The document discusses quality management in projects. It provides definitions and explanations of key quality management concepts including quality management processes, tools, and strategies. Specific quality management tools discussed include check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms. The document emphasizes that quality should be planned into projects from the beginning through prevention rather than just inspected, and that customer satisfaction is key.

1. Quality management in projects
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I. Contents of quality management in projects
==================
Project Quality Management includes the processes and activities of the performing organization
that determine quality policies, objectives, and responsibilities so that the project will satisfy the
needs for which it was undertaken.
It implements the quality management system through policy and procedures with continuous
process improvement activities conducted throughout, as appropriate.
Quality management, like every other aspect of the project management, should be proactive.
Limiting the quality management to detecting defects is reactive. You should plan the quality
and prepare an environment that does not create defects, instead of finding and repairing defects.
Many people who are new to project management find it strange that quality is treated as a
separate area. Surely, quality should be present in every aspect of the project. This way of
thinking begs the question, why is quality treated as something that can be managed separately?
To answer this, we need to define exactly what we mean by ‘quality’ and who gets to decide
what this definition is.
In some situations a quality standard might be obvious, for example a computer system used by
customer service staff must be able to deal with peak loads. In this case, a quality standard could
specify that when one hundred staff are inputting data at the same time, the system response time
must still be less than two seconds.

2. However quality is not always quite so easy to define; consider the design of the computer
interface to capture customer data.
The software engineer responsible for the design might consider conformance to industry
standards to be a sign of quality.
The user might consider it more important that the design matches the interfaces that they
already use.
In this example, it is more important that the users will be able to work more efficiently than that
the design conforms to some theoretical external standard. In fact, it should always be the end
user who decides what counts as quality rather than the people doing the work. Although this can
be complicated when a project has a number of users who have different priorities.
In the example we have been using, it may be important for customer service staff who deal with
billing to see a summary of the past two years of payment history on the ‘home’ screen whereas
staff who deal with complaints might prefer to see the complaint history. Focusing too much on
one group of users may compromise the others and leave them dissatisfied. This illustrates one
reason why a system to manage quality is required.

3. There are six possible reasons why quality standards might not be met despite everyone on the
project team doing their best to deliver the project as specified.
1. The users were not asked to specify their requirements in sufficient detail
2. Not all of the user groups were asked
3. The requirements were not understood
4. They were understood but could not be achieved
5. The quality requirements changed during the project
6. The quality requirements were exceeded
The first five points are easily understandable, but the last one needs some explanation. If quality
requirements have been exceeded then someone somewhere has done more work than has been
planned for in that part of the project.
This work needs to be paid for, in either time or money and this has not been budgeted for.
Because there is a lag when comparing actual progress against planned progress and for money
actually spent against the budget, this is not usually obvious at the time.
The result of exceeding the quality plan is that sooner or later either more money needs to found
or the scope of the project needs to be reduced. Neither of these things is acceptable.
The purpose of quality management is to make sure that the project meets the needs for which it
was created. To do this it needs to take account of the reasons above why it might not. It needs to
ensure that:
1. Users are asked to specify their requirements in sufficient detail
2. Requirements are fully documented
3. All stakeholders agree to them
4. There is a recognized process to deal with any changes
5. There is a process for monitoring and controlling quality

4. Modern quality management complements project management and both disciplines recognize
the importance of:
Customer Satisfaction
This involves understanding, evaluating, defining, and managing expectations so that customer
requirements are met. This requires a combination of conformance to requirements, to ensure the
project produces what it was created to produce, and fitness for use (the product or service must
satisfy real needs).
Prevention Over Inspection
One of the fundamental tenets of modern quality management states that quality is planned,
designed, and built in rather than inspected in. The cost of preventing mistakes is generally much
less than the cost of correcting them when they are found by inspection.
Continuous Improvement
The plan-do-check-act cycle is the basis for quality improvement. This is described in detail in
the ‘Project Process Groups’ eBook, which can be downloaded free from this website.
Management Responsibility
Success requires the participation of all members of the project team, but remains the
responsibility of management to provide the resources needed to succeed.
==================
III. Quality management tools
1. Check sheet

5. 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

6. 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

7. 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

8. 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

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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