1. quality management systems examples
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• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of quality management systems examples
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What is a Quality Management System (QMS)? It is a management system pure and simple. It
is not quality’s management system; it is a management system that, if done right, produces
quality products. A Quality Management Systems is built for ISO registration, to satisfy
customer requirements, or to produce better products. Building a robust Quality Management
System is not hard if you follow these 10 easy steps.
How To Build a Quality Management System:
1. D EFIN E AN D M AP YOUR PR OCESSES
The process of creating process maps will force you to define your processes and the sequence
and interaction of those processes (clause 4.1). Process maps are important for understanding
who is responsible for what. They define your core business process and communicate the flow
of your business. When mapping your processes:
 Follow an order through the system.
 Identify support processes for IT, HR, and Accounting.
 Determine how quality interacts with each process either for inspection, review, or to roll-up
metrics in support of Quality Objectives.
2. D EFIN E YOUR QUALITY POLICY
Your Quality Policy states the mission (what your customer wants from you) of your
organization as it relates to quality. It is your quality mission. When building your quality
management system abd writing your quality policy, think about your commitment to customer
focus:
 Quality – What do you need to do consistently to satisfy your customer?

2.  Customer Satisfaction – What are your customer’s requirements?
 Continuous Improvement – What do you need to do better to satisfy your customer more
than now?
3. D EFIN E YOUR QUALITY OBJECTIVES
These are the objectives of the quality management system. They must be communicatedand
each employee must understand their impact on quality. ISO requires that your quality
objectives are:
 Derived from your quality policy
 Measureable
 Deployed through the organization
4. D EFIN E YOUR D EFECTS FOR EACH PR OCESS
Defects are nonconformances that occur either as a product defect or a process defect. Each time
a defect occurs it needs to be counted in some way. When defining your defects:
 Determine transaction volume
 Determine defects (product and process based)
 Define how defects are recorded
 Define how defects are charted and communicated
5. D EVELOP YOUR D OCUM EN TS AN D R ECOR DS
ISO 9001:2008 requires a Quality Manual, six procedures, and 21 records. There are many more
optional documents and records that can be used but they are not required. Start with the
minimum ISO document set and add more as needed.
 Create required documents for your business model
 Create necessary policies, procedures and forms
 Create Records for each processes
6. D EFIN E YOUR QUALITY PR OCESS
Your quality processes include:
 Internal audit process
 Corrective and preventive action process
 Management review, communication, and commitment process
7. D ETER M INE YOUR TR AIN IN G N EED S
Everyone needs to demonstrate competence in their job. Training is only the beginning and can
occur on the job, from a class, or through other means. Either way:
 Internal auditor competence is critical
 Corrective Action training is highly recommended
 Failure Modes Effects Analysis (FMEA) training is strongly suggested to find preventive
actions
8. USE YOUR QUALITY M AN AGEMEN T SYSTEM
Yes you have to use the system for it to be any good at producing quality products. Using
the QMS means:
 Collecting nonconformance data continuously
 Reviewing nonconformance data for corrective action, continuously

3.  Reviewing FMEAs for Preventive action, frequently
 Performing internal audits and management reviews periodically
9. M EASUR E AN D M ONITOR YOUR PER FOR M AN CE
If you are using the quality management system, then you are collecting data. But what good is
data you collect but do not understand? You need to:
 Track your Quality Objectives performance
 Define new performance benchmarks
 Discover improvement opportunities in your data by identifying trends, patters, or correlations
10. TAKE ACTION THAT IM PR OVES YOUR PER FOR M AN CE
If you have data and identified trends, then it is time to act. Action is what the QMS is all about.
We are not tracking data for the auditor, for ISO, or for the quality manager. The whole goal is
to deliver improvement and this happens by:
 Prioritizing your improvement opportunities
 Choosing opportunities that make a difference
 Reinforcing your commitment to quality to achieve better results

Whether you realize it or not, the management system you are using is a quality management
system. So why not do it right and build a QMS to produce products with real quality?
Bizmanualz is here to help you build a lean ISO quality system that delivers real value and
rewards. Call to find out more information on how your business can benefit from
implementing lean ISO Quality Today.
==================
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:

4.  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

5. 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

6. 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

7. 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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