1. Quality management for dummies
In this file, you can ref useful information about quality management for dummies such as
quality management for dummiesforms, tools for quality management for dummies, quality
management for dummiesstrategies … If you need more assistant for quality management for
dummies, please leave your comment at the end of file.
Other useful material for quality management for dummies:
• qualitymanagement123.com/23-free-ebooks-for-quality-management
• qualitymanagement123.com/185-free-quality-management-forms
• qualitymanagement123.com/free-98-ISO-9001-templates-and-forms
• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of quality management for dummies
==================
Without quality control, your organization can't survive for long. Successfully implementing,
maintaining, and evaluating quality control standards is critical whether you're seeking ISO
certification or just keeping up with customer needs. When implementing a quality control
process, you'll likely face resistance from people within the organization. By staying vigilant and
addressing potential problems early, however, your organization can function at a high level.
How to Obtain ISO Certification to Demonstrate Quality Control Standards
Meeting ISO (International Organization for Standardization) quality standards ensures
customers that you'll provide quality product. ISO standards are the most recognized quality
standards — after all, the organization's members consist of the national standards organizations
of 150 countries. Follow these ISO quality standards, which provide a common language for
companies to trade across the globe:
Get commitment from top management to ensure success.
Train all employees on the basics of quality.
Prepare your quality policy manual.

2. Document operating procedures.
Perform an internal audit.
Select an ISO certification agency to use.
Have the certification agency perform the audit.
If you pass the audit, congratulations!
If you don't pass the audit, take necessary corrective action and repeat the audit.
How to Introduce Quality Control to Your Organization
Introducing a quality control program into your organization requires careful planning and
precise execution. Like any other major new program you introduce, thorough planning and
attention to detail will greatly increase the odds of success. Just follow these steps:
Create a roadmap to guide your organization to its quality goals.
Prepare everyone for the changes to come.
Obtain a sponsor to be the organization's champion of quality.
Select a quality control manager.
Create an effective communications program.
Provide employee training on quality control.
Select a pilot project.
Perform the pilot, prove its value, and apply the lessons learned.
Implementing Quality Control Standards: How to Overcome Organizational Reluctance
Many people in the organization will see the introduction of a quality control process as an
unwelcome change. Overcoming reluctance to a new quality control process calls for clear and
consistent communication, and a constant eye on the "big picture." Here are some tips on how to
rally the support of the willing, ease the fears of the reluctant, and overcome the obstacles put up
by the unwilling.

3. Monitor and manage the risks as your project progresses.
Believe in your goal, and focus on the benefits that quality will bring to your organization.
Don't go it alone; find support from the quality sponsor or other experts in your organization.
Remain rational when you face challenges.
Keep everything in perspective.
Break down any obstacles into manageable pieces.
How to Get Your Quality Control Project Back on Track
Of course you expect your quality-control project to succeed, but things rarely go as smoothly as
planned. Even the best-laid plans sometimes run into problems. Be ready for trouble and use the
following tips to get your quality project back on track.
Review your goals and focus on what's really important.
Evaluate where your project stands — look at what you've achieved and where the project truly
has problems.
Get professional help from an outside expert.
Learn from your mistakes by preventing them in the future.
Determine your minimum acceptable goals; you may have to scale back the project.
How to Improve Quality Control through Customer Feedback
Customer feedback is one of the most important resources for improving an organization's
quality control. If you're serious about quality control, you can't assume that you know what the
customer wants, and you can't wait for them to tell you. Actively seeking customer input ensures
that you know exactly what the customer wants, which is the only way to keep your organization
in business.
Your organization exists to provide for the needs of your customers. Use these tips to devise a
feedback survey that reveals your customers' needs:

4. Define your objectives; know what you hope to accomplish before you begin.
Think about how you'll analyze the data you gather; consider your objectives as well as time and
budget restraints.
Use good questions that fit within your objectives and data-analysis method.
Keep the data-collection process simple to minimize errors.
Use an unrelated party to collect data to prevent bias.
Train your data collectors to ensure consistency and accuracy.
Perform a trial run to work out any bugs in the collection process.
Make any modifications necessary and gather more information from a larger customer
population.
How to Measure Your Current Quality Control Process
Careful measurement is key to managing your quality control processes. Use the following steps
to ensure that you measure the right quality-control factors in the right way.
Determine what to measure (the items or processes you decide to measure are called metrics).
Determine your measurement process by selecting the best process for your needs.
Define exactly how you’ll use the selected measurement process.
Train your employees on the proper measurement process.
Perform gauge repeatability and reproducibility (R&R) tests to determine measurement
variation.
Perform the measurements and compare to customer specifications.
Confirm the quality of your data with compare-and-review checks and the help of a computer.
Make sense of your data with coding and different data charts.

5. ==================
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

6. 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

7. 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

8. 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.

9. 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

10. 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 for dummies (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