Quality Management Tool
Quality Management Tool
Quality Management Tool
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The team using it can study observed data (a performance measure of a process) for patterns over
a specified period of time. It is also used at the end of the change process to see whether the
change has resulted in permanent improvement.
The Pareto chart is named after Wilfredo Pareto, the Italian economist who determined that
wealth is not evenly distributed. The chart shows the distribution of items and arranges them
from the most frequent to the least frequent, with the final bar being miscellaneous.
The Pareto chart is used to define problems, to set their priority, to illustrate the problems
detected and determine their frequency in the process. It is a graphic picture of the most frequent
causes of a particular problem. Most people use it to determine where to put their initial efforts to
get maximum gain.
The cause and effect diagram is also called the "fishbone chart" because of its appearance and
the Ishikawa chart after the man who popularised its use in Japan. It is used to list the cause of
particular problems. Lines come off the core horizontal line to display the main causes; the lines
coming off the main causes are the subcauses.
This tool is used to figure out any possible causes of a problem. It allows a team to identify,
explore, and graphically display, in increasing detail, all of the possible causes related to a
problem or condition to discover its root cause(s).
The histogram is a bar chart showing a distribution of variables. This tool helps identify the
cause of problems in a process by the shape as well as the width of the distribution. It shows a
bar chart of accumulated data and provides the easiest way to evaluate the distribution of data.
Then there's the scatter diagram, which shows the pattern of relationship between two variables
that are thought to be related.
The closer the points are to the diagonal line, the more closely there is a one-to-one relationship.
The scatter diagram is a graphical tool that plots many data points and shows a pattern of
correlation between two variables.
Graphs are among the simplest and best techniques to analyse and display data for easy
communication in a visual format. Data can be depicted graphically using bar graphs, line charts,
pie charts and control charts. While the first three are commonly used, the last is a line chart with
control limits.
By mathematically constructing control limits at three standard deviations above and below the
average, one can determine what variation is due to normal ongoing causes (common causes)
and what variation is produced by unique events (special causes).
By eliminating the special causes first and then reducing common causes, quality can be
improved. Control chart provides control limits that are three standard deviations above and
below average, whether or not our process is in control.
This tool enables the user to monitor, control and improve process performance over time by
studying variation and its source.
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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:
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.
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