Quality in operations management

Quality in operationsmanagement
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I. Contents of quality in operations management
==================
Many things stand between a company’s operation management and its quality-centered end
game. Among the obstacles are the tendency to lose focus on the goal of quality improvement,
get sidetracked by a seeming “silver bullet” that promises to solve all the quality issues, or
simply give up when the gains don’t come fast enough.
Failure to focus
Implementing quality improvement requires a great deal of time and commitment. A mistake that
many organizations make is to jump headfirst into too many projects. At the start of the journey,
everyone is excited about the potential that quality improvement promises, and the firm may
embark on several projects at a time. This reduces the focus on any one project and often
stretches resources too thin.
Start by choosing a project that involves a process that employees have a lot of knowledge about
and understand well. This helps ensure that the results are successful and encourages others to
get behind future projects.
When adding projects, choose carefully. Tools such as the Pareto chart can help you select the
most important issues to address.
Paralysis by prioritization
Some companies fall into the trap of spending so much time on prioritizing potential projects that
they never get down to the real work. When selecting projects, companies typically rank them

based on anticipated benefit, which is often the expected return on investment. This often leads
to disagreement across the organization as to which project should get first priority. And as you
may know, benefits data is easy to manipulate.
Don’t get caught in this trap. If you must choose between two or more improvement projects that
have roughly the same value, just choose one and get started. Completing any project that
provides benefit to the business is much better than waiting to find the “perfect” project.
You may want to start with the project that’s easiest and quickest to implement. A quick victory
at the beginning of a quality improvement initiative can provide momentum to the organization
on future projects.
Avoid the lure of magical solutions
Falling victim to the “silver bullet” quality program happens to the best of ’em. Magical, fix-it-
all, pain-free programs are usually touted as the program that is going to save the company, but
easy, catch-all solutions often involve a consulting agency coming into the company with grand
ideas and beautiful presentations that are likely to fail because they don’t follow the proven
methods.
After a few months with few results, firms that embrace such programs often end up, moving on
to the next program that promises quick and easy results.
This approach may ultimately sabotage your quality improvement efforts. Employees become
tired and complacent about these programs, and they dismiss future programs as a waste of time;
after all, none of the past programs ever panned out very well.
Lack of employee involvement
In order for quality improvement projects to be successful, all levels of an organization must
commit to the effort. This means that all employees must understand the importance of the
quality projects and contribute to their success.
Employees sometimes view improvement efforts as a threat to their job security or as an
examination into whether they can do the job. Therefore, when identifying and implementing
any process changes, be sure to consult with and educate the people involved with performing
the process targeted for improvement. Often, just knowing why the change is being made is
enough to earn buy-in from the employees.
Know what to do
One of the biggest obstacles to quality success is not knowing what to do if a process is
discovered to be out of control, or unstable. Setting up and monitoring the control charts require
significant time and resources. Employees must take samples, perform the necessary
observations and measurements, and record the results on the chart. That’s the easy part.

What happens when a sample indicates that the process has changed and is now out of control?
That’s the hard part. A company must establish procedures and train employees to enact them to
deal with an out-of-control situation. Failure to act or acting inappropriately won’t lead to
improved quality and may even make the situation worse.
Learn from the experience
Few firms make the effort to learn from past quality projects. After completing a project,
companies often just move on to the next effort and lose what they learned from the last one.
Projects should have a formal documentation process (often called after-action or after-project
review) to record what happened, what the results were, and why.
Companies should also conduct after-project reviews to share project lessons. But don’t limit
these reviews to the project team; include personnel who may be working on other projects so
they can duplicate successes and learn from mistakes.
Calling it a program
A program implies something that has a defined beginning and end. Quality improvement
shouldn’t be a program; it should occur naturally as part of the everyday job. Quality must
become institutionalized and become an underlying foundation for the company. Without this
shift in culture, true quality success is only a passing dream.
Giving up
Achieving improvement and implementing change is a slow, continuous process. It doesn’t
happen overnight. Expecting instant improvements is a recipe for disaster, and giving up too
quickly because your first efforts don’t produce the desired results only leads to failure. But with
continual focus and a commitment, you will see improvement. Stick with it!
==================
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
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.
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
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
 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]
III. Other topics related to Quality in operations management (pdf download)
quality management systems
quality management courses
quality management tools
iso 9001 quality management system
quality management process
quality management system example
quality system management
quality management techniques
quality management standards
quality management policy
quality management strategy
quality management books

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