This document discusses quality management assurance and provides information on key topics related to quality management assurance including definitions of quality assurance and quality control, benefits of quality management, and common quality management tools. Quality assurance focuses on managing quality through planning and prevention, while quality control verifies output quality through checks and testing. Effective quality management provides benefits like increased customer satisfaction, reduced costs from rework, and financial rewards. Common quality management tools discussed include check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and quality management systems, courses, and standards.

1. Quality management assurance
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I. Contents of quality management assurance
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It is important for an organisation to agree on what the meanings ofQuality Assurance (QA)
and Quality Control (QC). Both form an integral part of the organisation's quality management
plan, and the effectiveness of delivery teams relies on the differences being well understood by
all stakeholders, including management.
Effective quality systems can contribute enormously to the success of projects, but the
counterpoint is that, when poorly understood, the quality systems are likely to be weak and
ineffective in ensuring that the delivered system is delivered on time, built by the team within
their allocated budget, and satisfies the customer’s requirements.
This article considers the difference between Quality Assurance and Quality Control. The
concepts are investigated by looking at guidance from key industry players.
Introduction
How many times has it struck you that many practitioners involved in the ICT field lack an
understanding of the difference between Quality Assurance and Quality Control? Often you will
hear someone talk about ‘QA’, when what they actually mean is ‘QC’.
This ambiguity consistently throws up problems and is a sure way of undermining a project.
Projects are negatively affected as it tends to lead to strained conversations and makes reaching
consensus difficult.

2. Although QA and QC are closely related concepts, and are both aspects of quality management,
they are fundamentally different in their focus:
 QC is used to verify the quality of the output;
 QA is the process of managing for quality.
Achieving success in a project requires both QA and QC. If we only apply QA, then we have a
set of processes that can be applied to ensure great quality in our delivered solution, but the
delivered solution itself is never actually quality-checked.
Likewise, if we only focus on QC then we are simply conducting tests without any clear vision
for making our tests repeatable, for understanding and eliminating problems in testing, and for
generally driving improvement into the means we use to deliver our ICT solutions.
In either case, the delivered solution is unlikely to meet the customer expectation or satisfy the
business needs that gave rise to the project in the first place.
Understanding the Difference Between QA and QC
So, what exactly is the difference between Quality Assurance (QA) and Quality Control (QC)?
A good point of reference for understanding the difference is the ISO 9000 family of standards.
These standards relate to quality management systems and are designed to help organisations
meet the needs of customers and other stakeholders.
In terms of this standard, a quality management system is comprised of quality planning and
quality improvement activities, the establishment of a set of quality policies and objectives that
will act as guidelines within an organisation, and QA and QC.
In the ISO 9000 standard, clause 3.2.10 defines Quality Control as:
“A part of quality management focused on fulfilling quality requirements”
Clause 3.2.11 defines Quality Assurance as:
“A part of quality management focused on providing confidence that quality requirements will
be fulfilled”
These definitions lay a good foundation, but they are too broad and vague to be useful. NASA,
one of the most rigorous software engineering firms in the world, provides the following
definitions (www.hq.nasa.gov/office/codeq/software/umbrella_defs.htm):
Software Quality Control:
"The function of software quality that checks that the project follows its standards, processes,
and procedures, and that the project produces the required internal and external (deliverable)
products"
Software Quality Assurance:
"The function of software quality that assures that the standards, processes, and procedures
are appropriate for the project and are correctly implemented"

3. Simply put, Quality Assurance focuses on the process of quality, while Quality Control focuses
on the quality of output.
Quality Assurance: a Strategy of Prevention
QA is focused on planning, documenting and agreeing on a set of guidelines that are necessary to
assure quality. QA planning is undertaken at the beginning of a project, and draws on both
software specifications and industry or company standards. The typical outcomes of the QA
planning activities are quality plans, inspection and test plans, the selection of defect tracking
tools and the training of people in the selected methods and processes.
The purpose of QA is to prevent defects from entering into the solution in the first place. In
other words, QA is a pro-active management practice that is used to assure a stated level of
quality for an IT initiative.
Undertaking QA at the beginning of a project is a key tool to mitigate the risks that have been
identified during the specification phases. Communication plays a pivotal role in managing
project risk, and is crucial for realising effective QA. Part of any risk mitigation strategy is the
clear communication of both the risks, and their associated remedies to the team or teams
involved in the project.
Quality Control: a Strategy of Detection
Quality Control, on the other hand, includes all activities that are designed to determine the level
of quality of the delivered ICT solutions. QC is a reactive means by which quality is gauged
and monitored, and QC includes all operational techniques and activities used to fulfil
requirements for quality. These techniques and activities are agreed with customers and/or
stakeholders before project work is commenced.
QC involves verification of output conformance to desired quality levels. This means that the
ICT solution is checked against customer requirements, with various checks being conducted at
planned points in the development lifecycle. Teams will use, amongst other techniques,
structured walkthroughs, testing and code inspections to ensure that the solution meets the agreed
set of requirements.
Benefits of Quality Management
The benefits of a structured approach to quality management cannot be ignored.
Quality Control is used, in conjunction with the quality improvement activity, to isolate and
provide feedback on the causes of quality problems. By using this approach consistently,
across projects, the feedback mechanism works towards identifying root-cause problems, and
then developing strategies to eliminating these problems. Using this holistic approach ensures
that teams achieve ever higher levels of quality.
As a consequence of formulating and executing a quality management plan the company can
expect:

4.  Greater levels of customer satisfaction, which will very likely result in both repeat
business, as well as referral business
 A motivated team that not only understand the policy objectives of the quality
management plan, but who also actively participate in executing the plan
 Elimination of waste by eliminating rework arising from either the need to address bugs,
or to address gaps in the solution’s ability to meet customer requirements
 Higher levels of confidence in planning, since the tasks arising from unplanned rework
will fall away
 Financial rewards for the company, which are a consequence of new projects from
existing and referral clients, as well as through the reduction of monies spent on rework
tasks.
As the company’s quality management plan matures, the confidence of all stakeholders will
grow. The company will be seen to be more effective and efficient in delivering an agreed ICT
solution to clients.
==================
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

5. 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
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

6. 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

7. 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
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.

8. 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

9. 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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