1. Quality management in project management
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I. Contents of quality management in project management
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In the late 80’s or early 90’s, quality management found its place within project management.
Today, no one can deny the fact that project management has become quality driven. Everyone
prefers not just project delivery but ‘quality’ project delivery.
Quality improvement, quality control, kaizen, valued added management etc – key elements in
quality management are gaining grounds in project management these days. A large number of
organizations are hugely investing on quality management professionals in order to ensure the
level of quality in projects.
Six Sigma or lean implementation is an approach taken in order to ensure zero defects in
projects. Constant improvement and elimination of errors are the desired results for all projects
and therefore, quality management has emerged as a major factor in project management. In fact,
project quality management is defined as a knowledge area of project management in PMBOK
Guide. In this article, let’s have a look at quality management processes implemented in a
project. Here is an evaluation of various aspects of quality management within project
management.
What is Quality Management?

2. Quality management as the term suggests is all about managing quality in services. When it
comes to project management, ensuring desired quality is the goal. The project delivery should
ensure quality management. Here, quality doesn’t always mean perfection and high quality
services, but maintaining consistency in quality across projects. The quality to be maintained in a
project is decided by the stakeholders, owners and clients of the project. Quality standards are
also defined based on organizational values and standards. A quality management process is
introduced in a project towards quality planning, quality assurance and quality control.
Quality Characteristics to be maintained in Project Management
In a project, quality characteristics are defined by the stakeholders. Some of the most common
quality characteristics are performance, functionality, suitability, reliability, consistency and
more. The levels of quality in these terms are measured as per project and organizational
standards. From project initiation and processes to project delivery, each should be measured in
terms of quality standards. In project deliveries, various things like computers, project
equipment, team etc. too matter in terms of ensuring quality characteristics as desired. Thus,
quality management should be in place from the beginning of a project till the end.
What are the different phases in Quality Management?
Quality management involves typically three phases – Quality Planning, Quality Assurance and
Quality Control.
 Quality Planning: Here, the quality plan is created. Every plan should have a desired objective
or goal and quality plan is no exception. The goal of quality management should be clearly
communicated to all the stakeholders in a project. After the goal is defined, the measures to
ensure the level of standard should be worked out. How will the customers be satisfied? What is
the level of quality that the stakeholders are expecting? How to determine if the quality measures
will lead to project success? When all the answers to these questions are in place, tasks should be
delegated to respective team members and quality plan is initiated.
 Quality Assurance: This is a process that moves along with project throughout the lifecycle.
Quality assurance is all about evaluating if a project is moving towards delivering quality
services. If all the quality characteristics are in place the quality plan can proceeding in an
effective manner. When quality goals are not achieved or are not in the process of getting
achieved, necessary steps and corrective actions should be identified. Ensuring corrective actions
too falls in the phase of quality assurance.

3.  Quality Control: Here, operational techniques are used in order to ensure quality standards. Any
time a problem arises relating to quality or if the quality plan is not executed in the desired
manner, corrective actions should be effective. Quality control involves monitoring project
results and delivery to check if they are meeting desired results or not. If not then alternative
actions should be implemented.
This is how, quality management is ensured in project management. By following the quality
management phases, projects are worked upon towards delivering desired results. Thus it ensures
quality standards that are defined and are a must in today’s project management world.
==================
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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