# Total Quality Management TQM

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Total Quality Management ­ MGT510
VU
Lesson # 38
CAUSE AND EFFECT DIAGRAM AND OTHER TOOLS OF QUALITY
Histograms
Variation in a process always exists and generally displays a pattern that can be captured in a histogram.
A histogram is a graphical representation of the variation in a set of data. It shows the frequency or
number of observations of a particular value or within a specified group.
Histograms provide clues about the characteristics of the population from which a sample is taken.
Using a histogram, the shape of the distribution can be seen clearly, and inferences can be made about
the population. Patterns can be seen that would be difficult to see in an ordinary table of numbers. The
check sheet below was designed to provide the visual appeal of a histogram as the data are tallied. It is
easy to see how the output of the process varies and what proportion of output falls outside of any
specification limits.
Example of a Check Sheet for Variable Data
Frequency
20
19
18
17
16
15
14
X
13
X
12
X
X
11
X
X
X
10
X
X
X
9
X
X
X
X
8
X
X
X
X
7
X
X
X
X
6
X
X
X
X
X
5
X
X
X
X
X
4
X
X
X
X
X
X
X
3
X
X
X
X
X
X
X
2
X
X
X
X
X
X
X
1
X
X
X
X
X
X
X
X
X
X
123456
7
8
9
10
11
12
13 14 15 16 17 18 19 20
Time to process loan request (days)
Cause-and-Effect Diagrams
The most useful tool for identifying the causes of problems is a cause-and-effect diagram, also known as
a fishbone or Ishikawa diagram, named after the Japanese quality expert who popularized the concept.
A cause-and-effect diagram is simply a graphical representation of an outline that presents a chain of
causes and effects. A team typically uses a cause-and-effect diagram to identify and isolate causes of a
problem. The technique was developed by the late Dr. Kaoru Ishikawa, a noted Japanese quality expert.
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Total Quality Management ­ MGT510
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An example is shown in figure below. At the end of the horizontal line is the problem to be addressed.
Each branch pointing into the main stem represents a possible cause. Branches printing to the causes are
contributors to these causes. The diagram is sued to identity the most likely causes of a problem so that
further data collection and analysis can be carried out.
IDENTIFYING CAUSES
Identifying causes is a critical step in the process. It involves the pairing off of causes and effects.
Effects are the problems that have already been identified. Say that one such problem has been targeted
for solving. A fishbone diagram has six spines and represents the six major groupings of causes:
manpower (personnel). Method,
Manpower
Method
Materials
Environment
Machines
Measurement
Sample Cause-and-Effect Diagram
Materials, machines (equipment), measurement, and environment. All causes of work- place problems
fall into one of these major groupings. Using the diagram, team members' brainstorm causes under each
grouping. For example, under the machine grouping, a cause might be insufficient maintenance. Under
the manpower grouping, a cause might be insufficient training.
A Cause-and-Effect Diagram
Client
Time
Unclear
directions
Word
processing
errors
Inattention
Did not
No spell check
understand
directions
Training
Typist
Cause-and-effect diagrams are usually constructed in a brainstorming setting so that everyone can
contribute their ideas. Usually small groups drawn from operations or management work with an
experienced facilitator. The facilitator guides the discussion to focus attention on the problem and its
causes, on facts, not opinions. This method requires significant interaction among group members. The
facilitator must listen carefully to the participants and capture the important ideas.
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Total Quality Management ­ MGT510
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Free-Throwing Cause-And-Effect Diagram
Materials
People
Measurement
Regulation
backboard
Hit
Player
and rim
Nothing but net
Miss
Touch rim
Short
Coach
Coach
Right
Coach
Low free-
throw
shooting
percentage
Shooting
Video
Practice
position
camera
Indoor
Technique
Ritual
Games
Focus point
Outdoor
Environment
Equipment
Method
Scatter Diagram
Scatter diagrams illustrate relationships between variables, such as the percentage of an ingredient in an
alloy and the hardness of the alloy, or the number of employee errors and overtime worked (Figure
3.12). Typically the variables represent possible causes and an effect obtained from cause-and-effect
diagrams.A general trend of the points going up and to the right indicates that an increase in one
variable corresponds to an increase in the other. If the trend is down and to the right, an increase in one
variable corresponds to a decrease in the other. If no trend can be seen, then it would appear that the
variables are not related. Of course, any correspondence does not necessarily imply that a change in one
variable causes a change in the other. Both may be the result of something else. However, if there is
reason to believe causation, the scatter diagram may provide clues on how to improve the process.
Scatter Diagram
Number of
errors
Volume of work
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Total Quality Management ­ MGT510
VU
Control Charts
These tools are the backbone of statistical process control (SPC), and were first proposed by Walter
Shewhart in 1924. Shewhart was the first to distinguish between common causes and special causes in
process variation. He developed the control chart to identify the effects of special causes. Much of the
Deming philosophy is based on the use of control charts to understand variation.
A control chart displays the state of control of a process. Time is measured on the horizontal axis, and
the value of a variable on the vertical axis. A central horizontal line usually corresponds to the average
value of the quality characteristic being measured. Two other horizontal lines represent the upper and
lower control limits, chosen so that there is a high probability that sample values will fall within these
limits if the process is under control ­ that is, affected only by common causes of variation. If points fall
outside of the control limits or if unusual patterns such as shifts up or down, trends up or down, cycles,
and so forth exist, special causes may be present.
Two fundamental mistakes that can be made concerning variation are
1.
Treating special causes as common causes, and
2.
Treating common causes as special causes.
Control charts minimize the risk of making these two types of mistakes. As a problem-solving tool, they
allow workers to identify quality problems as they occur and base their conclusions on hard facts.
EXAMPLE OF A CONTROL CHART
Percent shipped
within 24 hours
Upper
97%
Control
Limit
Average
93%
Lower
Control
89%
Limit
Day
1
2
2
4
5
6
7
8
9 10 11 12 13
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