1. Quality operations management
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I. Contents of quality operations management
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Continuously improving business processes is of increasing importance for companies
competing in today's global markets. For society, there is a strong need to sustain work
opportunities by realising and utilising the potential in existing businesses. Companies need to
compete both by bringing new products to the market and by improving existing products and
processes. These two aspects constitute the rationale underlying this master’s programme.
Programme description
Aimed at improving the processes through which new and existing products and services are
developed and delivered, we now witness a strong upsurge in industry’s interest in improvement
techniques and philosophies. Examples are Six Sigma, Lean Production and Lean Product
Development. Many industrial companies and public organizations are using improvement
programmes based on these or similar ideas and success in these programmes requires a change
in mindset together with organisational transformations. Engineers equipped with the knowledge
and competence required for leading quality driven change processes and business improvement
are therefore crucial.

2. This Master’s programme in Quality and Operations Management supports you as a student in
acquiring the knowledge and skills required to manage, improve and transform innovative and
organisational processes as e.g. developing new innovative offers, manufacturing and delivery.
The programme gives you a thorough understanding of tools for identifying customer needs and
business opportunities. You will be trained in the skills needed to manage the process of
developing products and services. A strong focus will be on the usage of different qualitative and
quantitative tools. Another focus will be on production and delivery processes, in both
manufacturing and service and on the tools and techniques for their improvement. You will
acquire a number of competencies needed to manage improvement and change processes,
whether they concern product development or production. These skills include leadership,
project management and change management.
Research connections
All courses and projects are taught and supervised by active researchers in the field of quality
and operations management and we apply or include our current research to examples, cases,
projects and regular lectures. As a student, you will be able to take part in research discussions
and analyses.
The programme is also connected with industry through guest lecturers in various courses and an
industrially based project course. The industrial cooperation is facilitated by the division’s
research collaboration with various companies and organizations, e.g. SKF, Volvo Trucks and
the Region of Västra Götaland.
If you choose to focus on any of the three main subject options – Six Sigma, operations
improvement, or product development – you will carry out a project in close co-operation with a
company. In the Six Sigma project an employee from the company involved will work together
with a group of students carrying out a Six Sigma project in that company. The operations
improvement project is similar to a small research study where a group of students investigate a
problem of interest to a certain company e.g. through interviews and study visits. The product
development project concerns development of a prototype for a company.
Career opportunities
Graduates of the programme can find positions as quality and improvement process facilitators,
problem solvers or change agents, production, quality or product development engineers or
project managers. There is also the prospect of working within management in the above
mentioned areas as well as management consultants or academic researchers.
Examples of positions acquired by students of earlier versions of this programme are:
management consultants, production engineers, quality engineers, Six Sigma Black Belts, project
leaders in product development and company internal quality consultants. Future career
opportunities are not restricted to manufacturing organizations, but also include service
providing organizations and public services.
Educational methods
The programme’s pedagogic frame is built on your participation and responsibility for your own
learning process. Within the theoretical parts of the education and in order to improve your
ability to present ideas both in written and oral form, you will write, present and discuss cases
and term papers at seminars.
In the programme a number of group projects will be carried out, all of them in cross-cultural
teams. This provides a training arena for cross-cultural group work preparing you for taking the
lead in cross-cultural improvement projects.

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

4. 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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