Seven QC tools are fundamental methods
to improve the quality of the product. They are used to analyze the production
process, identify the major problems, control fluctuations of product quality,
and provide solutions to avoid future defects. Statistical literacy is
necessary to effectively use the seven QC tools. These tools use statistical techniques
and knowledge to accumulate data and analyze them. Seven QC tools are utilized to
organize the collected data in a way that is easy to understand and analyze.
Moreover, from using the seven QC tools, any specific problems in a process are
identified.
7QC tools always include:
1. Stratification
2. Pareto diagrams
3. Cause and effect diagrams (Ishikawa diagrams)
4. Control Charts
5. Histograms
6. Check sheets
7. Scatter diagrams
1.Stratification
The objective of stratification is
to grasp a problem or to analyze its causes by looking at possible and
understandable factors or items. Collected data of a single population is
divided by time, workforce, machinery, working methods, raw materials, and so
on into a number of stratums (or layers) to find some latent characteristics
among the data be they the same or similar. For example, after collecting data
on photocopy mistakes, we can find some factors or peculiarities that can be
stratified in terms of operator, photocopy machine, sheet size, time, date, or
copy operation method.
How to stratify data
Step 1: Clarify the objectives of
stratifying data.
Step 2: Clarify the items to be
stratified within the problem.
Step 3: Determine the method of
collecting data.
Step 4: Check and compare the
stratified data items.
Step 5: Find causes by finding big
differences among data items If a big difference is not found,
keep going back to step 2 to add some other stratifying items until obvious
peculiarities among the data are discovered.
Typical Categories of stratification
1. by time: year, month, week, day,
hour, night, afternoon, morning, period, etc.
2. by workforce: division, section, day-shift, night-shift, group, age, experience, etc.
3. by machinery: line, equipment,
machine number, model, structure, jigs, dies, etc.
4. by working method: working
procedure, manual, speed, etc.
5. by raw material: place of
origin, supplier, lot, charge, etc.
6. by product: country, unit,
order, manufacturer, service provider, etc.
7. by environment: temperature,
humidity, weather, etc.
2. Pareto diagrams
A Pareto diagram is a form of bar
chart with the items arranged in descending order so that you can identify the
highest contributing factors to a problem. A Pareto diagram shows which
defective items should be tackled first. This type of diagram was given its
name by Dr. Joseph M. Juran because of its likeness to the 19th
century work of Vilfrido Pareto on uneven economic distribution work
postulating that 80 percent of the wealth of a nation is owned by 20 percent of
its population. Applying the principle to the production of a typical company,
Juran referred to the 20 percent of workers who produced 80 percent of its
output as the vital few and the remainder as the trivial many. By depicting
events or facts in order of decreasing frequency (or decreasing cost,
decreasing failure rate, etc.) a Pareto diagram can easily separate the vital
few from the trivial many. They are also used to compare conditions over time,
to see how both thedistributions and the total effects have changed after
corrective action has been taken. This type of diagram is one of the most
common statistical tools used by QC Circles.
How to construct Pareto diagrams
Step 1: Clarify the objectives of
constructing a Pareto diagram.
Step 2: Clarify the stratified
items of collected data within the problem.
Step 3: Design a data tally sheet
listing the items with their totals
Step 4: Fill out the tally sheet
and calculate the totals.
Step 5: Make a Pareto diagram data
sheet listing the items, their individual totals, cumulative totals,
percentages of overall total, and cumulative percentages.
Step 6: Arrange the items in terms
of number of occurrences and fill out the data sheet. The item “others” should
be on the last line, no matter how large it is. This is because it is a
collection of items for which the largest number of occurrences of any one item
is smaller than that for the smallest of the individually listed items.
Step 7: Construct a Pareto diagram
from the Pareto diagram data sheet.
1. Draw two vertical axes, marking
the left-hand vertical axis with a scale from 0 to the overall total and the
right-hand with a scale from 0% to 100%.
2. Draw a horizontal axis. Then
construct a bar diagrams, dividing the horizontal axis according to the numbers
of items.
3. Draw the cumulative curve
(Pareto curve)
Step 8: Add necessary information
regarding the diagram: title, significant quantities, units, sampling period,
subject and place of data collected, total number of data, etc.
3. Cause and effect diagrams (Ishikawa diagrams)
Cause and Effect Analysis was
devised by professor Kaoru Ishikawa, a pioneer of quality management, in the
1960s. The technique was then published in his 1990 book, “Introduction to
Quality Control.”
The diagrams that you create with
Cause and Effect Analysis are known as Ishikawa Diagrams or Fishbone Diagrams
(because a completed diagram can look like the skeleton of a fish). Cause and
Effect Analysis was originally developed as a quality control tool, but you can
use the technique just as well in other ways. For instance, you can use it to:
1. Discover the root cause of a
problem.
2. Uncover bottlenecks in your
processes.
3. Identify where and why a process
isn’t working.
Step 1: Identify the Problem
First, write down the exact problem
you face. Where appropriate, identify who is involved, what the problem is, and
when and where it occurs.
Then, write the problem in a box on
the left-hand side of a large sheet of paper, and draw a line across the paper
horizontally from the box. This arrangement, looking like the head and spine of
a fish, gives you space to develop ideas.
Step 2: Work Out the Major Factors
Involved
Identify all of the main categories
of causes of the problem, for example, man, method, materials, machine, and
environment. In figure below, these factors were grouped as appliances,
occupants, household procedures, and household food supplies. Use branch arrows
to connect the categories to the main arrow.
Example:
Identifies the following factors,
and adds these to his diagram:
1. Household Procedures.
2. Food Supply
3. Materials Electric Appliances
4. Occupants.
Step
3: Identify Possible Causes
Now, for each of the factors you
considered in step 2, brainstorm possible causes of the problem that may be
related to the factor. Show these
possible causes as shorter lines coming off the “bones” of the diagram. Where a
cause is large or complex, then it may be best to break it down into
sub-causes. Show these as lines coming off each cause line.
This diagram composed of lines and
symbols is designed to represent the relationship between effects and their
causes. It is sometimes called an Ishikawa diagram, after Dr. Kaoru Ishikawa
who is considered the father of QC Circles. Others call it a fishbone diagram
due to its resemblance to a fish skeleton. It is a very effective tool for
analyzing the causes of a problem, even household problems like high
consumption of electricity.
4. Control chart.
Control charts, first proposed by
W. A. Shewhart of Bell Telephone Laboratories in 1924, are used for maintaining
both process and manufacturing control in a stable condition. The control chart is a graph used to study how
a process changes over time. Data are plotted in time order. A control chart
always has a central line for the average, an upper line for the upper control
limit and a lower line for the lower control limit. These lines are determined
from historical data. By comparing current data to these lines, you can draw
conclusions about whether the process variation is consistent (in control) or
is unpredictable (out of control, affected by special causes of variation).
Control charts for variable data
are used in pairs. The top chart monitors the average, or the centering of the
distribution of data from the process. The bottom chart monitors the range, or
the width of the distribution. If your data were shots in target practice, the
average is where the shots are clustering, and the range is how tightly they
are clustered. Control charts for attribute data are used singly.
What
are the types of Control Chart.
There are two main categories of
Control Charts, those that display attribute data, and those that display
variables data.
Attribute
Data:
This category of Control Chart
displays data that result from counting the number of occurrences or items in a
single category of similar items or occurrences. These “count” data may be
expressed as pass/fail, yes/no, or presence/absence of a defect.
Attribute
type control charts include:
1. c Control chart
2. np Control chart
3. p Control chart (fraction
defective) and q control chart
4. u Control chart (number
defective)
Variables
Data:
This category of Control Chart
displays values resulting from the measurement of a continuous variable.
Examples of variables data are elapsed time, temperature, and radiation dose.
While these two categories encompass a number of different types of Control Charts
, there are three types that will work for the majority of the data analysis
cases you will encounter. The most common type of variable control charts are X
charts.
1. X-mR- Individuals and moving
range control chart
2. X bar R – Average and Range Control
chart
3. X bar S- Average and Standard
deviation control chart
4. X median R- Median and Range
control chart
5. I-MR-R – Individuals, Moving
range (between) and Range (within)
5. Histogram
A histogram is a bar graph of raw
data that creates a picture of the data distribution. The bars represent the
frequency of occurrence by classes of data. A histogram shows basic information
about the data set, such as central location, width of spread, and shape. Use histograms to assess the system’s current situation and to study
results of improvement actions. The histogram’s shape and statistical
information help you decide how to improve the system. If the system is stable,
you can make predictions about the future performance of the system. After
improvement action has been carried out, continue collecting data and making
histograms to see if the theory has worked.
When should we use a Histogram?
1. Summarize large data sets
2. Compare process results with
specification limits
3. Communicate information
graphically.
1. Title: The title briefly
describes the information that is contained in the Histogram.
2. Bars: The bars have two
important characteristics—height and width. The height represents the number of
times the values within an interval occurred. The width represents the length
of the interval covered by the bar. It is the same for all bars.
3. Horizontal or X-Axis: The
horizontal or X-axis shows you the scale of values into which the measurements
fit. These measurements are generally grouped into intervals to help you
summarize large data sets. Individual data points are not displayed.
4. Vertical or Y-Axis: The vertical
or Y-axis is the scale that shows you the number of times the values within an
interval occurred. The number of times is also referred to as “frequency.”
Constructing a Histogram
Step 1 – Count the total number of
data points you have listed. Suppose your team collected data on the miss
distance for the gunnery exercise described in the example. The data you
collected was for the fall of shot both long and short of the target. Simply counting the total number of entries in
the data set completes this step. In this example, there are 135 data points.
Step 2 – Summarize your data on a
tally sheet. You need to summarize your data to make it easy to interpret. You
can do this by constructing a tally sheet First, identify all the different
values found in Viewgraph 6 (-160, -010. . .030, 220, etc.). Organize these
values from smallest to largest (-180, -120. . .380, 410).
Then, make a tally mark next to the
value every time that value is present in the data set. Alternatively, simply
count the number of times each value is present in the data set and enter that
number next to the value, as shown in fig. This tally helped us organize 135
mixed numbers into a ranked sequence of 51 values. Moreover, we can see very
easily the number of times that each value appeared in the data set. This data
can be summarized even further by forming intervals of values.
6. Check sheets
A check sheet is a structured,
prepared form for collecting and analyzing data. This is a generic tool that
can be adapted for a wide variety of purposes.
1. When data can be observed and
collected repeatedly by the same person or at the same location.
2. When collecting data on the
frequency or patterns of events, problems, defects, defect location, defect
causes, etc.
3. When collecting data from a
production process.
Check Sheet
Procedure
1. Decide what event or problem
will be observed. Develop operational definitions.
2. Decide when data will be
collected and for how long.
3. Design the form. Set it up so
that data can be recorded simply by making check marks or Xs or similar symbols
and so that data do not have to be recopied for analysis.
4. Label all spaces on the form.
5. Test the check sheet for a short
trial period to be sure it collects the appropriate data and is easy to use.
6. Each time the targeted event or
problem occurs, record data on the check sheet.
Stratification is a technique used
in combination with other data analysis tools. When data from a variety of
sources or categories have been lumped together, the meaning of the data can be
impossible to see. This technique separates the data so that patterns can be
seen.
When to Use Stratification
1. Before collecting data.
2. When data come from several
sources or conditions, such as shifts, days of the week, suppliers or
population groups.
3. When data analysis may require
separating different sources or conditions.
Stratification Procedure
1. Before collecting data,
consider which information about the sources of the data might have an effect
on the results. Set up the data collection so that you collect that information
as well.
2. When plotting or graphing the
collected data on a scatter diagram, control chart, histogram or other analysis
tool, use different marks or colors to distinguish data from various sources.
Data that are distinguished in this way are said to be “stratified.”
3. Analyze the subsets of
stratified data separately. For example, on a scatter diagram where data are
stratified into data from source 1 and data from source 2,
draw quadrants, count points and determine the critical value only for the data
from source 1, then only for the data from source 2.
Stratification Example
The ZZ-400 manufacturing team drew
a scatter diagram to test whether product purity and iron contamination were
related, but the plot did not demonstrate a relationship. Then a team member
realized that the data came from three different reactors. The team member Redrew
the diagram, using a different symbol for each reactor’s data: Now patterns can
be seen. The data from reactor 2 and reactor 3 are circled. Even without doing
any calculations, it is clear that for those two reactors, purity decreases as
iron increases. However, the data from reactor 1, the solid dots that are not
circled, do not show that relationship. Something is different about reactor 1.
Stratification
Considerations
Here are examples of different
sources that might require data to be stratified:
1. Equipment
2. Shifts
3. Departments
4. Materials
5. Suppliers
6. Day of the week
7. Time of day
8. Products
Survey data usually benefit from
stratification. Always consider before collecting data whether stratification might
be needed during analysis. Plan to collect stratification information. After
the data are collected it might be too late.
No comments:
Post a Comment