7 qc tools
TRANSCRIPT
7 QC TOOLS
Training ModuleTraining Module
7QC tools
Developed by Innovation Cell
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Types of 7QC tools
Check SheetCheck Sheet
Pareto DiagramPareto Diagram
Cause & Effect diagramCause & Effect diagram
StratificationStratification
Scatter DiagramScatter Diagram
Graph &Control chartsGraph &Control charts
HistogramHistogram
7 QC Tools7 QC Tools
Types
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Check sheetCheck sheet
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What is a check sheet?
Why is a check sheet necessary?
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Check sheets are forms used for
• standardizing
• checking results of work
• verifying and collecting data
Check sheet
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Check Sheet
Measured Data
Measured Data
Counted Data
Counted Data
Primary Data
Primary Data
Point Scale Data
Point Scale Data
Ordered Data
Ordered Data
Indiscrete value such as height, weight, length, time & temp., Etc.Indiscrete value such as height,
weight, length, time & temp., Etc.
Discrete value such as no. Of recording errors, no. of Item sold
& Rejections etc.
Discrete value such as no. Of recording errors, no. of Item sold
& Rejections etc.
YES / NO or / X - Type
YES / NO or / X - Type
1st, 2nd Order …Very Good, Good, No Good
… - Type
1st, 2nd Order …Very Good, Good, No Good
… - Type
1 Point, 2 Point … etc.
1 Point, 2 Point … etc.
Types of Check Sheet
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Below items can be added , as necessary
1. The purpose of the checks
2. The items being checked
3. The methods of the checks
4. The dates and times of the checks
5. The person to perform the checks
6. The results
Check points for check sheets preparation
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Defect check sheet
Example of check sheet
Month ,day
Component
1
2
3
4
5
6
7
8
9
10
4/1 2 3 4
No. of No. of defectsdefects
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• Vilfredo Pareto was an Italian engineer in the 19th Century who studied the number of people in various income classes & declared
‘’20% of the people own 80% of the country’s wealth;
80% of the people own 20% of the country’s wealth”
Pareto
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Pareto Principle
Pareto principle holds good to the present day in various applications
‘ A few causes lead to many defects;
many causes lead to few defects.’
The few causes that lead to many defects are the vital few.
The many causes that lead to few defects are the trivial many.
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“Get to the biggest problems first”
‘Solve the vital few’
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1. Collect data
2. Arrange data in the descending order
3. Calculate the relative % for individual data
4. Calculate the cumulative % for individual data
5. Draw a graph with scales on both axis
6. Draw bar chart based on data
7. Using cumulative % data, draw cumulative curve
8. Identify the VITAL FEW (thumb rule > 70%)
Creating a Pareto DiagramSteps
7 QC TOOLSData collection through check sheet
Period : Week No. 45 To 50
No. Of External Phone Calls
Sl.No Department No. Of cells regd.
1 Production Engineering 10
2 Quality 2
3 Service 12
4 Marketing 45
5 Plant Maintenance 20
6 Factory production 2
7 Manufacturing Planning 1
8 Stores 5
9 Personnel 8
10 Materials 66
11 Finance 15
12 Research & Development 4
13 Information & Systems 6
14 Others 4
STEP 1
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Sl.No Department No. Of cells reqd.
1 Materials 66
2 Marketing 45
3 Plant Maintenance 20
4 Finance 15
5 Service 12
6 Production Engineering 10
7 Personnel 8
8 Information Systems 6
9 Stores 5
10 Research & Development 4
11 Others 4
12 Quality 2
13 Factory production 2
14 Manufacturing Planning 1
200
Pareto
STEP 2 Arrange data in the descending orderNo. Of External Phone Calls
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Sl.No Department Nos. Relative %
1 Materials 66 33.0
2 Marketing 45 22.5
3 Plant Maintenance 20 10.0
4 Finance 15 7.5
5 Service 12 6.0
6 Production Engineering 10 5.0
7 Personnel 8 4.0
8 Information Systems 6 3.0
9 Stores 5 2.5
10 Research & Development 4 2.0
11 Others 4 2.0
12 Quality 2 1.0
13 Factory production 2 1.0
14 Manufacturing Planning 1 0.5
200
STEP 3 Calculate the relative % for individualNo. Of External Phone Calls
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Sl.No Department Nos. Relative % Cumulative %
1 Materials 66 33.0 33.0
2 Marketing 45 22.5 55.5
3 Plant Maintenance 20 10.0 65.5
4 Finance 15 7.5 73.0
5 Service 12 6.0 79.0
6 Production Engineering 10 5.0 84.0
7 Personnel 8 4.0 88.0
8 Information Systems 6 3.0 91.0
9 Stores 5 2.5 93.5
10 Research & Development 4 2.0 95.5
11 Others 4 2.0 97.5
12 Quality 2 1.0 98.5
13 Factory production 2 1.0 99.5
14 Manufacturing Planning 1 0.5 100.0
200 100
STEP 4 Calculate the cumulative % for individualData No. Of External Phone Calls
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Sl.No Department Nos. Relative % Cumulative %
1 Materials 66 33.0 33.0
2 Marketing 45 22.5 55.5
3 Plant Maintenance 20 10.0 65.5
4 Finance 15 7.5 73.0
5 Service 12 6.0 79.0
6 Production Engineering 10 5.0 84.0
7 Personnel 8 4.0 88.0
8 Information Systems 6 3.0 91.0
9 Stores 5 2.5 93.5
10 Research & Development 4 2.0 95.5
11 Others 4 2.0 97.5
12 Quality 2 1.0 98.5
13 Factory production 2 1.0 99.5
14 Manufacturing Planning 1 0.5 100.0
200 100
TRIVIAL MANY
VITAL FEW
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Sl.No Department Nos. Relative % Cumulative %
1 Materials 66 33.0 33.0
2 Marketing 45 22.5 55.5
3 Plant Maintenance 20 10.0 65.5
4 Finance 15 7.5 73.0
5 Others 60 27 100
200 100
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7 QC TOOLSVitalFew
70 %
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• To Clearly prioritise the magnitude of the problem.
• To identify the vital few and trivial many problems.
• To find 80/20 rule which states that 80% of the
problems are created by 20% of the causes.
Why pareto ?
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1. The most important problem
2. The rate of each problem to the whole
3. The degree of improvement action
4. The comparison of improvement level
5. Before & after remedial action taken
Pareto diagram is used to find out …
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BRAIN STORMING
BRAIN STORMING CAN BE CONDUCTED IN TWO WAYS
1. STRUCTURED
IN THIS METHOD EVERY PERSON IN A GROUP MUST GIVE AN IDEA AS THEIR TURN ARISES IN THE ROTATION OR PASS UNTIL THE
NEXT ROUND. IT OFTEN FORCES EVEN SHY PEOPLE TO PARTICIPATE AND ALSO CREATE A CERTAIN AMOUNT OF
PRESSURE TO CONTRIBUTE.
2. UNSTRUCTURED
IN THIS METHOD, GROUP MEMBERS SIMPLY GIVE IDEAS AS THEY COME TO MIND. IT TENDS TO CREATE MORE RELAXED
ATMOSPHERE BUT ALSO RISKS DOMINATION.
THUMB RULE : 5 – 15 MINUTRES WORKS WELL
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BRAIN STORMING
BRAIN STORMING IS A TECHNIQUE TO OBTAIN CREATIVE IDEAS FROM A GROUP OF PERSONS IN A SHORTEST POSSIABLE TIME ON AN EFFECT.
BRAIN STORMING PLAYS AN IMPORTANT ROLE TO BUILD A CAUSE AND EFFECT DIAGRAM
WHY TO IDENTIFY THE PROBLEM - TO IDENYIFY THE CAUSES
TO FIND SOLUTION - TO PREVENT PROBLEM
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BRAIN STROOMING SESSION
• Let all the members speak freely and give ideas
• Encourage wild ideas
• “Quality” rather than “Quality” ideas
• Suspend judgment on “Good” or “Bad”
• Ride on another’s ideas
• Never criticize other persons’ opinions
• Never prohibit a person from speaking
• See the problem from different angles/facets
• Write down all the viewpoints
• List the cause/ideas
• Think of the countermeasures to eliminate the causes
• Leader/facilitator need to guide the members in generating ideas
• Whenever necessary non – members can also be involved
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WHY BRAIN STORMING?
TO IDENTIFY THE PROBLEM
TO IDENTIFY CRITICAL CAUSES
TO FIND THE SOLUTION
TO PREVENT THE PROBLEM
•Round Robin •Card system•Free wheeling etc
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A diagram that shows the systematic relationship between a fixed result and the related causes
Managing the cause means,
Managing effect even before it happens
-A positive effect,if we manage well
-A negative effect,if we don’t
If we shift from being “managers of effects” to
“managers of causes”,our firefighting days are over
JIS DEFINITION:
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EFFECT IS “WHAT?” HAPPENS
CAUSE IS “WHY?” IT HAPPENS
EFFECT = RESULT OR OUTCOME
CAUSE = REASON(S) OR FACTOR(S)
CONTRIBUTING TO THE EFFECT
THE ANALYSIS OF “WHY?” FOR “WHAT?”
IS CAUSE AND EFFECT DIAGRAM
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ISHIKAWA DIAGRAM
OR
FISH BONE DIAGRAM
The cause and effect diagram was developed by Dr.K.ISHIKAWA to represent the relationship between EFFECT or PROBLEM and all the possible CAUSES influencing it.
For every EFFECT there are likely to be several CAUSES.
The major causes can be summarized generally under four categories known as 4M’s—MAN,MACHINE,MATERIAL & METHOD, OR 4P’s – PLANT,POLICY,PEOPLE,PROCEDURE
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CAUSE = Reason or Factor
Contributing to the EFFECT.
CAUSE is WHY it happens
EFFECT = A Result or an outcome.
EFFECT is What happens
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MATERIALMETHOD
MAN MACHINE
Sub cause
Sub-sub cause
CAUSES EFFECT
The EFFECT or PROBLEM is stated on the right side of the diagram and the major INFLUENCES or CAUSES are listed to the left.
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MATERIALMETHOD
MAN MACHINE
Sub cause
Sub-sub cause
CAUSES EFFECT
PROBLEM
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COPYING PAPERHANDLING
MACHINE PROCESS CONSUMABLES
Party removed
Dust accumulation
POOR QUALITY
OF XEROX COPIES
Paper shortage
Over consumption
Not using specified quality of paper
Non-standard
Non-standard
Supply from various sources
Wrong paper usage
Paper with stablesLack of knowledge
Not understanding the defect codes/ communicating defects
Nominated persons not handling
Paper jam not cleared properly
Untrained personnel handling the machine
Limited machines
Heavy usage
No cleaning
Location
Frequentchanges Serviced by
unauthorized personsImproper service
No periodical service
No stabilizer
Power
High fluctuation
No communication
Specified toner not filled
Lack of knowledge
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Missing
Inner cableInner cable
H.TImproper
Pinion Speedo cable
Not connected
Dust deal
Missing
Improper meshing
Worm & Pinion
Improper
Assembly
Not done
missing
Cable
CutBroken
Female socket
Defective
Needle
Broken
Defective
Worn outSQ. Drive
Fixture
Not provided
Bush pressing
Pinion Broken
Hammering
Speedometer Method
Material Man
Speedometer Not
Working
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Cause and Effect Diagram
Or
Ishikawa Diagram
Or
Fishbone Diagram
It gives the relationship between Effect or Problem and all the possible cause influencing it. For every effect, there are likely to be several causes.
Normally, causes are analyzed under 4 categories such
4 ‘m’s – Man, Machine, Material and Method
Or
4 ‘P’ – Plant, Process, Procedure and People
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Cause = Reason or Factor Contributing to the Effect.
Cause is why it happens
Effect = A Result or an outcome.
Effect is what happens
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• cost RS. 0.80
• can be serviced to old customers
• simple assembly
• cost RS. 2.80
• can be serviced at a high cost of Rs. 141/- to old customers
• high rate of handling damages
Stratification
Stratification
Beeorr Guard
Cost
Casting
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Effect
Causes
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Effect
Causes
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Stratification is nothing but the act of dividing data to the fine tune, in order to make sure of the significance of the assured factors, to the grass root level.
Definition
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Case study
Sales % - Area wise
- Dealer wise
- Sales Officer wise
Period / Time of sales
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116
20
4 1 1 1
208
54
21
2 1 1
0
25
50
75
100
125
150
< 5000 5000 -10000
100001 -15000
> 25000 200001 -25000
150001 -20000
Magazine Value
Nu
mb
ers
No. Of Magazines
No. Of Copies
143 Magazines / 287 Copies
Stratified - Data
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CASE STUDY
Problem : More No. of Accidents
Plant wise
Shop wise
Shift wise
Machine wise
Machine manufacture wise
Operator wise
Type of injury
Parts suffered injury
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CASE STUDYProblem : Increased Inventory
sheetsBarsTubesImportsProprietarypressingsFastenersRubberRubberBearings
Shop wiseAssy wiseSub-Assy wiseStage wiseMachine wiseRtRejections
Castings
Raw Material
Components
Work in process
Tubes
Proprietary
Fasteners
Bearings
Shop wise
Sub-Assy wise
TotalInventory
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CASE STUDY
Machine A
Machine B
Producing 2000 pieces
per day
Rejection rate gone up to 30% ……!
Let us stratify the possible causes
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Histogram is an important diagnostic tool which gives a ‘Birds –eye-view’ of the variation in a data set. It is nothing but a frequency distribution chart.
Histogram helps to actually judge the changes in quality characteristic of a group and the dispersion manner against the mid-value.
The Pareto Diagram deals only with characteristic of a procut or service such as type of defects, problem, etc.
However, a histogram takes measurement data and reveals the amount of variation.
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Source Of Variations
Material PeopleMachine methods Environment
Total Process Variation
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CONSTRUCTING A HISTOGRAM
STEPS
1. Collect data
2. Determine the largest value & smallest value
3. Obtain the range R (The range is the smallest value in the set of data subtracted from the largest value
4. Divide the range value in to certain number of classes referred to as K
5. Determine the class width, H = R / K
6. Divide the value of class boundary
7. Construct a frequency table, based on the values compiled
8. Construct Histogram based on the frequency table
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STEP 1 Collect data
CASE STUDYPeriod : wk no. 15 to 20Material : ms flange collarUOM : mmThickness : 9 mm + 1.5 mm
9.9 9.3 10.2 9.4 10.1 9.6 10.1 9.9 9.89.8 9.8 10.1 9.9 9.7 9.8 10.0 9.9 9.69.7 9.4 9.6 10.0 9.8 9.9 10.4 10.1 10.010.2 10.1 9.8 10.1 10.3 10.0 9.8 10.2 10.79.9 10.7 9.3 10.3 9.9 9.8 9.5 9.8 9.49.3 10.2 9.2 9.9 9.7 9.9 9.5 9.8 9.49.0 9.5 9.7 9.7 9.8 9.8 9.6 9.3 9.710.0 9.7 9.4 9.8 9.4 9.6 10.3 10.0 9.89.5 9.7 10.6 9.5 10.1 10.0 10.1 9.8 9.39.6 9.4 10.1 9.5 10.1 10.2 9.5 9.8 9.310.3 9.6 9.7 9.7 10.1 9.8 10.0 9.7 10.09.5 9.5 9.8 9.9 9.2 10.0 9.7 10.0 9.79.9 10.4 9.3 9.6 10.2 9.7 9.7 9.7 10.79.9 10.2 9.8 9.3 9.6 9.5 10.7 9.6
N = 125
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CASE STUDY
STEP 2 DETERMINE THRE LARGEST AND SMALLEST VALUE
Minimum Maximum9.9 9.3 10.2 9.4 10.1 9.6 10.1 9.9 9.8 9.3 10.29.8 9.8 10.1 9.9 9.7 9.8 10.0 9.9 9.6 9.6 10.19.7 9.4 9.6 10.0 9.8 9.9 10.4 10.1 10.0 9.4 10.410.2 10.1 9.8 10.1 10.3 10.0 9.8 10.2 10.7 9.8 10.79.9 10.7 9.3 10.3 9.9 9.8 9.5 9.8 9.4 9.3 10.79.3 10.2 9.2 9.9 9.7 9.9 9.5 9.8 9.4 9.2 10.29.0 9.5 9.7 9.7 9.8 9.8 9.6 9.3 9.7 9.0 9.810.0 9.7 9.4 9.8 9.4 9.6 10.3 10.0 9.8 9.4 10.39.5 9.7 10.6 9.5 10.1 10.0 10.1 9.8 9.3 9.5 10.69.6 9.4 10.1 9.5 10.1 10.2 9.5 9.8 9.3 9.3 10.210.3 9.6 9.7 9.7 10.1 9.8 10.0 9.7 10.0 9.6 10.39.5 9.5 9.8 9.9 9.2 10.0 9.7 10.0 9.7 9.2 10.09.9 10.4 9.3 9.6 10.2 9.7 9.7 9.7 10.7 9.3 10.79.9 10.2 9.8 9.3 9.6 9.5 10.7 9.6 9.3 10.7
9.0 10.7
N = 125
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CASE STUDY
STEP 4 Divide the ‘R’ in to No. Of Classes, referred to as ‘K’ 125 data points would be broken down in to 7 – 12 classes.
Method - 1
No.of data points No. of classes
Under 50 5 – 7
50 – 100 6 – 10
100 – 250 7 – 12
Over 250 10 - 20
Method – 2
No. Of Classes – K =√N where,
N = No. Of sample
For example, if N = 125
K =√125 = 11
Let us use K = 10 classes
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CASE STUDY
STEP 3 Obtain the range of R
Maximum value – Minimum Value = R
10.7 – 9.0 = 1.7
R = 1.7
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CASE STUDY
STEP 5 Determine the class width ‘H’
R (Range)
= H K (# of classes)
1.7= 0.17
10
Can be rounded off to 0.20
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CASE STUDY
STEP 6 Divide the value of class boundary
For simple determination of class boundaries, take the smallest individual measurement in the data set. Use this number or round to the next appropriate lowest number. This will be lower end point for our first class boundary.
In our example this would be 9.0. Now take this number and add the class width to it, 9.00 + 0.20.
But it is essential to fix class boundaries in such way that every observed reading will fit in to ‘one’ and ‘only’ class. Therefore, we may choose the class boundaries with one decimal place more than the observed readings.
For example, if the observations are in one decimal, the class boundaries will be in two decimals and so on.
For our case study, it will be 8.95 + 0.20 = 9.15
Finally, consecutively add the class width, to the lowest class boundary until the correct number of classes, approximately 10 and containing the range of all our numbers is obtained.
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CASE STUDY
STEP 7 Construct a frequency table
Class #
Class Boundaries
Midpoint Frequency Total
1 8.95-9.15 9.05 1
2 9.15-9.35 9.25 9
3 9.35-9.55 9.45 16
4 9.55-9.75 9.65 27
5 9.75-9.95 9.85 29
6 9.95-10.5 10.05 26
7 10.5-10.35 10.25 11
8 10.35-10.55 10.45 1
9 10.55-10.75 10.65 5
10 10.75-10.95 10.85 0
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CASE STUDY
STEP 8 Construct Histogram
0
5
10
15
20
25
30
35
8.95 9.15 9.35 9.55 9.75 9.95 10.15 10.35 10.55 10.75
Thickness in mm
Fre
qu
ency
USL
The specification for the thickness characteristic is 7.5 to 10.5, with a target of 9. The above Histogram indicates the process is targeted high and that 3% may be above the upper specification limit.
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HISTOGRAM
Having two peaks
Having a cut end
Having extraordinarily high value in
the end internal
Having an isolated peak
Right & Left symmetrical
Slopping the right
Slopping to the left
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Average response time to patient rings (1st shift)
0
50
100
150
200
1 2 3 4 5 6 7 8 9 10
Minutes
# O
f R
es
po
ns
es
Histogram - Daily Example Height of 100 men
0
10
20
30
40
63 64 65 66 67 68 69 70 71
Height (inches)
# O
f M
en
Histogram-ManufacturingPrint Density
0
24
6
8
0.6 0.7 0.8 0.9 0.1 1.1 1.2 1.3 1.4
Block density of print
Fre
qu
en
cy
Histogram – Administration/Service Example
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Interpretation Tips
No. Of classes (bars in the graph) determine how much of a pattern will be visible.
Some processes are naturally skewed; don’t expect every distribution to follow a bell shaped curve.
Get suspicious of the accuracy of the data if the classes suddenly stop at one point (such as a specification limit) without some previous decline in number.
Always look for twin peaks indicating that the data is coming from two or more different sources, e.g., shifts machines, etc.,
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Scatter diagramScatter diagram
Scatter diagram
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In actual practice, it is often essential to study the relation of two corresponding variables.
For example, to what extent will the dimension of a machined part be varied by the change in the speed of a lathe?
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To study the relation of two variables such as the speed of the lathe & the dimension of the part we can use what is called a Scatter diagram.
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The two variables we will deal with are:
a) A quality – characteristic & a factor affecting it,
b) Two related quality characteristics, or
c) Two factors relating to a single quality characteristic.
Let’s consider the steps in making a scatter diagram
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Step 1
Collect paired data (x,y) between which you want to study the relations & arrange the data in a table. It is desirable to have at least 30 pairs of data.
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Step 2
Find the maximum & minimum values for both x & y. Decide the scales of horizontal & vertical axes so that both the lengths become approximately equal, then the diagram will be easier to read. Keep the number of unit graduations between 3 to 10 for each axis & use round numbers to make it easier to read.
=
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Step 3
Plot the data on the section paper.
Step 4
Enter all necessary items. Make sure that the following items are included so that anyone besides the maker of the diagram can understand at a glance:
a) Title of the diagram
b) Time interval
c) Number of pairs of data
d) Title & units of each axis
e) Name (etc) of the person who made the diagram
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Example
A manufacturer of plastic tanks who made them using the blow moulding process encountered problems with defective tanks that had thin tank walls. It was suspected that the variation in air pressure, which varied from day to day, was the cause of the defective thin walls. The table shows data on blowing pressure & percent defective. Let us draw a scatter diagram using this data according to the steps given previously.
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Date Air pressure Percent(kgf/cm2) Defective
Oct-01 8.6 0.8892 8.9 0.8843 8.8 0.8744 8.8 0.8915 8.4 0.8746 8.7 0.8867 9.2 0.9118 8.6 0.9129 9.2 0.89510 8.7 0.89611 8.4 0.89412 8.2 0.86413 9.2 0.92214 8.7 0.90915 9.4 0.90516 8.7 0.89217 8.5 0.87718 9.2 0.88519 8.5 0.86620 8.3 0.89621 8.7 0.89622 9.3 0.92823 8.9 0.88624 8.9 0.90825 8.3 0.88126 8.7 0.88227 8.9 0.90428 8.7 0.91229 9.1 0.92530 8.7 0.872
Data of blowing air pressure & percent defective of plastic tank
7 QC TOOLSStep 1
As seen in the table, we have 30 pairs of data.
Step 2
In this example, let blowing air pressure be indicated by X (horizontal axis), & percent defective by Y (vertical axis).
Then,
The maximum value of X: Xmax = 9.4 (kgf/cm2)
The minimum value of X : Xmin = 8.2 (kgf/cm2)
The maximum value of Y: Ymax = 0.928 (%)
The minimum value of Y : Ymin = 0.864 (%)
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We mark off
the horizontal axis in 0.5(kgf/cm2) intervals, from 8.0 to 9.5 (kgf/cm2) and
the vertical axis in0.01(%) intervals, from 0.85 to 0.93(%)
Step 3
Plot the data.
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0.85
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.93
8 8.5 9 9.5
7 QC TOOLSStep 4
Enter the time interval of the sample obtained (oct.1 – oct 30) number of samples (n = 30), horizontal axis (blowing air pressure [kgf/cm2]), vertical axis (percent defective [%]), and title of diagram (scatter diagram of blowing air pressure & percent defective).
0.85
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.93
8 8.5 9 9.5
(Oct 1 – Oct 30)
n=30
Pe
rce
nta
ge
de
fect
ive
Blowing air pressure
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05
101520253035
0 5 10 15 20
Series1
050
100150200250300350
0 100 200 300 400
Series1
Positive correlation Negative correlation
How to read scatter diagrams
You can grasp the correlation between pairs of data just by looking at the shape of a scatter diagram. 5 examples are given below
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0
10
20
30
40
0 5 10 15 20
Series1
0
100
200
300
400
500
0 100 200 300 400
Series1
Positive correlation may be present Negative correlation may be present
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0100200300400500600700
0 100 200 300 400
Series1
No correlation
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Thank you