This document provides an overview of an IT quality management system. It discusses ISO 9001, the most established quality management standard, which provides a structure for creating a quality management system. The document also lists and describes several common quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. These tools can help organizations streamline processes, reduce errors and costs, and improve quality.

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I. Contents of it quality management system
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Organisations are having to jump through an increasing amount of hoops in order to pre-qualify
for tender opportunities. Whilst this can seem like added bureaucracy, the public sector is under
pressure to demonstrate they are spending tax payers money wisely. With supply chain issues
hitting the headlines (the horse meat scandal, for example), the private sector is also starting to
expect more from their once trust trusted suppliers.
The new ‘hoops’ to jump through range from demonstrating what steps your organisation takes
to be sustainable, to your policy on diversity. This is all part of a movement towards greater
Corporate Social Responsibility (CSR), driven by governments’ recognising the benefits behind
organisations considering their wider impact.
However, despite these new expectations, a stalwart demand is evidence of quality, with buyers
wanting to be sure that you are running a robust, efficient business unlikely to make mistakes.
Understandably, they want to get some reassurance that you can deliver a good service, on time
and on budget. As such, it is common within tender documentation to be requested to give
evidence of a ‘Quality Management System’, or ‘QMS’ for short.
What exactly is a Quality Management System?
Many organisations already have a Quality Policy in place, perhaps gathering dust somewhere!
Whilst that may briefly outline the organisation’s aspirations to be dedicated to provide a quality
service, a Quality Management System (QMS) is much more in-depth than this.

2. Of course, a QMS’ raison d'être isn’t to fulfil a tender requirement; when executed properly, it
helps organisations to streamline their processes, helping to reduce errors, resulting re-work and
the costs associated.A QMS is a reflection of what your organisation does, how it is done and
how it is managed. The intent is that a QMS allows you to identify, measure, control and
improve your core processes for the benefit of your customers, delivering confidence in your
ability to consistently meet their expectations.
ISO 9001: the ready-made solution
Without buy-in and ongoing actions, as previously mentioned, policies and such like can go
unloved. Buying authorities are well aware of this, which is why the ISO 9001 standard is often
mentioned alongside questions regarding quality management.
What may look like an unusual acronym is the name of the world’s most established quality
management standard, providing a structure for creating a QMS. Created by the International
Standardization Organization, ISO 9001’s roots can be traced back to WW2 and has been
continually revised since through worldwide input.
The standard is popular for three reasons - credibility, its generic nature (suitable for all
organisations) and the fact that it has been proven to improve quality when implemented.
Credibility is further enhanced when obtaining certification for the standard, where an
independent Certification Body assesses the organisation against the requirements of the
standard. As such, this takes away the burdensome task for buying authorities to check each and
every potential supplier.
Importantly, ISO 9001 takes away the head-scratching result of being expected to put in a QMS
without any guidance. ISO 9001 sets out defined requirements so any organisation can put
together a QMS that works. Working with a Certification Body also ensures nothing slips, whilst
giving your clients the added reassurance that you can give more than your word; you’ve been
checked by a third-party body.
Want to read more about ISO 9001? See our dedicated ISO 9001 Beginner’s Guide, for more
information on the standard’s benefits and the process of achieving certification.
==================
III. Quality management tools
1. Check sheet

3. 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
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

4. 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
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

5. 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
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

6. 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.
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

7. 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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