1. project on quality management
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I. Contents of project on quality management
==================
When gathering requirements for a project, a manager needs to go beyond specifying what is
being developed (scope) and when it will be delivered (time). S/he also needs plan quality
measures into each deliverable, which contributes towards the end product or service. One can
think of quality management as answering the "how" part of problem solving. In the planning
process, a project manager assesses product/service specifications and arrives at S.M.A.R.T
(Specific, Measurable, Attainable, Realistic, and Timely) quality criteria for each deliverable.
These plans are executed throughout the project lifecycle (via testing, inspections, walkthroughs
etc.). As the project manager controls and monitors the project, s/he may modify and correct
product/service specifications and plans to achieve better quality. Lastly, the project manager
conducts an audit of product/service quality as the project reaches closure. A key concern of the
project manager at this stage is to have stakeholders formally accept the final product/service
through achieving a sign-off document. If quality planning and execution are done properly
within a project, it makes the end-product more appealing to stakeholders.
Quality planning involves identifying which quality standards are relevant to the project and
determining how to satisfy them. It is important to perform quality planning during the Planning
Process and should be done alongside the other project planning processes (i.e. Time Planning,
Risk Planning, etc.) because changes in the quality will likely require changes in the other
planning processes, or the desired product quality may require a detailed risk analysis of an
identified problem. It is important to remember that quality should be planned, designed, then
built in, not added on after the fact.

2. Enterprise Environmental Factors
Factors which are related to the type of business the project is being produced for can have an
effect on its quality. Such factors include government or industry standards, marketplace
conditions and stakeholder risk tolerances.
Organizational Process Assets
Organization Process Assets (or "OPAs") are inputs which come from the organization(s)
producing the project. They include quality policies, procedures and guidelines, historical
databases and lessons learned from previous projects. An organization's quality policy may be
adapted to a particular project, or used "as is." If no quality policy exists, or if more than one
organization is working on the project, the project management team needs to develop one. The
project management team is also responsible for making sure the stakeholders are aware of
quality policy.
Project Scope Statement
The project scope statement details the deliverables, objectives, thresholds and acceptance
criteria that the project must meet. This makes it very important to quality planning.
Acceptance criteria describe the requirements and conditions that must be achieved before
deliverables will be accepted. If the deliverables satisfy the acceptance criteria, then the result is
the customer's needs being met. The acceptance criteria can drastically increase or decrease the
costs of project quality. In addition, the product scope statement may contain a scope description
which contains issues that may affect quality planning.
Cost-Benefit Analysis
During the quality planning process it is important to consider cost-benefits trade-offs. The key
benefit of meeting sufficient quality requirements is that it results in less rework, which in turn
results in higher productivity, lower costs, and greater satisfaction from the stakeholder. The
main cost of achieving such quality requirements is the expense the comes with activities relating
to Project Quality Management.
Benchmarking
The process of benchmarking compares planned or existing project practices to the practices set
in place for other projects in order to generate ideas as to which areas of the project could be
improved upon. Furthermore, it is also used to provide a basis for measuring overall
performance. The projects used for comparison can be from within the performing organization
or from a source outside of it, and do not necessarily have to be from within the same application
area to be used.

3. Design of Experiments (DOE)
Design of Experiments (DOE) is a method used to identify factors which may influence certain
aspects of a product or process during the time it is under development or in production. It also
holds a key role in the process of optimizing products/processes. An organization would use
DOE to reduce the sensitivity of product performance to factors caused by differences in
manufacturing or the environment. The main benefit of DOE is that it provides the organization
with a framework to systematically change all of the important factors associated with a project,
rather than changing them one at a time. By analyzing the data obtained, an organization can find
the optimal conditions for their product/process, with a focus on factors influencing the results,
and showing the existence of correlations and interactions within the factors.
Cost of Quality (COQ)
Quality costs are the total of all costs incurred in preventing non-conformance to established
project/process requirements, appraising the product for conformance to requirements, and any
rework necessitated by a failure to meet requirements. Failure costs are divided into internal and
external costs. Failure costs are also known as cost of poor quality.
Additional Quality Planning Tools
Additional quality planning tools are often used to better define the situation and assist in
planning effective and efficient quality management activities. These include brainstorming,
affinity diagrams, nominal group techniques, matrix diagrams, flowcharts, and prioritization
matrices.
==================
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.

4. 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
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

5. 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
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method

6. 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
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

7. 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

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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