Quality management strategies

Quality management strategies
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I. Contents of quality management strategies
==================
Aconsiderable number of executives today still struggle to make quality a top priority. Based on
a survey of more than 300 executives, barely 50 percent of companies in large and mature
industries currently make quality a top priority. In fact, for some industries such as food and
pharmaceuticals, the percentage of companies that rate quality as a top objective is below 50
percent.
In manufacturing and industrial environments, quality is essential for survival (aka long-term
viability and profitability). If you’re not monitoring and continuously searching for ways to
improve the quality of your products or services, you’re going to fall by the wayside. Market
leaders are only too aware of this. That being said, though, there still seems to be a steady flow
of recalls and other quality issues throughout the world. Why is that?
At LNS Research, our data show that quality management software adoption has been increasing
in recent years. With its ability to centralize, standardize, and streamline quality data, it offers
companies a holistic tool for managing quality across the value chain. But quality management is
more than technology and automating processes. As noted by our Model of Operational
Excellence, it’s just as much about people and leadership.

Quality management as an executive priority
Our 2012 Quality Management Survey asked a number of questions pertaining to quality as it
relates to people, processes, and technology. One data point of note concerns a question about
executive priorities. As seen in the chart in figure 1 below, a majority of executives say that
quality management is currently a top executive priority. This sampling is from industries where
product quality and safety are imperative for success.
Figure 1: Quality as a top executive priority. Click here for a larger image.
Of particular concern here is the number of executives that do not say quality is a top priority.
Even those who plan to make it a top priority are still behind the curve. Plans are by no means
the same as a solid implementation, and many leaders have experienced how ineffective a quality
management program can be without organizational backing, especially one that does not start at
the top.
Getting the most out of quality management
When quality isn’t an executive focus, there tends to be disparate dedication to, and strategies
for, managing it. As a result, improvements and initiatives can become localized to specific
plants or departments rather than enterprisewide. This becomes even more of an issue when an
organization has hundreds of distributed facilities across the globe. The absence of unity around
quality management is a main culprit in adulterated products and adverse events.
Quality management strategy requires strong leadership to set the precedent for the rest of the
company. Even with some of the most effective technologies and efficient processes, without
top-level buy-in that supports, promotes, and practices quality, it’s nearly impossible to optimize
these resources. It’s most often a quality culture driven by the CEO or chief quality officer
(CQO) that gets the rest of the company to put quality as a top-of-mind issue.
Executive elements for a strong quality culture
A main goal for quality management programs is to have every employee considering how his
actions and decisions will affect the overall quality of his own processes as well as those

throughout the facility and the company as a whole. Support from the board of directors, in
collaboration with the CQO, CEO, and other executives, is critical for catalyzing cultural change
and movement toward this enterprisewide mindset. The board of directors and high-level
corporate management can take the following important steps toward this end:
Board of directors
• Make quality management a focus point of every board meeting.
• Stay current on quality trends, emerging technologies, and regulations.
• Establish or enhance a learning center for quality education courses, standard operating
procedures (SOPs), and instructions.
• Remain visible by walking around plants, attending both community and industry meetings.
• Make metrics such as cost of quality and overall equipment effectiveness (OEE) a top-level
issue.
Chief quality officer, CEO, and other executives
• Use both internal and third-party quality audits to assess and prioritize areas for improvement.
• Create an internal marketing plan around quality management for employees.
• Develop a plan for internal education, process improvements, and capital investments.
• Make reporting on metrics and plans for improvement a regular task for managers.
• Use rewards-based performance programs for quality improvements.
How executive focus resonates with managers and workers
Although the board of directors and executives may lay a foundation for a quality management
program, managers play a central role in its success as well. On the shop floor, for instance,
managerial actions stand as an example for how quality management should be practiced. If
managers are doing things like following SOPs, wearing safety goggles, and openly discussing
how their decisions will affect the quality of processes and products, then workers will be more
likely to exhibit this type of behavior.
Ideally, once a quality program is in full motion, workers shouldn’t feel as if they’re just another
cog in the wheel. They should have independent ideas and suggestions that they can express on
the topic. Only after a sense of ownership has been established on the shop floor will the benefits
of the quality management initiative be experienced at an enterprise level.
Act locally, benefit globally
Last week we had the chance to speak with an executive responsible for both quality and
software configuration management. Coming into his role not long ago, he saw a similar issue
with disparate quality management programs. Groups in the company were taking steps to

improve quality, but the improvements were siloed. What was interesting about this conversation
was that the executive was in favor of keeping quality management responsibilities localized, but
he understood the importance for an organizational, high-level change.
By taking actions such as reorganizing the structure of his quality organization, standardizing
SOPs, developing educational resources, and implementing a required metrics program, he
created an environment for managers and workers to optimize quality within their plants.
Basically, backed by executive buy-in, he enabled plants with the needed resources for success
and also decentralized quality management. His theory was that a local success would translate
to global success.
In future quantitative and qualitative research, LNS will continue to examine the specific benefits
of executive focus on quality and the shortcomings when such a focus is absent.
==================
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]
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