Training module on Basics of 7QC Tools .

This Training module makes you to,
Understand QC Tools & its types
Use relevant QC tool for the analysis
Go with the in-detailed analysis of right root cause for the problem identified.
Avoid problem repetition
Quick Response Quality Control (QRQC)

1. 7 QC
(Quality Control)
Tools
BASICS TRAINING
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2. Objective of the Training
•
To understand QC Tools & its types
•
Touse relevant QC tool for the analysis
•
To go with the in-detailed analysis of right root cause for the problem identified.
•
To avoid problem repetition
•
Quick Response Quality Control (QRQC)
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3. The Seven tools of Quality control
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0
10
20
30
40
50
60
70
80
90
100
EDBCAFGH
2
MAJOR CAUSE 1MAJOR CAUSE 2
EFFECT
MAJOR CAUSE 3MAJOR CAUSE 4
3
5
7
6
*In some cases, graphs(line, bar, Pie etc) and flow charts are used in place of stratification diagram.
1.Check Sheet
2.Pareto diagram
3.Cause & Effect diagram
4.Control Chart
5.Histogram
6.Scatter diagram &
7.Stratification
1
4

4. How not to collect a data !!
Consider a dimension of 1.000mm 0/+5m.
Start measuring and recording the details.
1.003,1.002,1.002,1.003,1.001,1.000,1.003,1.001,1.003,
1.003,1.002,1.003,1.003,1.002,1.003,1.002,1.003,1.006,
1.003,1.005,1.003,1.002,1.004,1.005,1.003,1.003,1.004
1.002,1.003,1.004,1.006,1.004.
This type of recording data is wrong way to collect data . The correct way is
shown in next slide.
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5. 1.Check Sheet
A check sheet is a systematic way of collecting data. Idea
of check sheet is to facilitate easy processing of information.
6
II
5
II
4
IIII
3
IIIIIIIIIIII
2
IIIIII
1
II
0
I
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6. Checksheets
Purpose:
•
Tool for collecting and organizing measured or
counted data
•
Data collected can be used as input data for
other quality tools
Benefits:
•
Collect data in a systematic and organized
manner
•
To determine source of problem
•
To facilitate classification of data
(stratification)
1.Check Sheet: Purpose and benefits
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7. Check Sheet: Sample sheet
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8. Vilfred Pareto -( 1846 -1923 ) An italianpolitical Economist, in his Studies
of old and new societies, observed a common tendency of wealth and
power to be unequally distributed.
This function is called Pareto’s law of maldistribution, 80/20
relationship.
Pareto’s Law
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9. 2.Pareto diagram
A Pareto diagram is one which Separates the Vital few problems
from the Trivialmany.
Vital -Means very important.
Trivial -Means not so important or less important.
Priority need not be given now.
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10. Pareto diagram
0
10
20
30
40
50
60
70
80
90
100
EDBCAFGH
Vital few
Trivial many
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11. PersonRuns
Scored
PersonDecendin
g order
Percent-
age
Row chart
A25C7023.3%23.3%
B30G6521.7%45.0%
C70I5518.3%63.3%
D15B3010.0%73.3%
E20A258.3%81.7%
F4E206.7%88.3%
G65D155.0%93.3%
H6J82.7%96.0%
I55H62.0%98.0%
J8F41.3%99.3%
K2K20.7%100.0%
11300 100.0%
Pareto chart on No of runs scored
PARETO CHART ON THE RUNS SCORED
23.3%
45.0%
63.3%
73.3%
81.7%
88.3%
93.3%
96.0%
98.0%99.3%100.0%
0
10
20
30
40
50
60
70
80
CGIBAEDJHFK
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
Pareto diagram-Example.
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12. AADYA L&D SOLUTIONS URL
0
20
40
60
80
100
120
Quantity
Defects 10442201410 6 4
DentScratchHoleOthersCrackStainGap
Pareto diagram-Example using check sheet for data
collection

13. Pareto diagram-Sample used Check sheet and Pareto

14. Defect in %
Percent
Defect
Count
4.5 2.2 1.8 1.8 3.9
Cum % 85.8 90.3 92.5 94.3
12.40
96.1 100.0
0.65 0.32 0.26 0.26 0.56
Percent 85.8
Other
peel off
Paste hole\Paste bubble
Edge chip/Face chip on blanks
Mask shift
Paste crack(Sepration)
16
14
12
10
8
6
4
2
0
100
80
60
40
20
0
Defectwise pareto chart for patrol inspetions May-2015
SlNo
Defect
Code
Defect Rejections
Rejections in
%
1a Dust particles 00.00
2b gold particle 00.00
3c Gold bubble 10.06
4d peel off 40.26
5e Discoloration 00.00
6f Stains 10.06
7gPaste hole\Paste bubble40.26
8h Scratches 10.06
9i
Edge chip/Face chip on
blanks
50.32
10j white patches 40.26
11k Etch mark 00.00
12l PR\PNE\PS 10.06
13m Paste spread 10.06
14n Mask shift 100.65
15oPaste crack(Sepration)19212.40
16p Inclined 00.00
Total 22414.47

15. 43
31
16
7
3
2
0 0
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
0
5
10
15
20
25
30
35
40
45
50
No
oscillation
PIVIElectrical
test
Final test
and Tap
test
Prebase
test
Crystal
assembly
Base
assembly
Before
SLT
Percentage
Qty
Defect Code
Pareto Chart-Wk-20
Qty
Cum sum
SI No
Defect
descriptionQty
%
Problems
in the
rejects
Cum
sum
1
No
oscillation43
42.1642.16
5 PI3130.3972.55
3 VI 1615.6988.24
2
Electrical
test
76.8695.10
4
Final test
and Tap test
32.9498.04
6Prebase test21.96100.00
7
Crystal
assembly
00.00100.00
8
Base
assembly
00.00100.00
9Before SLT 0.00100.00
102100.00
Pareto diagram-sample data using pareto

16. 3.Cause and effect diagram
There are many causes for an effect or a problem to occur!!
The Cause and effect diagram actually lists out all the possible
causes through a process called brain storming.
Finally action is taken on all relevant causesto remove the effect.
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17. Brain storming
Brain storming is an activity performed by a group of
persons who are relevant to the nature of the
problem.
The idea of brain storming is to collect maximum
ideason the causes for the problems.
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18. Cause and Effect diagram
MAJOR CAUSE 1MAJOR CAUSE 2
EFFECT
MAJOR CAUSE 3MAJOR CAUSE 4
Related causes
Related Causes
Related Causes
Related causes
Related causes
Related causes
Related causes
Related causes
Ishikawa diagram or Fish bone diagram.
MAN MACHINE
METHOD MATERIAL
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19. Cause & Effect Diagrams-Sample example
Incorrect shipping
documents
Manpower Materials
Methods Machine
Environment
Keyboard sticks
Wrong source info
Wrong purchase order
Typos
Source info incorrect
Dyslexic Transposition
Didn’t follow proc.
Glare on
displayTemp.
No procedure
No communications
No training
Software problem
Corrupt data
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20. Cause & Effect Diagrams-Sample example
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21. SlNOStage Process step Comments Investigation results
Strain
gage
testing
Impedance
testing
1
SMT
1st side paste printing
EMS and CRI are different lines using
different tools and settings.
Supporting PINs on EMS line and initially no
PINs on CRI line, enabling flexing.
Placement parameters with a higher stroke
on CRI line
C19 placed on 1st side
C32 placed on 2nd side
Can be a valid cause for C19, though not the sole cause of flex
cracking capacitors.
The mix of CRI and EMS parts is showing higher RMA # for CRI line.
However this needs a deeper analysis of the mix at P&P at the time of
mnf'gparts withshipDC1440 and 1441 to assess the validity.
2 1st side Pick & Place
3 1st side Reflow soldering
4 2nd side paste printing
X
5 2nd side Pick & Place
X
6 2nd side Reflow soldering
X
7
Cleaning
Vertical slots L trays.
Air gun drying after cleaning. Pressure
reduced to avoid excessive force applied to
panels
Can be a valid cause or a contributor to flex cracking X
8 Visual inspection X
9 Pack to stores Packing horizontal
10
Assembly
Preparation of kit & Visual Inspection
11
Paste radiators & Place crystals on
panels
Manual operation.
Initially performed with no pallet to support
the PCB. Pallet introduced 22/11/14
Can be a valid cause to flexing the boards when the technician is
placing the crystal. Initially the panel was sitting on a faom not able to
prevent a warpage of the panel
X
12
Crystal attach by reflow soldering and
soldering of the leads
Shim supporting the crystal in position are
removed following reflow. Some shim may be
difficult to slide out
Not considered as a valid cause at present time
X
Before & after
shim removal
13
Functional test of the circuits on the
panel
Manual operation.
Initially performed with no pallet to support
the PCB. Pallet introduced 22/11/14
Can be a valid cause to flexing the boards when probing before the
use of a pallet and a low force contact spring loaded probe
X X
14
US cleaning and Visual Inspection
Air gun drying after US cleaning
Same as point 7 above X
15
Depanelisation
Done with manual cutter initially.
Router implemented in W39
Can be a valid cause though not the sole. Capacitors have been found
cracked on routed PCBs. Also one instance recorded on a V scored
PCBs for a similar product
X X
Flow chart analysis : To eloborate on causes listed in
Fishbone diagram
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22. 16
Prepare base PCB
17
Attaching Oscillator circuit on base PCB
18
Ultrasonic cleaning and Visual
Inspection
19
Functional testing
a.Cutting the bottom PCBA
b.Functional test of the OSC circuit
c.Cover insertion & Initial Labeling
Case height:10.4±0.15mm
Loading / Unloading of the device in a test jig
X X
20
Final Visual Inspection
21
Sample Inspection by (QA)
22
Solder one point to secure the case
23
Scanning & addition to aging
Loading into aging jigs X
24 Remove device of aging racksUnloading jigs X
25
Final Adjustment as per FTO
Loading / Unloading of the device in a test jig
26
Sty cast application(Crystal to PCBA &
4 corners of the bottom side top PCBA)
and curing
27
C19 change as a containment action
6 # C19 found broken out of 7593 units at 09/12.
Equivalent FR of 790 ppm
X
28
Cleaning and visual Inspection by
production
29
Closing the cover- cover height -12mm
Max with final label (FIF -058)
30 Frequency in Temperature testing
31 Final Inspection
32 Final Labeling (FIF 119)
33
Sampling FQA Inspection (FIF 102)(QA)
Testing Assembly
Flow chart analysis : To eloborate on causes listed in Fishbone
diagram
SlNOStage Processstep Comments Investigation results
Strain
gage
testing
Impedance
testing
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23. Stratificationmeansto“dividethewholeintosmallerportionsaccordingtocertaincriteria.”
Incaseofqualitycontrol,stratificationgenerallymeanstodividedataintoseveralgroupsaccordingtocommon
factorsortendencies(e.g.,typeofdefectandcauseofdefect).
Dividingintogroups“fostersunderstandingofasituation.”Thisrepresentsthebasicprincipleofqualitycontrol.
The“commonandbasicprinciple”ofqualitycontrolisstratification,i.e.,tothinkamatteroutbybreakingitinto
smallerportions.Stratificationhasanumberofusefulpurposes.Thetablebelowshowsonlyafewexamplesof
thesepurposes.
Item Method of Stratification
Elapse of time
Hour, a.m., p.m., immediately after start of work,
shift, daytime, nighttime, day, week, month
Variations among workers
Worker, age, male, female, years of experience,
shift, team, newly employed, experienced worker
Variations among work
methods
Processing method, work method, working
conditions (temperature, pressure, and speed),
temperature
Variations among
measurement/inspection
methods
Measurement tool, person performing
measurement, method of measurement, inspector,
sampling, place of inspection

4. Stratification diagram.
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24. 4. Graphs.
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25. 5. Control chart.
Control charts are dynamic tools which gives us instant
information on a process.
They are,
1) Attribute control chart,
2) Variable control chart and
3) Pre-control chart.
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26. Attribute Control Chart
•Attribute control charts are those constructed from data which are
obtained by counting characteristics rather than measuring them.
•Control charts derived from such data are called attribute control chart.
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27. An attribute charting are those where decisions are made in the lines
of,
•PASS / FAIL
•YES / NO
•ACCEPT / REJECT
•GO / NO GO
Attribute data can be converted to variable data with little thought and
effort.
Attribute Control Chart
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28. Defect and defective
•A Defect is the flaw or non conformity which caused the
component or item to be rejected.
•A defective is a component or item which is unacceptable
because it does not match up to requirements.
•A defective can have many defects present in it. But to be adjudged
defective, a component or item must obviously have at least one defect.
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29. The types of Attribute control chart
•The Number of Defectives in a sample :( npchart )
•The proportion of defectives in a sample : ( P chart )
•The number of defects in a sample : ( c chart )
•The proportion of defects in a sample ( u chart )
The following plotting gives the respective charts.
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30. Start
X bar , R
X bar, S
IX, Moving Range
p (fraction defective) or
np (number def. Per sample
p
c (defects per sample or
u defects per unit
u
Control Chart Decision Tree
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31. Total
WATER
RESISTANCE
FAILURE

 10
DUST/ LINT ON
DIAL



12
BACK COVER GAP

 8
HANDS CLEARANCE

2
np Chart - RUNNING TEST - MODEL - X np Chart - Model X - Defectives
0
5
10
15
20
25
30
35
Defectives in Nos32151814192225262226
Week 1Week 2Week 3Week 4Week 5Week 6Week 7Week 8Week 9Week 10
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32. Conventional X-bar / Range Control chart
RD
RD
RAX
RAX
3
4
2
2


Use this formula for finding Upper Ctrl Limit
Use this formula for finding Lower Ctrl Limit
Use this formula for finding Upper range Limit
Use this formula for finding Lower range Limit
A Series of small sub groups are measured and the average
x double bar and range are calculated. The sub group size
is constant. 25 Such sub groups are needed.
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33. n A
1
A
2
D
3
D
4
d
2
23.7591.8800.0003.2681.128
32.3941.0230.0002.5741.693
41.8800.7290.0002.2822.059
51.5960.5770.0002.1142.326
61.4100.4830.0002.0042.534
71.2770.4190.0761.9242.704
81.1750.3730.1361.8642.847
91.0940.3370.1841.8162.970
101.0280.3080.2231.7773.078
Table of constants for calculating control limits
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34. DIMENSION 1.000 +/-15
12345
6
7891011121314151617
18
1920
15-5-106
7
0-14540126-10
9
01
2-4-80-25
7
-20-1303254-1-4
4
-22
3-3-3-3-33
6
-1-2-3-23553223
3
3-3
44-2-43-2
7
-4-3-502765032
-10
45
532351
8
-5-421-1-1-16-101
0
-1-1
x-bar
1.0-3.2-1.00.62.6
7.0
-2.4-2.0-0.61.41.62.82.64.22.20.60.4
1.2
0.80.8
range
910788
2
54975874747
19
68
5.15.15.15.15.15.15.15.15.15.15.15.15.15.15.15.15.1
5.1
5.15.1
-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1-5.1
1.00-3.20-1.000.602.607.00-2.40-2.00-0.601.401.602.802.604.202.200.600.401.200.800.80
18.318.318.318.318.318.318.318.318.318.318.318.318.318.318.318.318.318.318.318.3
9107882549758747471968
X-BAR , RANGE CONTROL CHART
-6
-4
-2
0
2
4
6
8
1234567891011121314151617181920
0
10
20
1234567891011121314151617181920
X chart->Averages of each
subgroups plotted, One of
the readings(subgroup 6)
found out of UCL).
R chart->Ranges of each
subgroups plotted, one of
the readings(subgroup 18)
is found out of spec.
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35. Control Charts: Steps
How is it done?
•
The data must have a normal distribution (bell curve).
•
Have 20 or more data points. Fifteen is the absolute minimum.
•
List the data points in time order.Determine the range between each of the consecutive data points.
•
Find the mean or average of the data point values.
•
Calculate the control limits (three standard deviations)
•
Set up the scales for your control chart.
•
Draw a solid line representing the data mean.
•
Draw the upper and lower control limits.
•
Plot the data points in time sequence.
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36. Control Charts
•
Next, look at the upper and lower control limits. If
your process is in control, 99.73% of all the data points
will be inside those lines.
•
The upper and lower control limits represent three
standard deviations on either side of the mean.
•
The distance between the centerline and the upper
control limit can be divided into three equal zones
representing three standard deviations.
Control Charts: Summary
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37. Control Charts: Interpretation rules
•
Specialsare any points above the UCL or
below the LCL
•
A Runviolation is seven or more
consecutive points above or below the
center (20-25 plot points)
•
A trendviolation is any upward or
downward movement of five or more
consecutive points or drifts of seven or
more points (10-20 plot points)
•
A 1-in-20violation is more than one point
in twenty consecutive points close to the
center line
UCL
LCL
UCL
LCL
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38. FOR INITIAL QUALIFICATION FIRST FIVE PARTS SHOULD FALL IN GREEN ZONE.
FOR SUBSEQUENT RUNNING TWO PARTS TO BE TAKEN UP FOR DECISION MAKING.
1234567891011121314151617181920
1
25 % OF
111
TOL
11 1 1
ZONE
1 1 1 1
111 11
50%
111 1 1
OF
11111 1 1
TOL
1 1
ZONE
1 1
1
25 % OF
1 1
TOL
11 1
ZONE
111
1
SET
UPRUNRUNRUNSTOPSTOPSTOPSTOPRUNRUNRUNRUNSTOPSTOPSTOPSTOPSTOPRUNRUNSTOP
OK
PRE-CONTROL CHART
Invented by Mr FrankSatterthwaite and developed by the consulting company of Rath
& Strondfor a Major fortune 500 company who had become disenchanted with Ctrl
charts.
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39. Sigma or Standard deviation.(s)
Sigma is a measure of variation in a
process. Sigma is expressed by a symbol
known as -s.
This can be measured from time to time,
Piece to piece, within a piece etc.
Sigma =
1
)(
2


=

n
xx
i
s
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40. Balls Distance Height
Bowled from middle from
( n ) of stump in cms.ground in cms
First ball 5 10
2 8 12
3 9 15
4 15 14
5 10 18
6 20 19
_
X 11.17 14.67
n = No of samples =6 6
Sum of (Xi -X)^2 =146.83 59.33
n -1 =5 5
Sigma = 5.423.44
Computer calculated =
5.419103.44480
Calculating standard deviation -Sigma-s
THE FORMULA
1
)(
2


=

n
xx
i
s
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41. 6. Histogram
Histogram is a collection of data represented in bar graph form.
It is looking at the data from birds eye view angle.
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42. Histograms
Purpose:
To determine the spread or variation of a set of data points in a graphical
form
How is it done?:
•
Collect data, 50-100 data point
•
Determine the range of the data
•
Calculate the size of the class interval
•
Divide data points into classes Determine the class boundary
•
Count # of data points in each class
•
Draw the histogram
Stable process, exhibiting bell shape
6. Histogram
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43. Benefits:
•Allows you to understand at a glance the variation that exists in a process
•The shape of the histogram will show process behavior
•Often, it will tell you to dig deeper for otherwise unseen causes of variation.
•The shape and size of the dispersion will help identify otherwise hidden sources of variation
•Used to determine the capability of a process
•Starting point for the improvement process
6. Histogram
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44. 152165165154154159159152
158 149 151156156161161158
161151151155155162162161
165153153158158163163165
166154154159159157157166
167155155169168158158167
168162162165165154154168
172 166166166166155155170
165150150165165156156165
164154154158158154154164
DATA ON HEIGHTS OF ALL THE STUDENTS IN A CLASS IN cms.
6. Histogram
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45. Max height : 172 cm.
Min height : 149 cm.
Range : 23 cm.
Cell interval : 23/6 = 3.83 = round off to 4
Multiply this by 0.75 = 3
Min ht-3 = 146 is the starting point.
> 146 to 150 = 3 > 158 to 162 = 12
> 150 to 154 = 17 > 162 to 166 = 20
> 154 to 158 = 20 > 166 to 170 = 7
> 170 to 174 = 1
6. Histogram
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46. >146 ~ 150
cms
>150 ~ 154
cms
>154 ~ 158
cms
>158 ~ 162
cms
>162 ~ 166
cms
>166 ~ 170
cms
>170 ~ 174
cms
Students 3 17 20 12 20 7 1
0
5
10
15
20
25
Histogram of Heights of 80 Students in a class.
6. Histogram
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47. 7. Scatter diagram
Scatter diagram is a tool to identify any relationship between two variables. This
tool will identify whether any relationship between the variables exists or not.
Consider x & y as two variables.
When “Y” increases with a corresponding increase in “X” then it is said to be “Positive Correlation”.
Ex. Sales to Turnover, Age VsHt( Under 21) , Interference to torque etc.,
When “Y” decreases with increase in “X” then is called “Negative correlation”. Ex. Height from Sea level to
atmospheric pressure.
When there is no increase or decrease in “Y” for a
change in “X” then it has no correlation and they are independent.
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48. Positive correlation
Humidity Vs Rust & Stickiness.
Age Vs height ( Under 22).
Rains to water availability.
Negative correlation
Atmospheric Pressure Vs Altitude.
Quality improvement Vs Market returns.
Mass manufacturing Vs Market price.
Scatter diagram -Contd….
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49. •Attendance to machine break downs.
•Speed Vsaccidents.
Scatter diagram -Contd….
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50. AADYA L&D SOLUTIONS URL
Correlation graphs-Examples for different trends

51. AADYA L&D SOLUTIONS URL
Correlation graphs-Examples for different trends

52. AADYA L&D SOLUTIONS URL
Application of 7QC tools in Problem solving

53. Thank you