The document discusses quality management topics and provides resources on the topic. It highlights the top 5 areas that quality executives are focusing on in 2013, which are: standardization of quality processes, quality management software, creating a business process platform, quality metrics programs, and supplier quality management. It also provides descriptions of common quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and others.

1. Quality management topics
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I. Contents of quality management topics
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Over the past year, the LNS Research team interacted with hundreds of quality executives via
online survey and live discussions at industry events. To date, we’ve collected responses from
over 900 executives and have analyzed critical quality management trends such as the strategic
initiatives companies are undertaking, metrics being measured, best practices, the use of quality
management software, etc.
We will use this blog post to highlight the top 5 areas executives are planning to focus on in
2013. Regardless of the maturity of your organization in quality management, industry, company
size, or geography, we’re confident that you’re most likely already focused on more than one of
the initiatives below.
Standardization of Quality Process
Standardization is a difficult and time consuming task. When you’re talking about organizations
that are global in nature, the project becomes even more complicated. Adding to the complexity,
there are organizations that have grown out of acquisitions, with every Business Unit (BU)
having its own quality standards. Organizations have always known the benefits of establishing a
standardized process for quality management across the organization, but the project generally
takes so long, and without corporate commitment, it can be difficult to get value from these
programs.

2. Another critical area of focus for standardization is in establishing a culture of collaboration
across functional groups. Organizations have increasingly started to create Corporate Quality
groups with the responsibility to execute such projects. These groups generally establish a 3 year
or 5 year plan around managing standardization projects and utilizemultiple ISO standards to
achieve those goals. Quality process standardization and the use of emerging technologies will
continue to be a focal point for quality executives in 2013.
Quality Management Software
LNS Research has seen an increase in the percentage of companies planning to implement
quality management software. More specifically, Enterprise Quality Management
Software and Statistical Process Control systems are increasingly adopted at an enterprise
level. Executives are implementing EQMS to manage content and business processes for quality
and compliance across the value chain. Similarly, Statistical Process Control is implemented
with a goal to reduce manufacturing variability and manage product quality.
Companies are moving away from implementing point solutions toward managing quality across
the organization. Executives have come to understand that even though the initial time and
resource requirements for these types of such targeted projects are minimal, such
implementations are difficult to scale and often end up being a roadblock for building a closed-
loop process for managing end- to- end quality processes.
The quality management software space has matured significantly over the past few years. It’s
typical for vendors to now have experience with multiple enterprise-wide implementations. It’s
important for quality executives to understand vendors’ experience in their company’s specific
industry, geography, functionality, and IT infrastructure, as well as the vendor's past successes.
As companies are starting this journey towards quality management software implementation,
it’s paramount to develop a well-established plan and have thorough knowledge of the software
space to align the right solutions with needs.
Creating a Business Process Platform
While we discussed EQMS implementation projects, another area of focus is on connecting
multiple systems such as ERP, PLM, EH&S, CRM, MOM etc. to manage quality across the
value chain. To effectively manage the quality of products and processes, organizations need to
establish control of quality across different stages of the value chain such as R&D, design,
engineering, operations, supply chain, customer service, etc.
Some organizations may have a very mature, existing PLM implementation and consequently
manage quality through the PLM application. Similarly, there are organizations with mature ERP
implementations that manage quality processes through the ERP system. Other systems such as
EH&S, CRM, MOM, and Supply Chain management touch quality in a critical way.

3. Executives have now started utilizing Business Process Management (BPM) tools to automate
end to end business process. In 2013, organizations will continue to focus on harmonizing the
existing investments made in these systems to ensure that quality is managed seamlessly across
the organization. Understanding points of integration between systems will be vital for
successfully interconnecting processes and data.
Quality Metrics Program
Measuring the quality performance of an organization is not a new concept. Organizations have
done it for years now and have established standardized metrics that are utilized across industries
and geographies. Industrial and manufacturing metrics such as Overall Yield, On time and
Complete Shipments, Supplier defects, etc., have been consistently used by many organizations.
However there are other metrics such as the Cost of Quality, New Products Introduction,
and Overall Equipment Effectiveness, etc., that companies are still having difficulty with
understanding and implementing.
Another area creating a challenge is in finding quality metrics data that can be used to
benchmark performance and understand gaps as compared to peers in the industry. LNS
Research has collected metrics data from hundreds of organizations and will be releasing it soon
to Global Quality Advisory Council members. We’ll also be covering quality and
manufacturing metrics more in depth throughout 2013.
Supplier Quality Management
The final area of focus will be to on supplier quality management. Executives in many
industries work with global supplier bases that are heavily tiered. An organization is not only
responsible for managing the quality of its own suppliers, but also its suppliers’ suppliers. There
are many critical issues that come up while managing a global supplier base such as compliance,
risk management, traceability etc., that executives have to manage. As organizations scale
business across geographies, this challenge will get more complicated. LNS will be publishing
best practices on the supplier quality management topic in 2013. Stay tuned!
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III. Quality management tools
1. Check sheet

4. 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

5. 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

6. 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

7. 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

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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