1. types of quality management
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I. Contents of types of quality management
==================
Quality management cannot guarantee project success, but it certainly provides a force
against failure. Quality management goes straight to the heart of any project, helping you to
deliver results designed to meet customer needs and expectations.
The goal of effective quality management is to set realistic project and process quality
objectives, define actionable quality expectations, ensure minimal product defects and
eliminate re-work. In short, quality management is designed to help you deliver the best
project results within established constraints and boundaries. These are impressive goals, but,
as with any other management process, quality comes at a price. Quality management adds to
the cost and timeline of any project through "overhead". Overhead can be simply defined as
the direct and indirect costs attached to a project as part of the overall execution process ... i.e.
the time, resources, tools and equipment needed to manage and deliver a project apart from
the costs of the actual project deliverables.
It's Unavoidable: Quality Needs Add Overhead Costs
Since quality management creates “overhead”, which adds to the costs and delivery timeline
of any project, related quality “actions” can be considered a potential point of failure. Left
uncontrolled, quality management overhead can impede an otherwise successful project by
extending the schedule or growing the budget. It is all a matter of balance - to find the point at
2. which quality objectives are achieved and defect risks are avoided, without exceeding
acceptable budgets and schedules. As with any other process, quality management must be
applied appropriately considering project needs and characteristics.
Finding the Right Balance of Quality vs. Overhead
Can quality be compromised? No. But quality must be practical, aligned with project needs,
capabilities and constraints. The question is the extent to which expenditures should be made
in the attempt to "guarantee" quality, and the extent to which quality management efforts may
put an otherwise do-able project out of reach. It's all about making the process fit the project
(the premise of fast tracked project management). The first step in this balancing act is to find
the sweet spot at which quality controls will deliver required results without adding excessive
overhead. These are the questions to be considered….
What are the established goals and objectives for process and project quality
(considering the practices used to manage the project and the actual project results)?
How is quality to be defined and measured for this project?
How important is “quality” to this project?
If quality requirements and expectations are not achieved, what are the likely costs
and consequences?
What types of quality management tasks and activities can be used to minimize or
eliminate these "low quality" consequences?
How will these quality management tasks and activities add to the project budget and
timeline?
Are these "overhead" additions acceptable to the project sponsor and customers, or,
are these stakeholders willing to absorb certain "quality defect" risks for the sake of a
shorter timeline and lower budget?
Some closing thoughts….
Once quality management overhead costs are calculated, all project stakeholders must be kept
fully informed of all the results and consequences. In order to strike the appropriate balance
between process and acceptable overhead, the project sponsor and customer must accept the
risks of any "quality compromises" made in an attempt to shorten project timelines, minimize
process complexity, or lower project costs. To make sense, quality management procedures
must be appropriate to project needs, and designed to deliver quality goals and expectations.
And, since quality is subjective, sponsor and customer buy-in is essential to project success.
==================
3. 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
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
4. 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
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.
5. 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
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
6. 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.
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
7. Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
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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