1. Quality management theories
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I. Contents of quality management theories
==================
The history of quality management, from mere 'inspection' to Total Quality Management, and its
modern 'branded interpretations such as 'Six Sigma', has led to the development of essential
processes, ideas, theories and tools that are central to organizational development, change
management, and the performance improvements that are generally desired for individuals,
teams and organizations.
These free resources, materials and tools are an excellent guide to the quality management area,
for practical application in organizations, for study and learning, and for teaching and training
others.
These free pdf materials are provided by permission of the UK Department of Industry - now the
Department for Business, Enterprise and Regulatory Reform - which is gratefully acknowledged.
The materials listed and linked from this page are subject to Crown Copyright.
Please note that since the replacement of the UK Department of Industry by the Department for
Business, Enterprise and Regulatory Reform, the branding on the materials is now obsolete.
Nevertheless, since the Quality Management technical and historical content is unaffected by the
DTI branding the materials remain relevant for training, learning and reference.
It is appropriate to note the passing a little while back now, of Joseph Juran, a seminal figure in
the history of quality management, who died 28 February 2008, age 103. Juran did more than
teach the Japanese about quality management. He was also arguably the first quality expert to
emphasise that no quality management system works unless people are empowered and
committed to take responsibility for quality - as an ongoing process - effectively for quality to

2. become part of part of people's behaviour and attitudes - an ethos. The section below
on Kaizen explains the connections between the true ethos of quality management, and the
positive ethical management of people.
Further total quality management information and quality management terminology explanations
are on the Six Sigma page.
history of quality management
The roots of Total Quality Management can be traced to early 1920's production quality control
ideas, and notably the concepts developed in Japan beginning in the late 1940's and 1950's,
pioneered there by Americans Feigenbum, Juran and Deming... More about Quality Management
and TQM history.
quality management gurus and theories
Quality Management resulted mainly from the work of the quality gurus and their theories: the
American gurus featured in the 1950's Japan: Joseph Juran, W Edwards Deming, and Armand
Feigenbum; the Japanese quality gurus who developed and extended the early American quality
ideas and models: Kaoru Ishikawa, Genichi Taguchi, and Shigeo Shingo; and the 1970-80's
American Western gurus, notably Philip Crosby and Tom Peters, who further extended the
Quality Management concepts after the Japanese successes... More about the Quality
Management gurus and their theories, including the development and/or use of the Plan, Do,
Check, Act (PDCA) cycle, Pareto analysis, cause and effect diagrams, stratification, check-
sheets, histograms, scatter-charts, process control charts, system design, parameter design,
tolerance design ('Taguchi methodology'), Quality Improvement Teams (QIT), Just In Time
(JIT), Management By Walking About (MBWA), McKinsey 7-S Framework, etc.
total quality management (TQM)
Total Quality Management features centrally the customer-supplier interfaces, (external and
internal customers and suppliers). A number of processes sit at each interface. Central also is an
organizational commitment to quality, and the importance of communicating this quality
commitment, together with the acknowledgement that the right organizational culture is essential
for effective Total Quality Management.... More about the fundamentals and structures of the
TQM model, including the people, processes and systems in the organization.
processes - understanding processes and methods for process improvement
Understanding processes is essential before attempt is made to improve them. This is a central
aspect to Total Quality Management, and also to more modern quality and process improvement

3. interpretations and models such as Six Sigma.... More about Total Quality Management process
and process improvement methods.
quality process improvement tools and techniques
A wide range of tools and techniques is used for identifying, measuring, prioritising and
improving processes which are critical to quality. Again these ideas and methods feature
prominently in modern interpretations of Total Quality Management methodology, such as Six
Sigma. These process improvement tools and techniques include: DRIVE (Define, Review,
Identify, Verify, Execute), process mapping, flow-charting, force field analysis, cause and effect,
brainstorming, Pareto analysis, Statistical Process Control (SPC), Control charts, bar charts, 'dot
plot' and tally charts, check-sheets, scatter diagrams, matrix analysis, histograms..... More
about tools and techniques for process evaluation and improvement.
A summary of quality tools is below.
The Kaizen methodology is also described below in some detail.
developing people and teams
People are a fundamental component within any successfully developing organization. Take
away the people and the organization is nothing. Take away the people's motivation,
commitment and ability to work together in well-organised teams, and again, the organization is
nothing. Conversely, inspire the people to work well, creatively, productively, and the
organization can fly. Logically therefore, the development and proper utilization of people are
vital to the success of all quality management initiatives. There are a wide range of models that
are used in selecting, assessing, training and developing and motivating people, among which are
classical models such as Belbin, Myers Briggs Type Indicator (see the personality
modelssection), Bruce Tuckman's 'Forming, Storming, Norming, Performing' model, John
Adair's Action Centred Leadership model.... More about people and culture within quality
management.
quality management systems
A 'Total Quality organization' generally benefits from having an effective Quality Management
System (QMS). A Quality Management System is typically defined as: "A set of co-ordinated
activities to direct and control an organization in order to continually improve the effectiveness
and efficiency of its performance." Customer expectations inevitably drive and define
'performance' criteria and standards. Therefore Quality Management Systems focus on customer
expectations and ongoing review and improvement.... More about Quality Management Systems,
what they are, and how to set up a good QMS.

4. performance measurement and management
There are many ways to measure organizational performance other than financial output or
profit. Modern measurement focuses on the essential activities, resources and other factors -
many less intangible than traditional indictors - that impact on final outputs. These include
modern methods such as Balanced Scorecard... More about performance measurement, and cost
of quality.
==================
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

5. 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
the control of the experimenter. If a parameter exists that

7. 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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