2. The 7 Basic Quality Tools
Ishikawa believed that 90% of all quality
problems could be solved through the use
of the 7 tools listed below:
Cause and Effect (Ishikawa) Diagrams
Check Sheets
Histrogram
Pareto diagrams
Flowcharts
Scatter Diagrams
Control Charts
3. What are the
Basic Seven Q.C. Tools?
Cause and Effect (Ishikawa)
Diagrams
-Brain Storming
Check Sheets
Frequency Diagrams
Pareto diagrams
Flowcharts
Scatter Diagrams
5. Brainstorming
Ideas generation
Time the session
Count of ideas generated
Named and anonymous ideas
Each idea depicted as node of a tree
Simple session
Free generation of ideas
Structured session
Predefined set of questions defined as a template
Generate ideas under each question
Brain Storming
6. Grouping
Print labels in background to involve participants in
grouping of ideas
Incubation
Print idea labels
Print list of ideas
Clarification
Add description to clarify the idea
Brain Storming
7. Preliminary Selection
Mark an idea/group/sub-group as selected
depicted by a check mark (May be carried
out while grouping the ideas)
Filtering and Prioritization
Criteria definition
Multi voting
Multi criteria voting and selection
Decision grid
Group weightage analysis thru ideas
ranking
Drivers and Barriers analysis and Final
selection of ideas
Brain Storming
12. Frequency Diagrams
Process centering, spread and shape
Frequency Diagram
with tolerances
0
2
4
6
8
10
12
100 -
109
110 -
119
120 -
129
130 -
139
140 -
149
150 -
159
160 -
169
170 -
179
180 -
189
190 -
199
200 -
209
210 -
219
Frequency
LowerTolerance(125)
UpperTolerance(185)
Spec.Spec.
(155)(155)
13. Frequency Diagrams
Process centering, spread and shape
Normal Distribution
Bi-Modal Distribution Multi-Modal Distribution
Positively Skewed Negatively Skewed
15. Cause and Effect Analysis
Ishikawa fish bone diagramming
Easy to draw like on paper
5 Why’s technique
Why?… Why?…. Why?…. Why?…….. Why?
Identification of Root causes
Mark a cause as a root cause
Assign a priority number
Cause and Effect DiagramsCause and Effect Diagrams
Find and cure causes NOT symptomsFind and cure causes NOT symptoms
16. Possible CausesPossible Causes
PROBLEMPROBLEM
(EFFECT)(EFFECT)
Area AArea A Area BArea B
Area DArea DArea CArea C
11
22
33
66
55
44
11
22
33
66
55
44
11
22
33
66
55
44
11
22
33
66
55
44
Cause and Effect DiagramsCause and Effect Diagrams
Find and cure causes NOT symptomsFind and cure causes NOT symptoms
17. Bad Solder
Joints
Machines Manpower
MaterialsMethods
Solder Gun
Size
Heat sink
Power Source
Skill
Training
Physical
limits
Terminals
Stripping
Technique
Manual
Flux
Solder
Wire Gauge
Cause and Effect DiagramsCause and Effect Diagrams
Find and cure causes NOT symptomsFind and cure causes NOT symptoms
18. Bad Solder
Joints
Machines Manpower
MaterialsMethods
Solder Gun
Size
Heat sink
Power Source
Skill
Training
Physical
limits
Terminals
Stripping
Technique
Manual
Flux
Solder
Wire Gauge
Cause and Effect DiagramsCause and Effect Diagrams
Find and cure causes NOT symptomsFind and cure causes NOT symptoms
20. contd…
Check Sheets and data analysis
Facility to design a Check sheet
Qualitative data
Quantitative data with summation, averages etc.
Data Collection facility
Data Analysis
Pareto chart
Scatter diagrams
Stratification
Bar graph
Line graph
Pie chart
Printing of check sheet(s) and collected
data
Check Sheets
Counting and accumulating data
21. Products Faults
Fault 1 Fault 2 Fault 3 Total
A | | | | | | 6
B | | 2
C | | | 3
D | | | | | 5
Total 8 3 5 16
Check Sheets
Counting and accumulating data
24. Pareto Diagrams
Focus on key problems
Fault No.
A. DRY JOINT
B. MISSED COMPONENT
C. REVERSED COMPONENT
D. ARCING
E. OPEN CIRCUIT
F. OTHER
2
5
8
4
1
3
TOTAL 23
25. Fault No.
C. REVERSED COMPONENT
B. MISSED COMPONENT
D. ARCING
F. OTHER
A. DRY JOINT
E. OPEN CIRCUIT
8
5
4
3
2
1
TOTAL 23
Pareto Diagrams
Focus on key problems
27. Fault No. COST TOTAL
COST
C. REVERSED COMPONENT
B. MISSED COMPONENT
D. ARCING
F. OTHER
A. DRY JOINT
E. OPEN CIRCUIT
8
5
4
3
2
1
2
2
5
1
4
6
16
10
20
3
8
6
TOTAL 23 - 63
Pareto Diagrams
Focus on key problems
28. Fault No. COST TOTAL
COST
D. ARCING
C. REVERSED COMPONENT
B. MISSED COMPONENT
A. DRY JOINT
E. OPEN CIRCUIT
F. OTHER
4
8
5
2
1
3
5
2
2
4
6
1
20
16
10
8
6
3
TOTAL 23 - 63
Pareto Diagrams
Focus on key problems
31. Flow charting
Drawing of a Process-chart with all standard step
symbols
Facility to define a Template Form for capturing
measurement data about each step
Summary report of the Process-chart
Facility to draw a Process map
Facility of dragging the steps of a Process-chart to a
Brainstorming session for analysis of each step
under a predefined template of questions
Flowcharts
Picturing the process
32. Terminator
Used to Start/Stop
flowchart
Data
Used to indicate data
in or out of the process
Activity
Used to show a task or
activity performed in the
process.
Decision
Shows the points in the
process where yes/no
questions are asked
Delay
Used to indicate delays
or stock points in the
process.
Connector
Used to link different
Points in the flowchart.A
Flowcharts
Picturing the process
36. 3.5
4
4.5
5
150 400 650
3.5
4
4.5
5
150 400 650
3.5
4
4.5
5
150 400 650
3.5
4
4.5
5
150 400 650
3.5
4
4.5
5
150 400 650
PositivPositive Correlation
Positive Correlation?
Negative Correlation
Negative Correlation?
No Correlation
An increasAn increase in y may depend
upon an increase in x.
E.g.
If X is increased, y may also
increase.
If X is increased, y may
decrease.
There is no demonstrated
connection between x and y.
An decrease in y may depend
upon an increase in x.
Scatter Diagrams
Measuring relationships between variables
38. Control Charts
Recognising sources of variation
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6 7 8 9 10
Lower Control
Limit (LCL)
Upper Control
Limit (UCL)
Average
(Xbar)
39. Control Charts
Recognising sources of variation
Variables data
Temperature
Length
Cost
Attributes data
Number of porous castings in a
sample (defective parts)
Number of cavities in a porous
casting (defects)
Shipping errors
40. Choose appropriate
control chart
Attribute data:
Counted and
plotted as
discrete events
Variable data:
Measured and
plotted on a
continuous scale
Control Charts
Recognising sources of variation
41. Attribute Data
Defect Data Defective Data
Defect = failure to meet
one of the acceptance
criteria.
Defective = An entire
unit fails to meet
acceptance criteria.
(Defectives may have
multiple defects)
Constant
sample size,
usually > 5c
c Chart
Variable
sample size,
u Chart
Constant
sample size,
usually ≥ 50
Variable
sample size,
usually ≥ 50
np Chart p Chart
Fraction defectiveNumber defectiveNumber of defects Number of defects
per unit
Control Charts
Recognising sources of variation
42. Variable Data
Sample size
= 1
X and Rm
Sample size
is small, 2~9
Sample size is
large
usually ≥ 10
and RX and sX
Individuals and
moving range
Average and
standard deviation
Average and Range
Control Charts
Recognising sources of variation
43. Control Charts
Recognising sources of variation
Point above or below
control limit.
Causes: “special cause”,
misread, data entry, etc.
Run 7 points increasing
or decreasing
Causes: maintenance, wear,
environment, etc
Run of 7 points above
or below the mean
Causes:changed dist’n,
new method, etc.
Erratic readings
Causes: over adjustment,
measuring equipment not
capable, etc
Shift in readings
Causes: change in mat’l,
change in operator, etc
Cyclic readings
Causes: work pattern,
Environment, etc.
GroupingFlier Shift
Trend Erratic Cycle
44. • Cause and Effect (Ishikawa) DiagramsCause and Effect (Ishikawa) Diagrams
-Brain Storming-Brain Storming
• Check SheetsCheck Sheets
• Frequency DiagramsFrequency Diagrams
• Pareto diagramsPareto diagrams
• FlowchartsFlowcharts
• Scatter DiagramsScatter Diagrams
• Control ChartsControl Charts
7- QC ToolS (BASIC)
45. Cause and Effect Diagram
Enables a team to focus on the content of a
problem, not on the history of the problem or
differing personal interests of team members
Creates a snapshot of collective knowledge
and consensus of a team; builds support for
solutions
Focuses the team on causes, not symptoms
Effect
Cause
46. Check Sheet
Creates easy-to-understand data
Builds, with each observation, a clearer
picture of the facts
Forces agreement on the definition of
each condition or event of interest
Makes patterns in the data become
obvious quickly xx
xxxxxx
x
47. Histogram
Displays large amounts of data that are
difficult to interpret in tabular form
Shows centering, variation, and shape
Illustrates the underlying distribution of the
data
Provides useful information for predicting
future performance
Helps to answer the question “Is the process
capable of meeting requirements?
48. Pareto Diagram
Helps a team focus on causes that
have the greatest impact
Displays the relative importance of
problems in a simple visual format
Helps prevent “shifting the problem”
where the solution removes some
causes but worsens others
49. Flowcharts
Shows unexpected complexity, problem
areas, redundancy, unnecessary loops, and
where simplification may be possible
Compares and contrasts actual versus ideal
flow of a process
Allows a team to reach agreement on process
steps and identify activities that may impact
performance
Serves as a training tool
50. Control Chart
Focuses attention on detecting and
monitoring process variation over time
Distinguishes special from common
causes of variation
Serves as a tool for on-going control
Provides a common language for
discussion process performance
* *
*
* *
*
*
51. Run Chart
Monitors performance of one or more
processes over time to detect trends, shifts,
or cycles
Allows a team to compare performance
before and after implementation of a solution
to measure its impact
Focuses attention on truly vital changes in the
process
* *
*
* *
*
*
52. Scatter Diagram
Supplies the data to confirm a hypothesis
that two variables are related
Provides both a visual and statistical
means to test the strength of a relationship
Provides a good follow-up to cause and
effect diagrams
*
* *
* *
*
Flow Charts - pictorial representation showing all of the steps of a process
Run Charts - used to analyze processes according to time or order
Histograms - bar graphs with frequency intervals
Pareto Diagrams - 80/20 histograms for identifying and prioritizing problems
Cause and Effect Diagrams - fishbone diagrams
Scatter Diagrams - Identifies the possible relationship between the changes observed in two different sets of variables.
Control Charts - used to determine whether a process will produce a product or service with consistent measurable properties
The results of an inspection or an experiment often come in a form like this. In order to use these results we must first put them into some usable form. The most simple form is a “tally”.
A tally is a simple form of categorising the data so as to let it speak for itself. In the first column, we have the basic categories themselves: 100 – 109, 110 – 119, and so on. In the second column, there is the tally – the actual count of the number of items found in that category. In the third column is the actual number in the category, or the frequency.
We now have the data in a form which “speaks” to us. The next obvious step is to display the data on a graph.
This is the frequency diagram, or histogram, Suppose this data represented a sample of the output of a process. It would then give a good indication of the capability of the process.
If we add the tolerance limits to the graph, we can see that we are going to have a large proportion of rejects from the process. From this it is easy to see how vital the concept of the frequency diagram is to analysing process capabilities.
These are some common shapes for frequency distributions.
The first, a symmetrical shape, is very common and arises in many industrial processes.
The second, has two peaks which means that the data contains two distinct groups. This could be caused by two different materials in the study or the results could be from two different machines or that some other change occurred during the investigation.
The third has multiple peaks which is an indication that the process is chaotic, or out of control.
The fourth and fifth shapes are skewed……
Frequency diagrams have many uses. They can not only give us a picture of the output of our processes, but tell us something about the nature of the processes themselves and how well they are controlled.
When you go to the doctors with the a bad back, you might expect him to give you some pain killers (this is treating the symptom). After you have seen him for the fourth or fifth time with your bad back you might expect him to send you to see a specialist to find the root of the problem, e.g. a slipped disc, and he will treat that (again, this is treating the symptom). He might also ask you about how this could have happened and find that you work in heavy industry and have been lifting incorrectly. Retraining on the correct lifting techniques by your employer will stop this happening again (this is treating the cause of the problem – if this was spotted earlier you could have prevented the problem happening at all – but at least we can now stop it happening again).
Fishbone diagrams (a.k.a. Cause and effect diagrams & Ishikawa Diagram – after Kaoru Ishikawa, Japanese quality control pioneer).
EFFECTS are in general the problems that we have to solve.
But we will not get far unless we can determine the causes of the effects that we are seeing.
Once the causes are identified, then we can start to solve the problem.
This diagram shows that there can be many causes which contribute to the problem or effect.
Because of it’s appearance, we call this chart a ‘fishbone’ diagram.
In this example, I’ve added some causes and some common categories – know as the 4 “M’s” – Machines, Manpower, Methods and Materials.
How do we arrive at the possible causes? The best (and most common) method is brainstorming. This generates a large number of ideas in a short period of time.
Once the diagram is complete, then we can continue with the evaluation.
We obviously can not tackle all the problems at once because there are too many and besides some will have such small effects that they will not be worth bothering about.
On this diagram, three of the causes have been highlighted.
These are thought to be the most important, and the ones to tackle. WHY?
There are a number of ways of choosing the front runners.
We could design a series of experiments to determine the biggest influence, OR
We could use existing data or experience, OR
We could make a judgement purely on opinion
Check sheets are used to systematically record data from historical sources or from observations as they happen so that patterns and trends can be clearly detected and shown.
Check sheets minimise clerical effort since the operator merely adds a mark to the tally on the prepared sheet rather than writing out a figure.
By showing the frequency of a particular defect (e.g. in a moulded part) and how often it occurs on a specific day or specific location, an operator can spot fault “outbreaks” more easily.
The example above shows a list of moulded part defects on a production line over a week. We can easily see whereto set priorities based on the results shown in this check sheet.
Assuming that production flow is the same on each day, the largest “bins” carries the highest priority for correction.
This is named after Vilfredo Pareto who studies showed the 80/20 rule in economics. After studying the distribution of wealth in nations under different political systems he discovered that whatever the politics, meaning that 80% of the money is owned by 20% of the people.
Allows differentiation of the VITAL FEW from the TRIVIAL (USEFUL?) MANY.
This is a list of faults occurring in a process over one week.
You can see which are the main faults but only by looking at the list carefully. Also, if the list is large then this becomes more difficult.
What about putting them in order of the number of faults?
From this we can see immediately which are the most common faults.
This now does away with the need for careful study of the results.
This is the same information displayed in a graph, called a Pareto chart or diagram.
But are these the most important faults?
What if we consider the costs of the faults?
Here we have the total costs for each fault.
This now shows us the true impact of each fault but again it is in the wrong order.
This is the same information, but it is now in the right order.
Reading down the list we can easily see the real impact of each fault in terms of its contribution to total cost.
But again, we can still do more to visualise the contribution to the total cost.
This is the same information in graphical form.
The aim of Pareto diagrams is to show the relative importance of each fault type as an aid to developing an improvement project.
We’ve used cost in this example but you could equally use machine downtime (some faults may cause linger breakdowns) or any other factor as long as it represents the true measure of impact.
Flowcharts can:
Force teams to agree process steps and examine activities that may affect the performance.
Show where processes can be simplified.
Compares and contrasts the actual varsus the ideal flow to determine improvement opportunities.
Identifies locations at which data can be collected.
Serves as a training aid to understand the complete process.
Control charts are used to monitor, control and improve process performance by focussing on its variation and its cause.
The control chart can be thought of as a target. The average line is the bull's-eye and the control limits are the extremes of the target.
Control charts are used by taking periodic measurements or observations of products or processes. These results are compared with calculated control limits, and if the limits are exceeded action is taken (and recorded) to bring the process “back into control”.
There are two important distinctions to make here.
The first is the difference between variables and attributes:
Variables data is that which can be physically measured. I.e. something that can be measured on a scale.
Attributes data is that which there is only two outcomes, pass or fail; yes or no.
The second is the difference between defects and defective parts.
