WHAT IS QUALITY?
28
Quality Management
 Introductions:
Tutor Name: Tom Phillips
 Delegate Name
 Organisation
 Present Position
 Length of Time
 Purpose for attending course

2
Quality Management
 Question
1: In any organisation
who is considered to be the
person responsible for Quality?
 Answer:
3
Quality Management
 Question
1: In any organisation
who is considered to be the
person responsible for Quality?
 Answer:
Quality Manager
4
Quality Management
 Question
2: In any organisation
who in fact is actually responsible
for Quality?
 Answer:
5
Quality Management
 Question
2: In any organisation
who in fact is actually responsible
for Quality?
 Answer:
Everyone
6
Quality Management
 Question
3: In an organisation who
should be appointed and given
authority by top management to
manage, monitor, evaluate and
coordinate the QMS?
 Answer:
7
Quality Management
 Question
3: In an organisation who
should be appointed and given
authority by top management to
manage, monitor, evaluate and
coordinate the QMS?
 Answer:Management Representative
8
Quality Management
 Question
4: In any organisation
who is ultimately responsible for
Quality, whether it be for a
product or service?
 Answer:
9
Quality Management
 Question
4: In any organisation
who is ultimately responsible for
Quality, whether it be for a product
or service?
 Answer:
Chief Executive Officer/
Managing Director
10
Quality Management

What is Quality?

Definition from ISO 9000:2005 (3.1.1)
Quality is the degree to which a set of
characteristics fulfils requirements
11
Quality Management

What are these characteristics?

Definition from ISO 9000:2005 (3.6.1)
Characteristic is a distinguishing feature

There are various classes of characteristics
such as the following:
12
Quality Management

Physical (e.g. Mechanical, electrical, chemical, biological)

Sensory (e.g. Related to smell, touch, taste, sight, hearing)

Behavioural (e.g. Courtesy, honesty, veracity)

Temporal (e.g. Punctuality, reliability, availability)

Ergonomic (e.g. Physiological, related to human safety)

Functional (e.g. Maximum speed of a motor vehicle, aircraft)

In reality quality involves a Way of Life
13
Quality Management

What exactly is Quality?

It differs according to client requirements

It is defined according to its specific characteristics

It is tangible

Quality is therefore inherent or existing in a product
or service having certain defined characteristics
14
Quality Management

What are the characteristics of Quality?
 Question 1: Does the product or service conform to
requirements
 Question 2: Does the product or service conform to
specifications
 Question 3: Is the product or service deemed suitable
and fit for the purpose it was intended to
fulfill

Quality can therefore be considered intangible until the characteristics
have been agreed between the seller and the purchaser.
15
Quality Management

Conformance to Requirements means:

When the purchaser’s requirements are clearly stated
and the product or service is in full compliance

Requirements not stated by the purchaser but necessary
for specified or intended use

Statutory and regulatory requirements applicable to the
product or service

Any additional requirements considered necessary by the
seller
16
Quality Management

Conformance to Specifications means:

When purchaser specifications are provided and
agreed upon by the supplier

The purchaser’s criteria are clearly stated for the
product or service

The purchaser and supplier agree on and sign for
acceptance of contractual obligations
17
Quality Management

Fit for Purpose means:

Purchaser has no specific requirements or specifications

The purchaser and supplier agree on the application and
use of the product or service

The drawing up and acceptance of contracts or orders
between purchaser and supplier are heavily reliant on
good communication and contract review processes
18
Quality Management

Question 6: Do top management
play a part in quality, and if so
what part should they play?

Answer: (Flip Chart)
19
Quality Management

The Role of Top Management

Top Management is responsible for:

Showing leadership, commitment and an active
involvement in the development and maintenance of
an effective QMS

Establishing a vision, policies and strategic objectives
consistent with the purpose of the organisation

Communicating organisational direction and values
regarding quality and the QMS

Providing resources that are necessary to support the
organisation’s strategic plans
20
Quality Management

The Role of Top Management contd.

Top Management is responsible for:

Promoting a commitment to quality throughout the
organisation

Establishing a quality policy that is appropriate to the
organisation

Identifying the processes that provide added value to
the organisation

Analysing and optimising the interaction of processes
21
Quality Management

The Role of Top Management contd.

Top Management is responsible for:

Creating an environment that encourages the
involvement and development of people

Establishing a clear understanding of customer’s needs
and expectations (both internal and external customers)

Identifying the applicable statutory and regulatory
requirements

Identifying the data required as well as the management
review team and conducting review meetings for
effective management of the QMS
22
Quality Management

The Role of Top Management contd.

Top Management is responsible for:

Promoting ethical, effective and efficient compliance
with current and prospective requirements

Leading the organisation toward continual
improvement of its performance and of the Quality
Management System

Identifying the current and potential impacts on society
in general and the local community in particular of its
products, processes and activities
23
Quality Management

Question 7: Do employees play
a part in quality, and if so what
part should they play?

Answer: (Flip Chart)
24
Quality Management

The Role of the Employee

Employees are responsible for:

Applying a Quality Ethic to the way they carry out their
work activities with regard to their understanding of
their customer (internal and external) needs and
expectations

Performing their work activities in a controlled manner
that ensures their customer’s satisfaction

Working within the boundaries and framework
established by the QMS
25
Quality Management

The Role of the Employee contd.

Employees are responsible for:

The ownership of all processes under their control
Performing their processes in logical sequence/steps
that transfers inputs to outputs
Identifying that each process has a supplier and a
customer
Realising that processes can operate crossfunctionally i.e. across departments



26
Quality Management

The Role of the Employee

Employees are responsible for:
27

End of What is Quality
Module
28
STATISTICAL TECHNIQUES
MODULE
Statistical Techniques
ISO 9001:2000 Clause 8.1 General
The organisation shall plan and implement the
monitoring, measurement, analysis and improvement
processes needed
 to demonstrate conformity of the product
 to ensure conformity of the quality management system to
continually improve the effectiveness of the quality management
system
This shall include determination of applicable methods,
including statistical techniques, and the extent of their
use.
30
Statistical Techniques
There is no thing as constancy and consistency
in the real world and things are continually on the
move and in the process of change.
Within industry as well as in real life everything
that happens (all work can be considered as being
a series of processes) is due to some influence or
other, whether good or bad.
31
Statistical Techniques
The capability of a process can be defined by the
inherent variation that takes place especially in
regard to tolerances.
All processes are affected by variation and certain
reasons or causes, and these causes can be
categorised into assignable causes and unassignable
(chance) causes.
Therefore in industry, Statistical Techniques, or in
other words, the use of scientific methods for
investigating, controlling and evaluating processes
are used.
32
Statistical Techniques
The improved measurement and analysis techniques
that have been developed were for the purpose of
establishing “best practice”.
The principle of continual improvement has resulted
in additional methodologies and techniques being
developed by industry.
33
Statistical Techniques
In order to assist in decision-making regarding process
improvement, certain information and data must be
collected for analysis.
The collection of data is an integral part of any statistical
study, and must be done carefully and accurately.
Data comes from many varied sources and for statistical
studies includes two basic types:

Variable data - length, mass, time etc.

Attribute data - % rejects, defects per unit etc.
34
Statistical Techniques
There are two fundamental considerations when it comes
to errors in an organisation, they are either management
controllable or they are operator/employee controllable.
Therefore operators/employees must be put in a state of
control by ensuring the following:




Knowing what is expected of them.
Knowing clearly what are they expected to do?
Knowing what their actual performance is and
Knowing how they are able to regulate it.
If these needs are met (without exception) the operator is
in a state of control, but if these criteria are not being met
the resulting defects are management controllable.
35
Statistical Techniques
Frequency Distribution

Graphical presentation
of frequency distribution
makes it possible to
observe variation
14
12
10
8
6

-
Variation patterns depend
on two main components
Assignable causes
Unassignable (Chance)
causes
4
2
0
495
497
496
499
498
501
500
503
502
505
504
36
Statistical Techniques
Frequency Distribution


The fundamental shape
which many statistical
tables are based on is the
"Bell shaped" or "Normal
Distribution" curve as
shown here.
Unfortunately the normal
distribution curve is not
often observed in industrial
or commercial data.
37
Statistical Techniques
Scenario

A camera was set up in a controlled environment
and 50 rolls of 24 exposure films were used to
photograph a still life picture.

The experiment was intended to determine if there
were any defective films being sold to the general
public.

The films were then developed and the number of
prints for each roll recorded as follows:
38
Statistical Techniques
Scenario
13
14
10
10
15
13
13
13
15
14
11
16
9
10
15
12
10
11
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13
11
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24
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10
9
8
7
6
5
4
3
2
1
0
5
10
15
39
Statistical Techniques
Scenario Conclusions




Exposures that developed successfully into a
print, closely resembles a normal distribution
curve.
There are no assignable causes evident.
Film quality is consistent.
The cause for such low numbers of exposure
resulting in a successful print must lie with some
another cause other than the film.
[This would be cause for a further investigation]
40
Statistical Techniques
Frequency Distribution
 Industrial data produces patterns for
quality characteristics in many varied
forms
−
Cooked
− Triangular
− Bimodal
− Rectangular
− Skewed
− Peaked
41
Statistical Techniques
Frequency Distribution
 Cooked
Possible reasons: Data has been modified and/or tampered with.
42
Statistical Techniques
Frequency Distribution
 Triangular
Possible Reasons: Operator fatigue and/or machine tool wear.
43
Statistical Techniques
Frequency Distribution
 Bimodal
Possible Reasons: The output from two processes were mixed.
44
Statistical Techniques
Frequency Distribution
 Rectangular
Possible Reasons: Multiple processes mixed and/or have drift in
a process.
45
Statistical Techniques
Frequency Distribution
 Skewed
Possible Reasons: Shelf life and/or light bulb life
46
Statistical Techniques
Frequency Distribution
 Peaked
Possible Reasons: Well controlled processes and/or incorrect
measurement resolution.
47
Statistical Techniques
Frequency Distribution
Normal Distribution Characteristics





Central tendency
Standard deviation
Variation pattern
It is of paramount importance for statistical
interpretation that data be reliable
Time spent planning data collection saves time
during data analysis
48
Statistical Techniques
Frequency Distribution

Normal Distribution
49
Statistical Techniques
Graphs
There are four major graphs




Line graphs
Bar graphs
Pie graphs
Pictorial graphs
NOTE: Histograms and Pareto graphs are bar graphs
50
Statistical Techniques
Graphs




When compiling graphs it is important to note that
the graph needs to be understood by the person
plotting the graph as well as those who use the
results.
Therefore the past and present characteristics
should easily be seen.
Graphs used so far have been used to indicate
an existing problem
It is important to find out about process changes
to begin to predict failure
51
Statistical Techniques
Graphs




Changes in a process can be dynamic, taking
place over a period of time.
The impact of various factors cause the change
Changes themselves also vary over time and
must be studied.
Two types of charts can be used
- Run Charts
- X bar R Charts (control Charts)
52
Statistical Techniques
Graphs - Run Charts




Used to plot measurements, item by item as a
process continues
Used to show the presence of trends
Indicates changes in a process
Not as effective as a control chart
53
Statistical Techniques
Graphs – Run Charts
J
F
M
A
M
J
J
A
S
O
N
D
Months
54
Statistical Techniques
Graphs – Xbar R Charts
 These graphs give a continuous picture of
process variation
 The graphs are always used together
 Data is reported on small constant size
subgroups (usually between 2 and 5 )
55
Statistical Techniques
Graphs – Xbar R Charts
56
Statistical Techniques
Technique for the Interpretation of Graphs
 Select a sub-group size (2 – 5)
 Select the frequency of measurement
 Ensure enough sub-groups are taken to include
any major variation in the process
 Collect at least 25 sub-groups of data
57
EXAMPLE:
SA POST OFFICE
Statistical Techniques
Example – SA Post Office

The South African Postal Services have recently received
some complaints regarding its turn-around times at a local
post office. Customers are complaining that the organisation
is under-staffed and this has caused delays in parcel delivery
times. The Post Master has been asked by the Regional
Manager to establish what the real delivery times are to
understand whether extra staff are justified. The manager
decides to collect records of the date on which the item is
received and the date on which the parcel is made available
for collection at its destination. He deducts the two times for
each delivery and records the elapsed time for five of the
deliveries on each day, being careful to select records that
spread across the entire day. He uses 25 such samples for
the exercise.
59
Statistical Techniques
Example - SA Post Office












Record Administrative Details to include:
Date
Time
Process
Division / Department / Unit
Customer
Supplier
Shift
Activity/part/batch #
Name of verifier/inspector
Characteristic
Unit of measurement
Sampling frequency
Transfer the sample data onto a control chart
60
Statistical Techniques
Example - SA Post Office
D
01
D
02
D
03
D
04
D
05
D
06
D
07
D
08
D
09
D
10
D
11
D
12
D
13
D
14
D
15
D
16
D
17
D
18
D
19
D
20
D
21
D
22
D
23
D
24
D
25
15
15
16
16
11
13
14
12
15
16
16
14
16
16
16
15
15
16
16
11
13
14
14
16
17
16
10
17
16
14
14
14
15
16
16
16
16
17
16
17
16
10
17
16
14
13
15
18
16
16
14
17
15
16
12
14
10
15
16
17
12
16
17
18
16
14
17
15
16
12
13
14
15
15
14
14
16
14
16
12
14
15
16
17
17
14
15
10
15
19
14
16
14
16
12
13
15
16
17
16
10
17
16
16
13
12
15
14
14
17
15
15
16
19
16
10
17
16
16
13
16
17
15
15
16
Duration of delivery times (days) over a 25 day period
61
Statistical Techniques
Example - SA Post Office
 Calculate the mean of each sub-group (x Bar) and the range of each sub-group (R)
 X Bar = (X1 + X2 + X3 + X4 + X5)/number of samples
 R = Largest sample value - smallest sample value
Sample # Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
1
15
15
16
16
11
13
14
2
16
10
17
16
14
14
14
3
14
17
15
16
12
14
10
4
14
16
14
16
12
14
15
5
10
17
16
16
13
12
15
X Bar
13.8
15.0
15.6
16
12.4
13.4
13.6
High
16
17
17
16
14
14
15
Low
10
10
14
16
11
12
10
Range
6
7
3
0
3
2
5
62
Statistical Techniques
Example - SA Post Office
Sample #
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
1
15
15
16
16
11
13
14
2
16
10
17
16
14
14
14
3
14
17
15
16
12
14
10
4
14
16
14
16
12
14
15
5
10
17
16
16
13
12
15
X Bar
13.8
15.0
15.6
16
12.4
13.4
13.6
High
16
17
17
16
14
14
15
Low
10
10
14
16
11
12
10
Range
6
7
3
0
3
2
5
63
Statistical Techniques
Example - SA Post Office
 Calculate the mean of the process
 XBarBar = (X1Bar+X2Bar+....)/No. of sub-groups
 XBarBar = 14.94
Sample #
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
1
15
15
16
16
11
13
14
2
16
10
17
16
14
14
14
3
14
17
15
16
12
14
10
4
14
16
14
16
12
14
15
5
10
17
16
16
13
12
15
X Bar
13.8
15.0
15.6
16
12.4
13.4
13.6
High
16
17
17
16
14
14
15
Low
10
10
14
16
11
12
10
Range
6
7
3
0
3
2
5 64
Statistical Techniques
Example - SA Post Office
 Calculate the mean of the range chart
 RBar = (R1+R2+R3....)/No. of sub-groups
 RBar = 3.52
Sample #
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
1
15
15
16
16
11
13
14
2
16
10
17
16
14
14
14
3
14
17
15
16
12
14
10
4
14
16
14
16
12
14
15
5
10
17
16
16
13
12
15
X Bar
13.8
15.0
15.6
16
12.4
13.4
13.6
High
16
17
17
16
14
14
15
Low
10
10
14
16
11
12
10
Range
6
7
3
0
3
2
5 65
x
Statistical Techniques
Example - SA Post Office
20.00
14.94
X BarBar
X Bar
10.00
RBar
R-LCL
Range
0.00
3.52
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
66
Statistical Techniques
Example - SA Post Office





Calculate the statistical control limits for x chart
UCL = Process mean (xBarBar) + 3 standard deviations
Where 3 standard deviations = A2 x RBar (A2 is a constant)
Upper Control Limit = 14.94 + 0.577 x 3.52
UCL = 16.97
Use with
xBar
chart
Use with
individual
chart
Use with
R chart
Sample Size
A2
d2
D3
D4
2
3
4
5
6
7
1.880
1.023
0.729
0.577
0.483
0.419
1.128
1.693
2.059
2.326
2.534
2.704
0
0
0
0
0
0.076
3.267
2.574
2.282
2.114
2.004
1.924
67
Statistical Techniques
Example - SA Post Office





Calculate the statistical control limits for x chart
LCL = Process mean (xBarBar) - 3 standard deviations
Where 3 standard deviations = A2 x RBar (A2 is a constant)
Lower Control Limit = 14.94 - 0.577 x 3.52
LCL = 12.91
Use with
xBar
chart
Sample size
2
3
4
5
6
7
A2
1.880
1.023
0.729
0.577
0.483
0.419
Use with
individual
chart
d2
1.128
1.693
2.059
2.326
2.534
2.704
Use with
R chart
D3
0
0
0
0
0
0.076
D4
3.267
2.574
2.282
2.114
2.004
1.924
68
Statistical Techniques
Example - SA Post Office
20.00
16.97
14.94
X BarBar
12.91
X-UCL
X-LCL
10.00
X Bar
RBar
R-LCL
Range
3.52
0.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
69
Statistical Techniques
Example - SA Post Office
•
•
•
•
Calculate the statistical control limits for R chart
UCL = D4 x RBar i.e. UCL = 2.114 x 3.52
LCL = D3 x RBar i.e. LCL = 0 x 3.52
UCL = 7.44
LCL = 0
Use with
xBar chart
Sample size
2
3
4
5
6
7
A2
1.880
1.023
0.729
0.577
0.483
0.419
Use with
individual
chart
d2
1.128
1.693
2.059
2.326
2.534
2.704
Use with R
chart
D3
0
0
0
0
0
0.076
D4
3.267
2.574
2.282
2.114
2.004
1.924
70
Statistical Techniques
Example -SA Post Office
Control Chart
20.00
X BarBar
X-UCL
X-LCL
X Bar
10.00
RBar
R-UCL
R-LCL
Range
0.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
71
Statistical Techniques
Exercise 14
72
Statistical Techniques
Applications of xBar R charts
 Service examples
−
Time taken for delivery of items
− Arrears in processing documents
− Claims processing
− Turn-round time for claims processing
− Analysis of course admission figures
− Downtime on computer networks
− Response times for complaints
− Time to respond to telephone calls
73
Statistical Techniques
Applications of xBar R charts
 Manufacturing examples
−
−
−
−
−
−
Machine response times
Electricity, water consumption
Time taken to set-up machine centres
Time to cure
Length, weight measurement
Tensile values
74
INTERPRETATION OF
CHARTS AND GRAPHS
Statistical Techniques
Interpretation of Graphs

R Charts
One point above the control limit
76
Statistical Techniques
Interpretation of Graphs

R Charts
One point below the control limit
77
Statistical Techniques
Interpretation of Graphs

R Charts
Seven consecutive points increasing or decreasing
78
Statistical Techniques
Interpretation of Graphs

R Charts
x
Fourteen consecutive points alternating up or down
79
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
C
C
B
A
One point beyond zone A of lower or upper control limit
80
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
C
C
B
A
Eight consecutive points on one side of the mean
81
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
C
C
B
A
Seven consecutive points increasing or decreasing
82
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
C
C
B
A
Two out of three consecutive points in zone A on one
side of the mean. The odd point could be anywhere.
83
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
C
C
B
A
Four out of five consecutive points in zone B or beyond,
on one side of the mean. The odd point could be anywhere.
84
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
x
C
C
B
A
Fourteen consecutive points alternating up or down
85
Statistical Techniques
Interpretation of Graphs

xBar Charts
A
B
C
C
B
A
Fifteen consecutive points in zone C
86
Exercise 15
87
Statistical Techniques
Process Capability


A process capability study is carried out to establish the
quality that a particular process may be expected to
produce under defined conditions
The results may be used for many purposes including
−
−
−
−
−
To determine if the process can meet the established
specification
To provide factual data to set realistic specs.
To determine economic maintenance cycles
To serve as final acceptance qualification on new
equipment/processes
To determine an industry's capabilities and set service or
product tolerances to aid the consumer
88
Statistical Techniques
Process Capability
A capability index (Cp) indicates the ratio of the tolerance
to the variation of the process
89
Statistical Techniques
Process Capability
Planning a capability study






Can assignable causes be controlled?
What type of data is required?
How much data is required?
In what format should the data be collected?
A process capability study includes tests for normality and
determines an average and standard deviation
Testing for normality


Is the process of testing the distribution to establish whether it
is close enough to a normal distribution
Only when all assignable causes are removed can
capability be calculated
90
Statistical Techniques
Process Capability
 Since Cp is a ratio it does not indicate the process location
relative to the specification limits
Variation
Tolerance
Upper Specification Limit
Lower Specification Limit
91
Statistical Techniques
Process Capability
•
•
To establish the process location, relative
to the nominal value we use the CpK
capability index
Process capability studies
−
−
Can be compiled manually (difficult/needs experience)
Computer programs are available
92
Statistical Techniques
Process Capability
93
Statistical Techniques
Process Capability
 Process capability
•
•
•
•
•
•
Cp = Tolerance/(6 x RBar/d2)
Cp > 1 indicates a capable process
Capable processes have an inherent ability to satisfy
specification limits
Cpk gives an indication of the position of the process
relative to the nominal value
Cpk = (USL + xBarBar)/3 sigma or (xBarBar - LSL)/3
sigma
Where sigma = RBar/d2
94
Statistical Techniques
Techniques
Statistical
Statistical
Techniques
Exercise 13
Exercise 16
96
 End
of Statistical Techniques
Module
97
QUALITY COSTING
MODULE
Quality Costing

Improvement in product/service quality presents
opportunity for improved profitability

Without measuring quality costs it is difficult to focus
on the areas of highest potential improvement.

Past studies (eighties) have shown 25%-30% of
revenue lost to poor quality

Recent comments indicate nothing has changed!

30 cents/$ in USA software development is
attributable to inappropriate development
99
Quality Costing
Similar returns are typically only achievable through
massive increases in revenue
Profit
Waste
Cost of doing business

100
Quality Costing


Mistakes are made and
people are paid to make them?
101
Quality Costing

“Mistakes” = non-conformance

Costs of non-conformance are the focus of quality
costing efforts

Don’t apply more effort than the process is capable
of yielding
102
Quality Costing

(a) = Cost of evaluation and prevention

(b) = Cost of non-conformance
x = minimum input for maximum output
(a)
Costs

x
(b)
Tim e
103
Quality Costing

ISO 9004: 2000 6.3.4

By reporting quality management system
activities in financial terms, management will
receive the results in a common language for
all functions within the organization
104
Quality Costing

Most Businesses use a number of parameters to
measure performance

Quality costing measurements should be related to

Losses associated with customer satisfaction

Losses associated with process efficiencies

Societal quality losses
105
Quality Costing
•
Benefits
•
A system for analysing the organisations performance,
regarding losses due to error and inconsistency
• Identifies activities that should be targeted to reduce
losses
• Provides insight into activities that will benefit from
improvement programs
• Measures the effectiveness of the overall Management
System
• Customer requirements can be economically met
• Total costs may be reduced
106
Quality Costing
DEFICIENCIES
Introduction of
formal system
Business
process
measurement
Continual
improvement
TIME
107
Quality Costing
•
Introduction of a quality culture means all
staff look for and report non-conformities
•
Initially expect an increase in the number
of non-conformities reported
108
Quality Costing
No. of NC's
Time
109
Quality Costing
•
Categories
 Immeasurable
and Measureable Costs
 Immeasurable
Loss of Customer satisfaction
 Societal loss

•
Measurable
 Losses
associated with efficiency and product
conformity
110
Quality Costing
•
Three types of quality costs
 Prevention,
appraisal and failure
 Prevention Costs
Costs of any action taken to investigate, prevent
or reduce the risk of non-conformance
 Quality Planning
 Quality Auditing
 Training
 Improvement Programs
 Liability Insurance

111
Quality Costing
Three types of quality costs
 Appraisal
Costs
Inspections (Receiving, Process, and Final)
 Equipment maintenance
 Calibration of measuring equipment
 Stock evaluation
 Approvals and Endorsements

112
Quality Costing
Three types of quality costs

Failure costs - Internal Failures
 Scrap
 Rework
 Retest
 Down Time
 Down Grading
113
Quality Costing
Three types of quality costs
 Failure
costs - External Failures
Returns
 Complaints
 Claims and Refunds
 Discounts on substandard material

114
Quality Costing
•
The QMS is preventive and offers
increased confidence in product/service
quality
−
cost of non-conformance is reduced
− levels of inspection may consequently be
reduced
− cost of appraisal may consequently be
reduced
115
Quality Costing
Cost of nonconformance
Cost of appraisal
Profit
Cost of nonconformance
Cost of appraisal
Cost of prevention
Cost of prevention
Before introduction of a QMS
After introduction of a QMS
116
Quality Costing
Preparation
•
•
•
Identify areas where costs can easily be
measured
Get the Financial Department Personnel on
your side
Assess information available from the Financial
Department
117
Quality Costing
Management Approval
•
Report to Management in the language they
understand (Financial)
•
Prepare an exercise in conjunction with the
Finance Department
•
Indicate the benefits
•
Show the gains
•
Management approval should be in the form
of a policy on Quality Costs
118
Quality Costing
•
Schedule for Implementation
−
Provide a plan (time table, project plan etc.)
− Identify the areas where the system will be
initiated
− Identify the type of data required and how it
will be collected and recorded
− Allow enough time to collect meaningful
information
•
Prior to implementation review all details
and results
119
Quality Costing
•
•
Data collection should be standardised
throughout the company
Data can be collected from:
−
−
−
−
−
−
−
Credits passed
Expense claims
Rework reports
Inspection reports
Purchase orders
Scrap reports
Production cost reports
120
Quality Costing
•
Collecting Data
−
Usually in the beginning this is performed manually
 Typical sources being
Corrective action reports
 Customer complaints
 Defect reports

•
Investigate
−
−
Time spent in failure investigation
Costs of material and time spent on repair work
121
Quality Costing
• Reporting
−
Presentation of costs will usually be broken
into categories covering
Prevention
 Appraisal
 Failure

Internal
 External

122
 End
of Quality Costing
Module
123