This document provides an introduction and overview of various quality control tools used in Total Quality Management (TQM). It defines TQM as a comprehensive organization-wide effort to improve quality of products and services. Key concepts covered include meeting customer requirements, doing things right the first time, consistency, and continuous improvement. Seven basic quality tools are introduced: cause and effect diagrams, check sheets, control charts, flow charts, histograms, Pareto diagrams, and scatter diagrams. Each tool is defined and its uses and procedures for implementation are described.

1. Prepared by Tahani AL-Yamani
* Total Quality Management

2. *Introduction To TQM
What is TQM?
*A comprehensive, organization-wide effort to improve the
quality of products and services, applicable to all
organizations.
sweeping “culture change” efforts to position a company for
greater customer satisfaction, profitability and
competitiveness.

3. *Total Quality Is…
*Meeting Our Customer’s Requirements
*Doing Things Right the First Time; Freedom from
Failure (Defects)
*Consistency (Reduction in Variation)
*Continuous Improvement
*Quality in Everything We Do

4. *Seven Basic Quality Tools
1. Cause and effect diagram.
2. Check Sheet.
3. Control Chart.
4. Flow chart
5. Histogram
6. Pareto Diagram
7. Scatter diagram

5. *Cause and Effect Diagram
*The cause and effect diagram is an investigative tool.
* It is also known by the name of (Ishikawa diagram),
(Fishbone diagram).
*This diagram is helpful in representing the relationship
between an effect and the potential or possible causes
that influences it.
*This is very much helpful when one want to find out the
solution to a particular problem that could have a
number of causes for it and when we are interested in
finding out the root cause for it.

6. *Cause-and-Effect Diagram
Quality
Problem
Out of adjustment
Tooling problems
Old / worn
Machines
Faulty
testing equipment
Incorrect specifications
Improper methods
Measurement
Poor supervision
Lack of concentration
Inadequate training
Human
Deficiencies
in product design
Ineffective quality
management
Poor process design
Process
Inaccurate
temperature
control
Dust and Dirt
Environment
Defective from vendor
Not to specifications
Material-
handling problems
Materials

7. * Procedure
1. Agree on a problem statement (effect). Write it at the center right of the
flipchart or whiteboard. Draw a box around it and draw a horizontal arrow
running to it.
2. Brainstorm the major categories of causes of the problem. If this is difficult
use generic headings:
* Methods
* Machines (equipment)
* People (manpower)
* Materials
* Measurement
* Environment
3. Write the categories of causes as branches from the main arrow.
4. Brainstorm all the possible causes of the problem. Ask: “Why does this
happen?” As each idea is given, the facilitator writes it as a branch from the
appropriate category. Causes can be written in several places if they relate to
several categories.
5. Again ask “why does this happen?” about each cause. Write sub-causes
branching off the causes. Continue to ask “Why?” and generate deeper levels
of causes. Layers of branches indicate causal relationships.
6. When the group runs out of ideas, focus attention to places on the chart where
ideas are few.

8. *Check Sheets
*These are data collection forms that facilitate the
interpretation of data. Quality-related data are of two
general types: Attribute Data (obtained by counting or from
some type of visual inspection) and Variable Data (collected
by numerical measurement on a continuous scale.

9. *Check Sheets
Description
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.
When To use
When data can be observed and collected repeatedly by the same person or at the
same location.
When collecting data on the frequency or patterns of events, problems, defects,
defect location, defect causes, etc.
When collecting data from a production process.

10. *Check Sheets
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.

11. *Check Sheets
USES
*to gather data
*to test a theory
*to evaluate alternate solutions
*to verify that whatever improvement process you implement
continues to work
STEPS
* team agrees on what to observe
* decide who collects data
* decide time period for collecting data
* design Check Sheet
* collect data
* compile data in the Check Sheet
* review Check Sheet

12. 12
*Check Sheet
COMPONENTS REPLACED BY LAB
TIME PERIOD: 22 Feb to 27 Feb 2002
REPAIR TECHNICIAN: Bob
TV SET MODEL 1013
Integrated Circuits ||||
Capacitors |||| |||| |||| |||| |||| ||
Resistors ||
Transformers ||||
Commands
CRT |
• Check sheets are nothing but forms that can be used to systematically
collect data.
• Check sheet give the user a place to start and provides the steps to be
followed in Collecting the data

13. *Control Chart
*Control charts are considered as the backbone of
statistical process control and were first proposed
by Walter Shewhart.

14. *Control Chart
* Description
* 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).
14

15. *Control Chart
Process variations can be one of two types:
Random variations which are created by many minor factors.
Assignable variations whose main source can be identified and corrected.
There are four types of control charts:
Control Charts for Variables. Variables are measured such as length of time a certain
item is out of stock. Control charts can be:
Mean Control Charts (central tendency of process)
Range Control Charts (variability of process)
Control Charts for Attributes. Attributes are counted such as number of service calls
and number of returns on an item. Control charts can be:
p-charts (percent failure)
c-Charts (number of defects)

16. *Control Chart
When To use
When controlling ongoing processes by finding and correcting problems as they
occur.
When predicting the expected range of outcomes from a process.
When determining whether a process is stable (in statistical control).
When analyzing patterns of process variation from special causes (non-routine
events) or common causes (built into the process).
When determining whether your quality improvement project should aim to
prevent specific problems or to make fundamental changes to the process.

17. *Control Chart
Procedure
1. Choose the appropriate control chart for your data.
2. Determine the appropriate time period for collecting and plotting data.
3. Collect data, construct your chart and analyze the data.
4. Look for “out-of-control signals” on the control chart. When one is identified, mark
it on the chart and investigate the cause. Document how you investigated, what you
learned, the cause and how it was corrected.
5. Continue to plot data as they are generated. As each new data point is plotted,
check for new out-of-control signals.
6. When you start a new control chart, the process may be out of control. If so, the
control limits calculated from the first 20 points are conditional limits. When you
have at least 20 sequential points from a period when the process is operating in
control, recalculate control limits.

18. *Control Chart
18
12
6
3
9
15
21
24
2 4 6 8 10 12 14 16
Sample number
Numberofdefects
UCL = 23.35
LCL = 1.99
c = 12.67
A control chart is nothing but a run chart with limits. This is helpful in finding
the amount and nature of variation in a process.

19. Histograms do not
take into account
changes over
time.
Control charts can
tell us when a
process changes

20. *Flowcharts
*This is a picture of a process that shows the sequence of steps
performed. It is also called a process map.
*Flow charts give in detail the sequence involved in the
material, machine and operation that are involved in the
completion of the process.
*Thus, they are the excellent means of documenting the steps
that are carried out in a process.
Operation Decision
Start/
Finish
Start/
Finish
Operation
OperationOperation
Operation
Decision

21. *Flowcharts
*When To Use
* To develop understanding of how a process is done.
* To study a process for improvement.
* To communicate to others how a process is done.
* When better communication is needed between people involved with
the same process.
* To document a process.
* When planning a project.

22. *Flowcharts
Procedure
1. Define the process to be diagrammed. Write its title at the top of the work
surface.
2. Discuss and decide on the boundaries of your process: Where or when does
the process start? Where or when does it end? Discuss and decide on the level
of detail to be included in the diagram.
3. Brainstorm the activities that take place. Write each on a card or sticky note.
Sequence is not important at this point, although thinking in sequence may
help people remember all the steps.
4. Arrange the activities in proper sequence.
5. When all activities are included and everyone agrees that the sequence is
correct, draw arrows to show the flow of the process.
6. Review the flowchart with others involved in the process (workers,
supervisors, suppliers, customers) to see if they agree that the process is
drawn accurately.

23. *ExampleofaFlowchart

24. *Histograms
*This 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.
*It provides clues about the characteristics of the population
from which a sample is taken.

25. *Histogram
0
5
10
15
20
1 2 6 13 10 16 19 17 12 16 2017 13 5 6 2 1
Histograms help in understanding the variation in the process. It also helps
in estimating the process capability.

26. *Histogram
* Description
* A frequency distribution shows how often each different value in a set of data
occurs. A histogram is the most commonly used graph to show frequency
distributions. It looks very much like a bar chart, but there are important
differences between them.
26
Histogram

27. *Histogram
When to Use
When the data are numerical.
When you want to see the shape of the data’s distribution, especially when
determining whether the output of a process is distributed approximately normally.
When analyzing whether a process can meet the customer’s requirements.
When analyzing what the output from a supplier’s process looks like.
When seeing whether a process change has occurred from one time period to another.
When determining whether the outputs of two or more processes are different.
When you wish to communicate the distribution of data quickly and easily to others.

28. *Histogram
Histogram construction
Collect at least 50 consecutive data points from a process.
Use the histogram worksheet to set up the histogram. It will help you determine the
number of bars, the range of numbers that go into each bar and the labels for the
bar edges. After calculating W in step 2 of the worksheet, use your judgment to
adjust it to a convenient number. For example, you might decide to round 0.9 to an
even 1.0. The value for W must not have more decimal places than the numbers
you will be graphing.
Draw x- and y-axes on graph paper. Mark and label the y-axis for counting data values.
Mark and label the x-axis with the L values from the worksheet. The spaces
between these numbers will be the bars of the histogram. Do not allow for spaces
between bars.
For each data point, mark off one count above the appropriate bar with an X or by
shading that portion of the bar.

29. *Histogram
* A graph which presents the collected data as a frequency distribution in
bar-chart form
Complaint Type
0
1
2
3
4
5
6
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8
9
JanuaryFebruaryM
arch
April
M
ay
June
JulyAugust
Septem
berO
ctober
N
ovem
ber
D
ecem
ber
Month
Frequency
Late
Wrong
Faulty
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30. *Histogram
Histogram Analysis
Before drawing any conclusions from your histogram, satisfy yourself that the process
was operating normally during the time period being studied. If any unusual events
affected the process during the time period of the histogram, your analysis of the
histogram shape probably cannot be generalized to all time periods.
Analyze the meaning of your histogram’s shape.

31. *Histogram
* Normal
* A common pattern is the bell-shaped curve known as the “normal distribution.” In a
normal distribution, points are as likely to occur on one side of the average as on the
other. Be aware, however, that other distributions look similar to the normal distribution.
Statistical calculations must be used to prove a normal distribution.
* Don’t let the name “normal” confuse you. The outputs of many processes—perhaps even
a majority of them—do not form normal distributions , but that does not mean anything is
wrong with those processes. For example, many processes have a natural limit on one
side and will produce skewed distributions. This is normal — meaning typical — for those
processes, even if the distribution isn’t called “normal”!
31
Histogram

32. *Histogram
* Skewed
* The skewed distribution is asymmetrical because a natural limit prevents outcomes
on one side. The distribution’s peak is off center toward the limit and a tail
stretches away from it. For example, a distribution of analyses of a very pure
product would be skewed, because the product cannot be more than 100 percent
pure. Other examples of natural limits are holes that cannot be smaller than the
diameter of the drill bit or call-handling times that cannot be less than zero. These
distributions are called right- or left-skewed according to the direction of the tail.
32
Histogram

33. *Histogram
* Double Peak or Bimodal
* The bimodal distribution looks like the back of a two-humped camel. The outcomes
of two processes with different distributions are combined in one set of data. For
example, a distribution of production data from a two-shift operation might be
bimodal, if each shift produces a different distribution of results. Stratification
often reveals this problem.
33

34. *Pareto Diagrams
*Pareto analysis is a technique for prioritizing types or sources
of problems. It separates the “vital few” from the “trivial
many” and provides help in selecting directions for
improvement.

35. *Pareto Diagrams
* Description
* A Pareto chart is a bar graph. The lengths of the bars represent frequency or cost
(time or money), and are arranged with longest bars on the left and the shortest to
the right. In this way the chart visually depicts which situations are more significant.
* Often called the 80-20 Rule
* Principle is that quality problems are the result of only a few problems e.g. 80% of the
problems caused by 20% of causes
35

36. *Pareto Diagrams
When to Use
When analyzing data about the frequency of problems or causes in a process.
When there are many problems or causes and you want to focus on the most
significant.
When analyzing broad causes by looking at their specific components.
When communicating with others about your data.

37. *Pareto Diagrams
Procedure
1. Decide what categories you will use to group items.
2. Decide what measurement is appropriate. Common measurements are frequency,
quantity, cost and time.
3. Decide what period of time the Pareto chart will cover: One work cycle? One full
day? A week?
4. Collect the data, recording the category each time. (Or assemble data that already
exist.)
5. Subtotal the measurements for each category.
6. Determine the appropriate scale for the measurements you have collected. The
maximum value will be the largest subtotal from step 5. (If you will do optional
steps 8 and 9 below, the maximum value will be the sum of all subtotals from step
5.) Mark the scale on the left side of the chart.
7. Construct and label bars for each category. Place the tallest at the far left, then
the next tallest to its right and so on. If there are many categories with small
measurements, they can be grouped as “other.”

38. *Example of a Pareto
Diagram

39. NUMBER OF
CAUSE DEFECTS PERCENTAGE
Poor design 80 64 %
Wrong part dimensions 16 13
Defective parts 12 10
Incorrect machine calibration 7 6
Operator errors 4 3
Defective material 3 2
Surface abrasions 3 2
125 100 %
*Pareto Analysis

40. Percentfromeachcause
Causes of poor quality
0
10
20
30
40
50
60
70
(64)
(13)
(10)
(6)
(3) (2) (2)
*Pareto Diagrams

41. *Scatter Diagrams
*Scatter diagrams illustrate relationships between variables.
Typically the variables represent possible causes and effects
obtained from cause-and-effect diagrams.

42. *Scatter Diagram
Y
X
It is a graph of points plotted; this graph is helpful in comparing two
variables.
The distribution of the points helps in identifying the cause and effect
relationship Between two variables.

43. *Scatter Diagram
Procedure
1. Collect pairs of data (50-100) where a relationship is suspected.
2. Draw a graph with the independent variable on the horizontal axis and the
dependent variable on the vertical axis. For each pair of data, put a dot or a
symbol where the x-axis value intersects the y-axis value. (If two dots fall
together, put them side by side, touching, so that you can see both.)
3. Look at the pattern of points to see if a relationship is obvious. If the data clearly
form a line or a curve, you may stop. The variables are correlated. You may wish
to use regression or correlation analysis now.

44. *Scatter Diagram
When to Use
When you have paired numerical data.
When your dependent variable may have multiple values for each value of your
independent variable.
When trying to determine whether the two variables are related, such as…
When trying to identify potential root causes of problems.
After brainstorming causes and effects using a fishbone diagram, to determine objectively
whether a particular cause and effect are related.
When determining whether two effects that appear to be related both occur with the same
cause.
When testing for autocorrelation before constructing a control chart

45. *Scatter Diagram
Scatter Diagram for faulty installations
0
20
40
60
80
100
120
140
160
180
0 1 2 3 4 5 6 7
Number of faulty installations
Numberofinstallationspercrew
* A graphical tool to check if two relationships exist between two variables.
45
Cause Variable
EffectVariable

46. *Scatter Diagram
Positive Correlation Negative Correlation No Correlation

47. *Summary
Continuous improvement is driven by the need to solve problems
effectively:
Get to the root cause
Use improvement methodology PDCA
Use data, not opinion
Use quality tools to collect and analyze data

48. *THANK YOU !!