![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](https://image.slidesharecdn.com/msinqualitymanagement-150208182113-conversion-gate02/85/Ms-in-quality-management-4-320.jpg)
![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](https://image.slidesharecdn.com/msinqualitymanagement-150208182113-conversion-gate02/85/Ms-in-quality-management-5-320.jpg)
![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](https://image.slidesharecdn.com/msinqualitymanagement-150208182113-conversion-gate02/85/Ms-in-quality-management-6-320.jpg)
![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 Ms in quality management (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](https://image.slidesharecdn.com/msinqualitymanagement-150208182113-conversion-gate02/85/Ms-in-quality-management-7-320.jpg)
Ms in quality management
- 1. Ms in quality management In this file, you can ref useful information about ms in quality management such as ms in quality managementforms, tools for ms in quality management, ms in quality managementstrategies … If you need more assistant for ms in quality management, please leave your comment at the end of file. Other useful material for ms in quality management: • 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 ms in quality management ================== Eastern Michigan University offers an online Master of Science in Quality Management degree designed specifically using the American Society of Quality’s (ASQ) requirements for designation as a Certified Quality Engineer (CQE), Certified Quality Manager (CQM), and the Certified Quality Auditor (COA). The program is ideal for mid-career individuals who have quality-related responsibilities in areas of manufacturing, social services, testing laboratories, finance, education, construction, engineering, health care, and government. The MS in Quality Management seeks to prepare personnel to assist organizations to stay competitive through quality improvement and provide attainment of a professional certification. The MS in Quality Management is a 30- to 36-credit-hour program, depending upon whether the capstone research option or the coursework only option is chosen. Both options require 15 hours of required core courses. The coursework only option requires a minimum of 21 additional credit hours taken from a list of 10 quality concentration courses. The research option requires six to nine hours of quality concentration courses and a capstone course of six to nine hours. The required hours for the research option depend upon whether the student chooses a research project or a thesis project.
- 2. Disclaimer Approved Colleges is dedicated to providing users with deep and critical information resources about institutions and programs of higher education. Information found on ApprovedColleges.com is generated with a strong emphasis on research and accuracy through publicly disclosed sources. These sources range from government agencies to the schools themselves and are generally accepted as being trustworthy in the information they provide. Despite our best efforts the potential for inaccuracies and omissions is ever-present and thus Approved Colleges makes no claims or guarantees regarding the accuracy or credibility of information presented on our site at any given time. The information available from ApprovedColleges.com is intended to be an aid and resource to prospective students but should not be used to make enrollment or financing decisions. Approved Colleges does not allege, assert or guarantee that credits earned from any listed institution will necessarily transfer from one institution to another. Users are expected to do thorough research utilizing multiple sources and ultimately obtain additional information directly from the institutions themselves. Completing a form on ApprovedColleges.com is a request to be connected with an education institution for information and does not represent a decision to enroll or validate any preconceptions or knowledge a user may take from Approved Colleges' resources. Disclosure New laws now require that schools provide additional information regarding their graduation rates and amount of student debt by program. This information is often difficult to find and not standardized in how or where it is shown. ApprovedColleges.com will continue to make best efforts to locate and provide links to this important information whenever it is available. Some schools provide this to us for disclosing proactively and others do not. In cases were we have been unable to find information to direct users to we will provide a link to another organization whose site is specific to this type of data. Simply follow the provided link and search for the information using the available resources. ApprovedColleges.com makes no representations or guarantees about the accuracy of presented information. Questions related to these items should be directed to a member of the admissions team at the school. Approved Colleges strongly recommends that students fully research and examine every facet of an institution or program before making the decision to enroll. ================== III. Quality management tools 1. Check sheet
- 3. 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
- 4. 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
- 5. 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
- 6. 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
- 7. 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 Ms in quality management (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