This document provides information about a quality management diploma program. It discusses the contents and modules of the diploma, which covers topics like quality management systems, standards, tools, and strategies. The diploma is offered both in-person and online, and provides an internationally recognized qualification in quality management.

1. Quality management diploma
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I. Contents of quality management diploma
==================
The Diploma in Quality Management course is an internationally recognised qualification and is
the only chartered qualification of its kind offered in Ireland. The course is held one and two
evenings per week by classroom or by distance and offers you the latest knowledge and skills to
plan and manage the quality management function of an organisation. Suitable for professionals
working in services or product sectors. Past students have come from health, pharma, production
plant sectors and related areas.
The Diploma in Quality Management course is recognised by Ofqual to level 5 in England,
which is broadly equivalent to level 7 in the Republic of Ireland. You will learn how to best
optimise quality in the context of different types of organisations and structures. If you wish to
attain a recognised chartered qualification. then this course is for you.
The newest feature of this course is Live Online classes. Live online classes allow you to interact
and ask questions while listening to your lecturer deliver their class live online. If you are unable
to make the session, all live classes are recorded for revision and playback purposes. They are
available for both classroom and distance students.
Typically, students come from a range of diverse backgrounds, some aspire to work as quality
managers and others work in disciplines that engage with, or support the quality function.

2. Course Aims
The focus of quality management is to ensure that organisations have the capability to meet
consistently the needs of their customers and stakeholders. This breaks down into two main areas
of activity:
 To act as the ‘conscience’ of the organisation, providing confidence to the organisation, its
customers, regulators and other stakeholders
 To act as a mentor and coach at all levels of the organisation by helping those within the
organisation and its supply chain to improve.
You will learn ISO 9000 requirements and standards. By examining ISO standards, it will
explain the methodology of quality improvement, in particular the various tools and techniques
for analysis. Learning about ISO 9001 sets out the requirements of a quality management
system, ISO 9000 covers the basic concepts and language, ISO 9004 focuses on how to make a
quality management system more efficient and effective, ISO 19011 - sets out guidance on
internal and external audits of how its quality management system is working.
Other important areas covered on the course includes different types of quality management
systems, improvement and change management, process management, auditing and quality tools.
If you wish to work in quality monitoring and control, you will learn the importance of correct
tests and recording of results, and the value of data management and prevention. You will learn
that quality control is not just about finding problems, but is about putting measures in place so
issues do not occur again. Your knowledge and understanding will greatly improve on areas such
as audits, quality management systems, quality gurus, quality circles, KPI's, and quality costs.
Key Facts
 Aspiring quality professionals in all sectors of industry and public service are now studying
for the Chartered Quality Institute’s Diploma in Quality Management
 To address this need on a nationwide basis, CMI offers a flexible way for you to study by
classroom learning or by online learning.
 The CQI Diploma comprises nine modules which address the key requirements of quality
management .
 Students who achieve the CQI Diploma in Quality gain the right to use the designated
letters DipQ and satisfy the academic requirements for CQI membership.
 The Diploma is not necessarily an end in itself, as there are progression opportunities to
higher programmes.

3. Course Modules
There are 9 units to cover, comprising of 3 exams, 5 x 4500 word Assignments and 1 Business
Research Project ;
1. Management System Models; 2. Quality Related Legislation and
Regulation;
3. Quality Planning in the Product Life Cycle; 4. Quality Management ;
5. People in Quality ; 6. Monitoring and Measuring for
Quality ;
7. Using Quality to Improve Business Performance; 8. Management Systems Audit
9. Business Research
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected

4. 2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart

5. A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that

6. is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.

7. 5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

8. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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