This document discusses quality management issues and provides resources on the topic. It begins by outlining common quality management issues organizations may face and provides questions to help assess an organization's quality management processes. It then discusses specific issues in more depth, including nurturing a quality culture, assessing metrics, integrating disparate quality systems, handling increasing data volumes, and closing the quality loop. The document also introduces several quality management tools, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It concludes by listing additional quality management topics.

1. Quality management issues
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I. Contents of quality management issues
==================
The top five quality management issues facing the manufacturing industry most likely reflect
those of your own. However, what can give your organization an edge over competitors is the
ability to integrate quality management processes into a closed-loop quality management system
(QMS).
To stay on top of the latest quality issues facing the manufacturing industry heading into 2014,
here are the key questions you need to answer:
1. How has your organization nurtured a culture of quality over the last year?
2. Have you made gains in assessing metrics and key performance indicators?
3. Do you still have disparate QMS solutions in place?
4. Is your technology architecture ready to handle exponential data growth?
5. Is your organization poised to close the loop on quality management?
Answering each of these questions can provide your organization with a rough estimate of its
ability to mitigate these quality issues in manufacturing today.
1. How has your organization nurtured a culture of quality over the last year?
Whether you call it top-level support or executive buy-in, leadership is absolutely critical to
nurturing a successful culture of quality. The LNS Research 2012-2013 Quality Management
Survey reiterates the familiar theme of a lack of employees taking ownership of quality issues.
Of all of the top five quality management issues facing the manufacturing industry, this issue
remains a primary concern.

2. Over 50% of executives surveyed by LNS Research reported that their organization views
quality as a department, not a responsibility throughout the enterprise. Personnel working outside
of quality management depend upon executive leadership to foster a complete, holistic culture of
quality. Without such support, quality initiatives will likely produce minimal gains. Selling
quality to all departments
2. Have you made advances in assessing metrics and key performance
indicators?
Your organization must be able to trust the validity of its quality metrics. For more than half of
executives surveyed by LNS Research, the ability to effectively measure quality metrics remains
a top quality issue. To enable QMS success, organizations should at the very least look for
advancements in measuring:
 Cost of good quality
 Cost of poor quality
 Overall equipment effectiveness
 New product introductions
These are still arguably the most notable of all QMS metrics. Learn to build a business case for
EQMS with the Cost of Quality
3. Do you still have disparate QMS processes and solutions in place?
If your answer to this question is yes (and it most likely is), your organization should be able to
make substantial gains by integrating these disparate QMS solutions. Quality issues can originate
in any link of the value chain. With several different QMS solutions in place among dispersed
manufacturing operations, mitigating nonconformances and corrective/preventative actions is
cumbersome to say the least. As a solution, enterprises must turn to QMS integration. How to
integrate QMS with other business systems
4. Is your technology architecture ready to handle exponential data growth?
The amount of data collected by manufacturers around the world will continue to grow to
exponentially throughout 2014 and beyond. Some have coined this deluge “Big Data” — but
remember that “big” is a matter of context.
Organizations that effectively calibrate metrics already collect large volumes of data despite the
“Big Data” hype in recent times. Before your organization can prepare for exponential data
growth, it must continue to break down barriers between silos of quality process data, which may
be bound by legacy technology.
5. Is your organization poised to close the loop on quality management?
Recently, QMS capabilities have adopted an enterprise-wide scope. As such, the enterprise
quality management software space continues to mature rapidly. Closed-loop quality
management is fast becoming the new paradigm of industry-leading manufacturing enterprises.
In fact, an enterprise QMS can potentially enable novel solutions to all of the previous questions
above.

3. These five questions all relate to the most pressing quality issues in manufacturing today. To
meet the challenge, further integration of QMS capabilities throughout the enterprise steps
forward as a viable strategy.
==================
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]
III. Other topics related to Quality management issues (pdf download)
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