oil leak
from one machine causing staining of a number of garments.
This example illustrates why it is important to differentiate between special and common cause variation.
The sporadic special cause variation is due to specific activities attributable to a machinist and direct
support personnel caused by a weakness in operational control.
The initial overall performance of a 10 % fault rate, however, is a result of common causes endemic in the
system, which is a management responsibility. Management should be judged on whether or not their
words of dedication to never-ending improvement is actually showing through in improvements in the
capability of processes evidenced by the simple but very revealing attribute data control chart.

6 © BSI 31 October 2003

 BS 5701-1:2003

Summarizing, Figure 3 illustrates that an attribute control chart is no more than a simple run chart with
additional guidance in interpretation in the form of centre-lines and control limits. It is easy to create and
yet it provides direct visual answers to the three key questions one should be asking of every significant
process in any organization, namely:
i) Is the process in control?
ii) What is the performance of the process?
iii) Is there evidence of improvement in performance?
An additional point is that many control systems include reaction procedures only for adverse
“out-of-control” situations. It is illustrated here that a process also needs to go “out-of-control”, in the
favourable sense, for an improvement in performance to occur.

5 Business process focus and management

5.1 Business process focus
In today’s business environment, a process focus is essential, as each and every activity, function or task
within an organization can be considered to be a process. In focusing on the processes, a number of concepts
and principles should be borne in mind. These are as follows.
a) The mindset of today is one of prevention and continuous improvement.
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b) Process improvement focuses on the end-to-end (concept to customer) process.

c) Process improvement stems from a disciplined and structured approach.
d) Processes have internal customers (e.g. down-stream recipient), and external customers
(e.g. end-users).
e) Customer expectations drive process improvement.
f) Every business is made up of processes.
g) Every person manages a process.
h) Every person is simultaneously both a supplier to someone and a customer of someone else.
i) Every process has inputs and outputs.
j) Every process has resources and controls.
k) Process characteristics affect output.
l) Processes cross organizational boundaries.
m) Processes are often independent of hierarchical organizational structures.
This leads to a concept, a need and three very pertinent business questions that require answers:
— Concept: every process generates information (voice of the process) that can be used to control and
improve its performance.
— Need: to develop informed perceptive observers using appropriate methodology.
— Questions:
i) Is the process in control?
ii) What is the performance of the process?
iii) Is there evidence of performance improvement?
Proper application of the control chart within an appropriate process management environment provides
the answers to these three questions.
5.2 Business process management
5.2.1 Four key steps
Effective process management involves four key steps:
a) identify and define the process (see 5.2.2);
b) establish responsibilities (see 5.2.3);

© BSI 31 October 2003 7

 BS 5701-1:2003

c) define and establish process controls (see 5.2.4);

d) ensure an ongoing commitment to continual improvement in performance (see 5.2.5).
These four steps will help everyone to:
i) better understand their processes from start to finish;
ii) focus on the principal elements of each process that help contribute to, or detract from, customer
expectations and process efficiency;
iii) continuously monitor, evaluate and improve each process and ensure responsiveness to change;
iv) adopt a structured approach to defining process goals and understanding the best way to go about
achieving them.
5.2.2 Identify and define the process
The process should be identified and developed as follows.
a) Isolate a discrete set of related activities.
b) Identify this named set in terms of inputs and outputs, resources and controls.
c) Construct a process model. This process model is often best expressed pictorially by developing process
flow charts or cascades of block diagrams from simple block diagram models.
This activity will help focus the attention and efforts of process members. It will ensure that they, and
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others on whom they rely (e.g. suppliers of product and information) and support (e.g. customers), share a
common understanding of the nature of the process and the impact of its performance on both the
processor, customers and input suppliers.
5.2.3 Establish responsibilities
Responsibilities should be defined as follows.
— Ensure that there is someone in charge of the process.
— Ensure that individual responsibilities are established for process control and team responsibilities
for process performance improvement.
The primary objectives at this step are to:
a) identify an overall process owner who is ultimately accountable for the process and who manages the
process across functional and organizational interfaces;
b) define roles and responsibilities of everyone involved in process control and process improvement;
c) ensure significant interface interests are represented;
d) make it quite clear who is responsible for initiating and managing reaction to out-of-control signals
and process improvement projects.
5.2.4 Define and establish process controls
Ensure that controls are established to ensure stability of the process and to enable assessment of the
degree to which the process is satisfying customer expectations and business objectives. This involves the
definition of:
a) what to control. Identify control subjects that are relevant. Use selective emphasis. Separate the vital
few from the trivial many. For example, key accounts might have preferential treatment; bottlenecks
might have special consideration and characteristics generally that receive controls appropriate to their
criticality;
b) dominant influencing factors. Although a process can be subjected to many sources of variation often
a few sources predominate. Once located these predominant factors provide the basis for economic and
effective control. Dominant factors to look out for include:
i) information dominant: where non-conformities and other undesirable events arise from frequent job
and requirement changes;
ii) time dominant: where process performance changes with time;
iii) set-up dominant: where the characteristic is highly reproducible once correctly set up;

8 © BSI 31 October 2003

 BS 5701-1:2003

iv) process dominant: where the process output is highly dependent on certain process parameters;
v) processor dominant: where the process is highly dependent on the skill, care and attention of the
people performing the task.
c) the level of control. Choose the appropriate level of control. Examples are:
i) automatic control, e.g. thermostatic control of temperature, laser scanning of fabric for faults during
production, traffic lights and sleeping policemen (humps) in the road;
ii) self-regulation by the person performing the task (e.g. the car driver who drives naturally on the
proper side of the road and obeys traffic signals);
iii) independent check/inspection/test/audit;
iv) information control where selected personnel are provided with reports of errors and absenteeism.
Whatever the decisions made in terms of items a) to c) the methods of process control recommended in this
standard have universal applicability to attribute data characteristics. The control charts described are
easy to construct and interpret. They express a common language readily understandable throughout the
world.
5.2.5 Assess and improve process performance
Achieving and maintaining stability of a process is essential. Control, too, in the sense of meeting budgets,
quota and specifications is equally important. But holding rejects at a budgeted level of, say, 7 %,
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particularly if this loss exceeds the profit margin, obviously does not make good business sense.
The conclusion is that a preoccupation with control should not blind one to the needs, expectations and
opportunities for process performance improvement.
A number of thought stages are involved in successful process improvement projects. These include:
i) understanding the extent to which process performance affects customer (internal and external)
satisfaction and process cost;
ii) appreciating the need to achieve a stable process by applying appropriate controls before assessing
process performance;
iii) estimating “first-run” process performance for individual process stages and for the process as a
whole;
iv) prioritizing opportunities for improvement;
v) achieving and sustaining improved levels.

6 Supporting techniques for effective process control and performance

improvement
6.1 Overview
Process diagnostic and performance improvement activities normally involve the use of supporting
techniques such as process modelling and flow charting, Pareto diagrams and cause and effect diagrams.
These techniques are frequently used in two principal ways. Firstly, in conjunction with the discrete data
control chart in pinpointing and eliminating special causes to resolve adverse out-of-control situations.
Secondly, in the assessment of improvement opportunities and the setting of priorities to reduce common
cause variation to exploit advantageous out-of-control situations.
6.2 Process modelling and flow charting
6.2.1 What is a process?
A process is a set of inter-related or interacting activities that transforms inputs into outputs. An
individual process is best portrayed in simple block diagram form showing inputs, outputs, resources and
controls as shown in Figure 4.

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 BS 5701-1:2003

Controls

Inputs Outputs
PROCESS

Resources

Figure 4 — Basic process model

NOTE 1 Inputs are materials and/or data that are transformed by the process to create outputs.
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NOTE 2 Outputs are the result of transformation of inputs. Outputs can include acceptable customer wants, unacceptable process
wants, waste and process information.
NOTE 3 Controls are regulators and/or influences on the process. Controls embrace procedures, methods, control plans, standards
and legislation.
NOTE 4 Resources are people, equipment, material, space, etc. that are not transformed into output.
An application of a typical basic process model is shown in Figure 5.

work schedule

work instructions

change documents

new parts
updated
items to change ACTION equipment
installed equipment DESIGN information
CHANGE
surplus parts
tools

test equipment

personnel

Figure 5 — Application of the basic process model to the actioning of a product design change

A process can relate to a complete organization in macro format; to a single task in micro format
(as in Figure 5) or to a set of several processes in cascade form (as in Figure 6).

10 © BSI 31 October 2003

 BS 5701-1:2003

Customer Customer
Outputs Inputs Outputs
Inputs
Supplier Supplier

Figure 6 — Business is made up of integrated multistage processes

6.2.2 Integrated process flow diagram

Figure 7 shows a model of an actual integrated process consisting of a number of stages. It shows the
opportunities for monitoring at various stages to provide information in order to control, measure and
improve process performance.
Such monitoring is most beneficial when it takes place on process parameters that have a significant
impact on the output of each stage of the multiple stage process prior to the output being produced. This
facilitates the achievement of first run capability at each stage.
Figure 7 illustrates the distinction between the strategy of detection associated with post-process
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monitoring and the strategy of prevention possible with in-process monitoring. Examples of post-process
monitoring, namely “after the event” product characteristic monitoring, include number and types of fault
at each stage, and post-process stage inspection information. Examples of in-process monitoring, namely
“during the event” process parameter monitoring, include ram velocity, transition pressure, cooling time,
pin positions on extrusion, proximity switch position, router choice, extruder type and trimming blade
status.
Whilst Figure 7 relates to a manufacturing process, the same approach is applicable to any process in any
organization.

ram velocity
extruder rpm transition press
no of pins cooling time router calibration,
pin positions proximity sw. pos. choice R & R capability

vinyl pellets,
moisture extrude prod. rate,
nos & types
puck puck of fault
cycle time,
press nos & types
puck discs
with of fault
flash
trim prod. rate,
disc nos & types
of fault
discs

inspection info
measure
inspected discs

extruder, press, moulds, trimming instrument,

operator pumps, water, blade, operator
operator operator

Figure 7 — Video disc pressing: flow diagram of integrated multistage process

© BSI 31 October 2003 11

 BS 5701-1:2003

6.2.3 Case study: overall process first time capability

In a multistage process, many stage decisions are taken by individual functional business units in order to
optimize the stage for which they have departmental responsibility. In Figure 7, for example, the extrusion
section has operational responsibility for extruding the puck and the press section for pressing the disc.
Each section can well be concerned, almost exclusively, with meeting its own departmental requirements
without real regard to the effect of this on the line as a whole.
It is essential in such a situation to ensure appropriate management, focus and coordination to optimize
overall line performance. An important aspect of this is to have a means of assessing the overall
performance of the integrated line in terms of individual stage process performance. From a quality
viewpoint, such line performance is estimated from its first time capability rather than its logistic
performance.
Figure 8a) shows three scenarios regarding the disposition of faulty items:

Three scenarios: re: disposition of faulty items

Process 1. all scrap
Process 2. all rework
Process 3. some scrap, some rework
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

scrap = 3 scrap = 2

i/p =100 o/p = 97 i/p =100 o/p = 100 i/p =100 o/p = 98
Process Process Process
1 2 3

rework = 2 rework = 3

Logistic Logistic Logistic

Capability = 97 % Capability = 100 % Capability = 98 %
First Run First Run First Run
Capability = 97 % Capability = 98 % Capability = 95 %

Figure 8a) — Illustration of the calculation of logistic capability and first run capability of a
process stage

·
good output
NOTE 1 Logistic capability = -------------------------------- × 100 % .
input
(N – W)
NOTE 2 First run capability (FRC) = --------------------- × 100 %.
N
N = number of items entering the process.
W = waste = number of items that are not processed right first time whatever the ulitmate disposition (e.g. reworked, scrapped).

In Figure 8b), the principles of Figure 8a) are applied to the integrated multistage process illustrated
in Figure 7.

12 © BSI 31 October 2003

 BS 5701-1:2003

scrap = 10 scrap = 3 scrap = 5 scrap = 8

i/p = 100 90 87 82 o/p = 74

extrude press trim inspect

rework = 5 rework = 6

Stages 1 2 3 4

Logistic
capability 90/100 = 90 % 87/90 = 96.7 % 82/87 = 94.3 % 74/82 = 90 %
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

First run
capability 90/100 = 90 % 82/90 = 91.1 % 76/87 = 87.4 % 74/82 = 90 %

Overall process logistics capability = 74/100 = 74 %

Overall process first run capability = (100 – 10 – 8 – 11 – 8)/100 = 63 %
Figure 8b) — Logistic and first run capabilities of the video disc manufacturing process

Figure 8b) shows that whilst stage capabilities look quite respectable, being in the 90 %’s, overall
capabilities of the integrated multistage process are much less attractive. It also shows that the logistic
capability, at 74 % overall, is much more optimistic than the actual first run capability, at 63 % overall.
These examples illustrate the value of the use of overall first run capability to identify process
improvement opportunities, to exploit these, and to verify the effectiveness of any changes made to the
process. They also show the need for an overall management perspective when dealing with multistage
processes rather than the narrow stage view that can be taken by discrete functional departments.
6.3 Pareto analysis
Pareto analysis is a simple graphical technique for displaying the relative importance of features, problems
or causes of problems as a basis for establishing priorities. It distinguishes between the “vital few” and the
“trivial many” and hence focuses attention on issues where maximum quality improvement is secured with
the minimum effort. An example is shown in Figure 9.

© BSI 31 October 2003 13

 BS 5701-1:2003

100 60

Number of occurences
80 50

40
60
Percent

30
40
20
20
10

0 0
l run
s rs es on pra
y pits
pee blis
te tch ati un s
Defect: range g s and scra flot overs g
o sa

Count: 24 17 8 5 5 2 2
Percent: 38.1 27.0 12.7 7.9 7.9 3.2 3.2
Cum.%: 38.1 65.1 77.8 85.7 93.7 96.8 100.0
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

Figure 9 — Example of Pareto diagram for paint process faults

NOTE The faults are arranged in order of incidence in Figure 9. However, other measures of meaningfulness, such as costs, are
sometimes used. For instance, one fatal accident insurance claim can be of much greater significance than dozens of claims for minor
injuries

6.4 Cause and effect diagrams

The cause and effect diagram is often called an Ishikawa diagram (after its Japanese creator) or a fish bone
diagram because of its shape. It displays pictorially possible cause and effect relationships and is intended
to help determine the root cause(s) of a problem. An example of a process-based cause and effect diagram
is shown in Figure 10.

14 © BSI 31 October 2003

 BS 5701-1:2003

small
squeeze
pressure ladle size
mould
size cycle rate of large
time pour
blow
pressure mould pour innoculation
hardness temp.

cracks in
BLEND MOULD MELT POUR brake disc
casting

50/50 hold max

w/s time Mn
Bentonite
30/70 45/55 min
9 % 70/30 tap
temp melt composition
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

% active
clay
12 % 70/30

Figure 10 — Process cause and effect diagram for cracks in a brake disc casting

6.5 Check sheets/tables

The object of a check sheet/table is to gather data in the form of frequencies in order to detect patterns. It
can be in the form of a tally chart in terms of elements of a concern, or opportunity, (e.g. by type of fault)
or by presenting numbers (figures) in a column and row format.
An example of a check sheet/table is shown in Annex A.

7 Relationship with Six Sigma initiatives

A number of organizations have adopted the Six Sigma initiative [1]. The Six Sigma initiative consists of a
performance measure, a performance benchmark and an organization-wide continual improvement
process. Its relevance to this standard lies in:
a) the setting of a world class benchmark of 3.4 adverse events or non-conformities per million
opportunities. This performance level is deemed to be 6 Sigma;
b) the relationship between the Sigma value and non-conformities per million opportunities and
equivalent percentage yield as expressed in Table 2;
c) the organization-wide continual improvement initiative that is statistically based.

Table 2 — Relationship between Sigma value, non-conformities per million opportunities and
percentage yield
Six Sigma Non-conformities (or events) per million Yield
Sigma Value opportunities %
1 691 462 30.8
2 308 538 69.1
3 66 807 93.3
4 6 210 99.4
5 233 99.98
6 3.4 99.999 7

© BSI 31 October 2003 15

 BS 5701-1:2003

This standard provides methods to support the Six Sigma continual improvement initiative in respect of
attribute data. However, the principal purposes of this standard are to engender a mindset and provide
methods that support the targeting on:
1) “preferred value or condition”, rather than something that is “just acceptable” by proposing an offset
or relief from a target condition.
2) zero fault rates, rather than an arbitrary finite value.

8 Typical case study

8.1 Overview
A case study illustrates the wide range of application of attribute charting and associated control and
improvement tools.
Attribute charting is simpler to apply than measured data charting. However, unlike for measured data
where decisions are made against objective criteria, attribute data judgements are frequently taken on a
subjective basis. The effective application of attribute data is thus highly dependent on decisions taken on,
say, whether an item is classified as acceptable or not acceptable or whether a slight flaw or blemish
warrants being termed a non-conformity. This prior need to ensure appropriate decisions are taken is dealt
with in Part 4 of this standard.
Another possible concern with attribute data is the need for a larger sample size than that for measured
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

data, particularly on high performance processes.

8.2 Role of attribute control charting in defining and monitoring an improvement project
8.2.1 Scenario
Consider the trouser manufacturer who has come under pressure from his principal customer, a high
quality retailer, because of the occasional faulty garment getting through 100 % final examination. The
initial reaction of the manufacturer was to put in an additional inspection stage in spite of its adverse
impact on an already very thin profit margin. The retailer’s reaction was quite different: analyse the
situation with a view to eliminating the production of sub-standard garments. This case study illustrates
the initial stages of this activity.
8.2.2 First time quality profile of one trouser line
The quality profile of one of the trouser lines was established over a period of 52 working days. The
particular line consisted of 13 discrete operations and 22 machinists were involved. A simple run chart was
first constructed by plotting the number of faults per working day. A centre-line and upper and lower
control limits were superimposed on this plot. This has the effect of transforming the run chart into an
attribute control chart as shown in Figure 11.

16 © BSI 31 October 2003

 BS 5701-1:2003

70

60
UCL
50
Number of faults

40
Mean
30

20 LCL

10

0
0 10 20 30 40 50 60

Day
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

Figure 11 — First time quality profile of trouser line

NOTE 1 Figure 11 is constructed from the data given in Annex A.

NOTE 2 The mean is determined from the total number of faults divided by the total number of working days = 1 830/52 = 35.
NOTE 3 The upper control limit is determined from: mean + 3 mean = 35 + 18 = 53.
NOTE 4 The lower control limit is determined from: mean – 3 mean = 35 – 18 = 17.
Figure 11 indicates that over this period some 35 faults per day were detected. This excludes those that got
through to the retailer and were detected ex-works. There is no evidence of any improvement in overall
inherent performance. So this performance is likely to continue into the future unless fundamental issues
are addressed. Adverse out-of-control situations arose on four occasions. However, the principal concern
here is the ongoing average of some 35 faults per day. Additional inspection to protect the customer can be
looked upon as a short term expedient. However, this is unlikely to be wholly effective and, in any case, will
have a serious impact on the viability of the business. Quite obviously, there is a need for a drastic review
of the whole operation with a view to significantly improving first time quality performance.
The effective use of this data in achieving improvement in line performance is best achieved by first
separating out the various potential sources of faults, for example here, by process operation (or type of
fault) and by machinist/operator as shown in Annex A.
8.2.3 Principal sources of faults by operation and machinist
8.2.3.1 General
In pinpointing principal sources of faults, it is very important to keep all personnel on-side. One should
avoid stating, for instance, that machinist A is responsible for x % of the total faults. This makes a gross
assumption concerning the cause and can give rise to feelings of antagonism and non co-operation. It is
better to state that x % of the total faults occur with machinist A. Then ask how machinist A can be helped
to increase the quality of her work and hence her earnings and job satisfaction.
In this case in point, all the machinists are on a bonus system based on good work produced. So it is in
everyone’s interest to produce fault-free work. The fact that they are not may be due to a number of causes.
Machinists work in a system that is not of their making. Isolating the source does not imply responsibility.
For example, the effect of previous operations such as panel preparation and cutting, the need to de-skill
certain difficult operations, machine settings and capability and workplace and job design can adversely
affect the output from a particular machinist’s operation.
A preliminary Pareto analysis in terms of operation and machinist is shown in Figure 12 and Figure 13,
respectively. These Pareto diagrams provide the key to establishing priorities for improvement. The next
step is to plot attribute control charts for those operations and machinists that provide the highest
numerical opportunities.

© BSI 31 October 2003 17

 BS 5701-1:2003

100

80 1 500

Number of occurences
60
Percent

1 000
40
500
20

0 0
SS op s ds egs fly s rs
Defect: Lap ar lo ban de l lose End othe
B Insi C

Count: 571 290 250 181 177 124 237

Percent: 31.2 15.8 13.7 9.9 9.7 6.8 13.0
Cum.%: 31.2 47.0 60.7 70.6 80.3 87.0 100.0
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

Figure 12 — Pareto analysis by process operation

100

80 1 500
Number of occurences
60
1 500
Percent

40
500
20

0 0
en dy lie a e e e e t et n m rs
Defect: Su Dore Man Rosa Rit Juli Jan Jun Su Pa Jan Daw Pa Othe

C ount 280 275 250 171 138 108 92 83 75 63 60 52 52 130

Percent 15.3 15.0 13.7 9.3 7.5 5.9 5.0 4.5 4.1 3.4 3.3 2.8 2.8 7.1
C um % 15.3 30.3 44.0 53.4 60.9 66.8 71.8 76.4 80.5 83.9 87.2 90.0 92.9 100.0

Figure 13 — Pareto analysis by machinist/operator

18 © BSI 31 October 2003

 BS 5701-1:2003

8.2.3.2 Lap side seam

Taking the lap side seam operation first where a total of 571 faults have occurred over the period making
up over 31 % of the total faults. Four machinists are involved, Doreen, Sue, Jane and Rita. Their control
charts are shown in Figure 14, Figure 15, and Figure 16.

30
Number of faults 25

20
Doreen
15
UCL
10
Mean
5

0
0 10 20 30 40 50 60

Day
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

Figure 14 — Doreen lapping side seams (5.3 faults per day); Total 275 faults
(15 % of total faults)

10

Jane (2.1/day)
Number of faults

UCL

Mean

0
0 10 20 30 40 50 60

Day

Figure 15 — Jane lapping side seams (2.1 faults per day)

© BSI 31 October 2003 19

 BS 5701-1:2003

25

20
Rita (5.5/day)
Number of faults

15

10 Sue (2.8/day)

0
0 10 20 30 40 50 60

Day

Figure 16 — Sue and Rita lapping side seams

Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

The tentative initial conclusions drawn on lapping side seam performance is that this is the most difficult
operation in the assembly of trousers. Even the most skilled machinists, Sue and Jane, suffer a
between 2 % and 3 % fault rate. Sue left the company early in the project and Jane followed later. The fault
rates borne by Doreen and Rita are about twice that of the other two.
8.2.3.3 Bar loops
There is a big contrast between the performance of the two machinists barring the trouser loops as shown
in Figure 17 and Figure 18. Brenda is seen to be inherently capable of a virtually fault-free performance.
Figure 17 shows that Su initially produced some 8.8 faults per day compared with that of Brenda’s 0.19
faults per day. This represents a difference of some 46 times. Following retraining/recalibrating regarding
the standard required, Su’s performance is seen to improve from 8.8 faults per day to 0.33 faults per day.
Apart from the odd special cause concern, Su is now also inherently capable of a zero defect performance.
The results from this situation should be sufficient to alert management to the potential for a major
breakthrough in business performance. It should be used as an exemplar of what can, and should, be
achieved across this and similar lines in the business.

30

25
Faults per day

20 Su (8.8/day)
UCL

15

10 Su (0.33/day)
after calibration
5
UCL
0
0 10 20 30 40 50 60

Day

Figure 17 — Su barring loops

20 © BSI 31 October 2003

 BS 5701-1:2003

Faults per day

3

1 Brenda (0.19/day)

0
0 10 20 30 40 50 60

Day

Figure 18 — Brenda barring loops

Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

8.2.3.4 Bands on
Mandy is the only machinist on this line putting the bands on. She is looked upon as being a very good
operator. However, the control chart in Figure 19 indicates three poor, but quite different, performance
levels. An initial performance of 4.9 faults/day is followed by a period of 3.1 faults/day that in turn is
succeeded by some 9 faults/day. A similar situation exists on a different line. It appears that shading
(variations in colour shade) of the bands, which are supplied in batches, may be a major contributor to the
variation in, and sub-standard performance of, this operation. This is to be investigated.

20

15
Mandy
Faults per day

10 9.0

4.9
5
3.1

0
0 10 20 30 40 50 60

Day

Figure 19 — Mandy putting the bands on

8.2.3.5 Inside legs

Three machinists are involved in this operation. Rosalie is known to have problems in getting her centres
to meet, as shown in Figure 20, the non-resolution of this has resulted in Rosalie having a long term
average output performance of 3.3 faults per day (171/52) making up some 9.3 % of total faults. On the other
hand, Delia, and Dorry who took over from Delia when she left, are, apart from the presence of the
occasional special cause, inherently fault-free performers.

© BSI 31 October 2003 21

 BS 5701-1:2003

10
9 UC L
8 Rosalie
7
Number of faults

6
5
4 Mean
3
2
1
0
0 10 20 30 40 50 60

Day

Figure 20 — Rosalie machining inside legs

Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

Dorry
2
Number of faults

Delia
1

0
0 10 20 30 40 50 60

Day

Figure 21 — Delia and Dorry machining inside legs

8.2.3.6 Close fly

The consequences of Dawn taking over the job of Shirley, without proper induction, is shown in Figure 22.
Fifteen faults were produced on the first day of what was previously a virtually fault-free performance.
Janet is seen, in Figure 23, to have a fault rate of 1.5 per day. Jean, in Figure 24, has a different profile.
Her performance varies in phases. She appears inherently capable of producing 1.2 faults per day but this
can double under certain process conditions.

22 © BSI 31 October 2003

 BS 5701-1:2003

15

Faults per day

10 Dawn

Shirley

0
0 10 20 30 40 50 60

Day

Figure 22 — Shirley and Dawn closing the fly

Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

5
Janet (1.5/day)
Faults per day

0
0 10 20 30 40 50 60

Day

Figure 23 — Janet closing the fly

© BSI 31 October 2003 23

 BS 5701-1:2003

5
Jean (1.8/day actual)
4
Faults per day

[1.2/day achievable]
3

0
0 10 20 30 40 50 60

Day

Figure 24 — Jean closing the fly

Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

8.2.3.7 Situation summary

This case study illustrates the role of attribute charting in pinpointing opportunities and establishing
priorities for significant improvement in business performance. Although dealing with a specific situation,
the approach is generally applicable to any business process.
Concentration here has been largely on highlighting ongoing common (inherent) causes where the most
substantial scope for improvement occurs. Special causes have not been so addressed in this study as the
study was to some extent retrospective. The primary reason for separating out special causes is that they
can be dealt with (and are seen to be dealt with) operationally, at source, at the time of the event. The
operational value of the control chart in achieving this is obvious from the expectation of a positive reaction,
in practice, to each out-of-control situation shown in the control charts portrayed.
Two principal issues are involved, one relating to machinists and the other to management.
A necessary prerequisite is to keep operational personnel and particularly the machinists involved
on-board throughout. This is readily achieved if criticism of their relative abilities is avoided and it is put
over to them that it is their own interest, financially and job satisfaction wise to seek out and remove the
reasons for fault generation. This will, for example, avoid requiring them to undertake the time consuming
task of rework with the consequent hassle and loss of bonus involved.
Bringing the data out in the open in such a pictorial manner and by separating out special causes from ones
inherent to the operation makes it far less likely for rational management to continue to accept that the
currently achieved, and budgeted, norm is really par for the course. It is intended to provoke and stimulate
immediate fundamental changes in the way business is conducted. Regardless of the nature of the process
the use of simple attribute control charting, in this manner, is supportive of a transparent, focused and
decisive style of management aimed at achieving corporate objectives.

24 © BSI 31 October 2003

Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

Annex A (informative)
© BSI 31 October 2003

Base data for case study of Clause 8

Table A.1 — Base data for case study of Clause 8
Process Machinist Day
Yokes Teresa 0 0 0 3 1 0 0 0 1 0 2 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0
Bk Seams Glen 0 0 0 1 2 4 3 1 2 2 0 0 0 1 1 2 0 2 4 3 0 0 0 0 0 0 1
Roll Pkt Dawn1 0 0 0 0 0 0 0 0 2 1 0 0 0 2 3 1 1 0 0 1 1 0 0 0 2 2 1
Bind Pam 2 2 3 1 0 2 0 1 0 0 0 0 1 0 0 0 0 1 2 3 2 1 0 0 1 2 1
Serge Fly Pat 0 0 1 0 0 1 0 0 4 2 0 4 1 1 5 3 3 6 3 2 0 0 0 1 1 0 0
Close Fly Jean 0 2 2 1 0 2 5 1 4 3 2 3 2 3 2 2 2 1 1 0 6 2 6 3 2 3 1
.. Janet 3 2 3 3 1 5 3 0 3 2 2 1 1 1 0
.. Shirley 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
.. Dawn2
Lap SS Doreen 1 0 1 2 7 0 4 7 6 8 4 2 6 10 5 4 3 4 2 3 5 4 16 8 5 5 3
.. Sue 2 2 1 0 2 0 1 1 5 7 6 7 5 5 3 4 2 3 5 4 2 0 0 1 3 3 1
.. June 1 0 1 0 0 2 0 2 0 0 1 3 3 2 2
.. Rita
Bands Mandy1 3 3 5 15 2 0 3 5 4 11 5 2 3 4 10 4 2 4 4 5 7 5 15 4 2 8 3
Ends Julie 0 1 0 1 1 2 3 1 7 2 0 0 3 1 3 4 1 0 2 2 3 1 3 0 2 3 2
.. Sharon 0 0 3 3 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1 3 2 0 0 0 0 0
Barr Loops Su 14 7 1 24 4 2 7 7 16 12 9 2 4 13 8 10 5 3 13 16 23 3 6 8 11 8 2
.. Brenda 3 0 4 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0
Fly Tack Jeanet 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Zip Stop Mandy2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
.. Heather 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
.. Jeanet 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
In Legs Rosali 10 2 8 4 5 6 3 6 0 0 2 1 0 3 7 4 3 2 3 6 6 2 4 4 6 5 2
.. Delia 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0

BS 5701-1:2003
.. Dorry
Total 37 19 29 55 24 19 29 30 51 48 31 21 31 47 53 42 24 36 42 50 62 22 53 37 39 43 19
25
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI
Table A.1 — Base data for case study of Clause 8 (continued)
26

BS 5701-1:2003
Process Machinist Day
Yokes Teresa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 12
Bk Seams Glen 1 0 0 0 0 0 0 0 0 0 0 1 1 1 3 0 3 1 0 0 4 4 0 0 1 49
Roll Pkt Dawn1 0 0 1 2 3 4 16 3 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 52
Bind Pam 2 0 2 1 1 1 2 2 0 1 2 0 1 2 2 1 0 0 1 2 1 2 0 0 1 52
Serge Fly Pat 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 3 3 2 0 8 1 0 3 1 63
Close Fly Jean 3 1 0 2 1 2 0 2 1 1 2 0 1 2 2 0 2 0 3 1 0 1 2 0 2 92
.. Janet 2 2 0 1 2 1 2 2 3 1 2 4 1 2 3 1 1 0 0 0 0 0 0 0 0 60
.. Shirley 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
.. Dawn2 15 7 0 0 0 0 1 0 0 0 23
Lap SS Doreen 3 2 3 3 2 3 4 3 6 3 5 2 8 3 0 6 3 25 17 5 15 13 5 6 5 275
.. Sue 75
.. June 3 2 2 7 4 0 3 3 2 2 0 1 2 2 0 1 3 9 3 0 2 4 2 5 4 83
.. Rita 2 3 1 2 2 2 6 6 3 3 3 5 2 4 3 6 4 5 11 7 12 11 5 7 23 138
Bands Mandy1 3 2 10 2 4 2 5 1 6 3 5 3 4 3 4 3 4 1 2 0 11 6 11 4 13 250
Ends Julie 3 0 1 4 2 4 2 3 2 2 1 0 0 1 0 4 2 7 3 1 8 6 3 0 1 108
.. Sharon 0 0 0 0 0 0 0 0 0 0 16
Barr Loops Su 14 6 5 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 280
.. Brenda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
Fly Tack Jeanet 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 5
Zip Stop Mandy2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
.. Heather 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
.. Jeanet 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1
In Legs Rosali 0 7 3 2 3 3 6 5 0 3 1 7 4 0
2 3 0 1 2 1 2 3 6 1 2 171
.. Delia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
.. Dorry 0 0 0 0 0 0 0 0 0 7 0 7
Total 37 25 28 36 24 22 46 31 24 20 23 24 25 21 20 40 32 52 44 18 64 52 34 35 60 1 830
© BSI 31 October 2003
 BS 5701-1:2003

Bibliography

British Standards
BS 5701-2, Guide to quality control and performance improvement using qualitative (attribute) data —
Part 2: Fundamentals of standard attribute charting for monitoring, control and improvement.
BS 5701-3, Guide to quality control and performance improvement using qualitative (attribute) data —
Part 3: Technical aspects of attribute charting: special situation handling.
BS 5701-4, Guide to quality control and performance improvement using qualitative (attribute) data —
Part 4: Attribute inspection performance control and improvement.
BS 5702-1:2001, Guide to statistical process control (SPC) charts for variables — Part 1: Charts for mean,
median, range and stardard deviation.

Other publications
[1] TRUSCOTT, W. Six Sigma: Continual improvement for businesses. Butterworth-Heinemann: 2003.
Licensed Copy: Ekrem ARSLAN, Bechtel Ltd, 14 March 2004, Uncontrolled Copy, (c) BSI

© BSI 31 October 2003 27

 BS 5701-1:2003

BSI — British Standards Institution

BSI is the independent national body responsible for preparing
British Standards. It presents the UK view on standards in Europe and at the
international level. It is incorporated by Royal Charter.

Revisions
British Standards are updated by amendment or revision. Users of
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