7qc ppt modified
TRANSCRIPT
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PRESENTED BY:Umesh KumarSymphony Limited,Ahmedabad
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Histogram/Frequency DiagramsCause and Effect (Ishikawa) Diagrams
-Brain StormingCheck SheetsPareto diagramsFlowcharts
Scatter DiagramsControl Charts
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FREQUENCY
DIAGRAMS(Histogram)
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Displays large amounts of data that aredifficult to interpret in tabular form
Shows centering, variation, and shape
Illustrates the underlying distribution ofthe data
Provides useful information forpredicting future performance
Helps to answer the question Is theprocess capable of meetingrequirements?
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Measurements of 50 items from process XYZ
147 179 185 125 210
131 137 141 142 166
198 142 205 150 141
190 161 157 165 155
165 155 169 158 150
170 125 177 108 193
178 181 155 186 145
157 135 148 171 124
168 141 151 162 150
145 177 154 137 160
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TALLY CHARTRANGE TALLY NUMBER
100-109 | 1
110-119 0
120-129 | | | 3
130-139 | | | | 4
140-149 | | | | | | | | | 9
150-159 | | | | | | | | | | | 11
160-169 | | | | | | | | 8
170-179 | | | | | | 6
180-189 | | | 3
190-199 | | | 3
200-209 | 1
210-219 | 1
TOTAL 50
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LowerTolerance(125
)
UpperTole
rance(185
)Spec. (155)Spec. (155)
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Normal Distribution
Bi-Modal Distribution Multi-Modal Distribution
Positively Skewed Negatively Skewed
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CAUSE & EFEECT
DIAGRAM
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Enables a team to focus on the content ofa problem, not on the history of theproblem or differing personal interests ofteam members
Creates a snapshot of collectiveknowledge and consensus of a team;builds support for solutions
Focuses the team on causes, notsymptoms
Effect
Cause
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Cause and Effect Analysis Ishikawa fish bone diagramming
Easy to draw like on paper
Identification of Root causes
Mark a cause as a root cause Assign a priority number
Cause and Effect DiagramsCause and Effect Diagrams
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Any ofthe speed
off in
motor
MaterialsMethods
Main Connection loose
Power Source
unskilled
Untrained
Terminals
Stripping
Technique
Manual
WireGauge
Cause and Effect DiagramsCause and Effect Diagrams
Fatigue
Machines Man
Winding not as
per standard
Soldering
Flux
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MaterialMaterialManMan
MethodMethodMachineMachine
11
22
33 66
55
4411
22
3366
55
44
11
22
3366
55
44
11
22
3366
5544
Cause and Effect DiagramsCause and Effect Diagrams
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Easy-to-understand data.Builds, with each observation, a
clearer picture of the facts.
Forces agreement on the definitionof each condition or event ofinterest.
Makes patterns in the data becomeobvious quickly.
xxxxxxxx
x
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Products Faults
ColourMismatch
SpeedOFF
OtherVisual
Defects
Total
Sumo Go | | | | | | 6
Winter | | 2
Jumbo | | | 3
Jumbo Jr | | | | | 5
Total 8 3 5 16
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Process Name: Final InspectionProduct Name: Sumo Slim
TOTAL3/82/81/831/730/7Defective part
61811151413TOTAL
7| | || | ||Other
2||Connection Loose
9|| || | | ||Vibrations
14| | | || | || || | | |Dent, Scratch
8|| | | || |Low RPM
21| | | || | || | | | || | || | | |
Warpage
OEM Name: RavikiranProduct Code: XYZ
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PARETO DIAGRAM
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Rejection Categories Rejection Qty
Joint Short 50
Medium & Low speed open 41
Aux Winding short 170
Jam 14
Burnt 44
Loop Burnt 9
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RejectionCategories
Rejection Qty Rejection %(rej
qty/total rejection)*100
Cumulative %
Aux Winding
short
170 51.83 51.83
Joint Short 50 15.24 67.07
Burnt 44 13.41 80.49
Medium & Low
speed open
41 12.50 92.99
Jam 14 4.27 97.26
Loop Burnt 9 2.74 100.00
Step 1 : Calculate rejection % & Cumulative % ofeach factor
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FLOW CHARTS
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Shows unexpected complexity, problemareas, redundancy, unnecessary loops,and where simplification may bepossible
Compares and contrasts actual versusideal flow of a process
Allows a team to reach agreement on
process steps and identify activities thatmay impact performance
Serves as a training tool
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TerminatorUsed to Start/Stop
flowchart
DataUsed to indicate datain or out of the process
ActivityUsed to show a task oractivity performed in the
process.
DecisionShows the points in the
process where yes/no
questions are asked
DelayUsed to indicate delays
or stock points in the
process.
Connector
Used to link differentPoints in the flowchart.A
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StartStartDoes
patient have
an appt
?
Patient
arrives
Y
N
Waiting
roomConsultation
A AMake an
Appointmt
Y
N
Wait
to see
doctor
?
Need
a follow-up
appt
?
Y
N
Need
to pick updrugs
?
Fill prescripn
at pharmacyPatient
leavesStop
Y
N
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SCATTER DIAGRAM
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Supplies the data to confirm ahypothesis that two variables arerelated
Provides both a visual and statisticalmeans to test the strength of arelationship
Provides a good follow-up to cause
and effect diagrams
*
* ** *
*
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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 demonstratedconnection between x and y.
An decrease in y may depend
upon an increase in x.
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CONTROL CHARTS
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Focuses attention on detecting andmonitoring process variation overtime
Distinguishes special fromcommon causes of variation
Serves as a tool for on-going
controlProvides a common language for
discussion process performance* *
*
* **
*
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Lower ControlLimit (LCL)
Upper Control
Limit (UCL)
Average
(Xbar)
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Variables data TemperatureLength
CostAttributes dataNumber of porous castings in a sample
(defective parts)Number of cavities in a porous casting
(defects)Shipping errors
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Choose appropriate
control chart
Attribute data:
Counted and
plotted as
discrete events
Variable data:
Measured and
plotted on a
continuous scale
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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
c
c Chart
Variable
sample size
u Chart
Constant
sample size
Variable
sample size
np Chart p Chart
Fraction defectiveNumber defectiveNumber of defects Number of defects
per unit
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Symbols Terminologyn Sample Size
d No. of defectives in asample
p d/n
p Proportion of defectivesproduced by entire
process
UCL Upper Control Limit
LCL Lower Control Limit
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Date Number of coolersinspected
No. of defectivecoolers
FractionDefectives (p)
3 = 3 X Sqroot[p(1-p)/n]
P + 3 P - 3
1 600 77
2 500 78
3 540 64
4 610 90
5 670 96
6 660 110
7 650 78
8 730 88
9 750 80
10 720 90
11 670 71
12 660 75
13 650 85
14 510 70
15 550 58
16 590 61
17 630 65
18 650 115
19 700 82
20 740 55
Tot
al
12780 1588
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Date Number of coolersinspected
No. of defectivecoolers
FractionDefectives (pi)
3 = 3 X Sqroot(p(1-p)/n)
P + 3 P - 3
1 600 77 0.128 0.040 0.164 0.084
2 500 78 0.156 0.044 0.168 0.08
3 540 64 0.119 0.042 0.166 0.082
4 610 90 0.147 0.040 0.164 0.084
5 670 96 0.143 0.038 0.162 0.086
6 660 110 0.167 0.038 0.162 0.086
7 650 78 0.120 0.039 0.163 0.085
8 730 88 0.121 0.037 0.161 0.087
9 750 80 0.107 0.036 0.160 0.088
10 720 90 0.125 0.037 0.161 0.087
11 670 71 0.106 0.038 0.162 0.086
12 660 75 0.114 0.038 0.162 0.086
13 650 85 0.131 0.039 0.163 0.085
14 510 70 0.137 0.044 0.168 0.080
15 550 58 0.106 0.042 0.166 0.082
16 590 61 0.103 0.041 0.165 0.083
17 630 65 0.103 0.039 0.163 0.085
18 650 115 0.177 0.039 0.163 0.085
19 700 82 0.117 0.037 0.161 0.087
20 740 55 0.074 0.036 0.160 0.088
Tot
al
12780 1588 p = 1588/12780 = 0.124
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0.06
0.08
0.1
0.12
0.14
0.16
0.18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Mean Upper Control Limit Lower Control Limit P bar
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Symbols Terminologyn Sample Size
d No. of defectives in asample
p Fraction defective in asample
np Total number of defectives produced by
entire samples inspectedUCL Upper Control Limit
LCL Lower Control Limit
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Factors Formulaep Defectives/number of pieces inspected
np Total defectives/no.
Of samples inspected
n*Sq root[p(1-p)/n]
Upper Control Limit(UCL) np + 3
Lower Control Limit(LCL)
np - 3
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Control chart for samplemean (x-Chart)
Control chart for samplerange (R-Chart)
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Symbols Terminology Formulae
R Individual Range Max(x)-min(x)
R Average Range Average(individual
Ranges)
UCL Upper Control Limit D4R
LCL Lower Control Limit D3R
Where D3 & D4 are constants which depends on sample size.
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n A2 D3 D4
2 1.88 0 3.27
3 1.02 0 2.57
4 0.73 0 2.28
5 0.58 0 2.11
6 0.48 0 2.00
7 0.42 0.08 1.92
8 0.37 0.14 1.86
9 0.34 0.18 1.82
10 0.31 0.22 1.78
11 0.29 0.26 1.74
12 0.27 0.28 1.72
13 0.25 0.31 1.69
14 0.24 0.33 1.67
15 0.22 0.35 1.65
16 0.21 0.36 1.64
17 0.20 0.38 1.62
18 0.19 0.39 1.61
19 0.19 0.40 1.61
20 0.18 0.41 1.59
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We are doing final audit of 100%coolers in all OEM.
Let us take an example of RPM
checking.Suppose hourly we are considering 4
coolers & take observation for RPM at
any of the speed.Data as follows on next slide...
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S No. Cooler 1 Cooler 2 Cooler 3 Cooler 4
1 1201 1215 1190 12152 1198 1218 1197 1222
3 1220 1190 1210 1209
4 1200 1191 1205 1240
5 1203 1205 1205 1194
6 1205 1210 1210 1190
7 1180 1235 1160 1189
8 1196 1219 1187 1195
9 1199 1179 1185 1200
10 1201 1197 1190 1208
11 1210 1196 1193 1210
12 1215 1199 1250 1220
13 1190 1203 1208 1250
14 1185 1203 1215 1198
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S No. Cooler 1 Cooler 2 Cooler 3 Cooler 4 IndividualMean
(Avg)
Range(max-
min)1 1201 1215 1190 1215
2 1198 1218 1197 1222
3 1220 1190 1210 1209
4 1200 1191 1205 1240
5 1203 1205 1205 1194
6 1205 1210 1210 1190
7 1180 1235 1160 1189
8 1196 1219 1187 1195
9 1199 1179 1185 1200
10 1201 1197 1190 1208
11 1210 1196 1193 1210
12 1215 1199 1250 1220
13 1190 1203 1208 1250
14 1185 1203 1215 1198
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S No. Cooler 1 Cooler 2 Cooler 3 Cooler 4 IndividualMean
(Avg)
Range(max-
min)1 1201 1215 1190 1215 1205.25 25
2 1198 1218 1197 1222 1208.75 25
3 1220 1190 1210 1209 1207.25 30
4 1200 1191 1205 1240 1209 49
5 1203 1205 1205 1194 1201.75 11
6 1205 1210 1210 1190 1203.75 20
7 1180 1235 1160 1189 1191 75
8 1196 1219 1187 1195 1199.25 32
9 1199 1179 1185 1200 1190.75 21
10 1201 1197 1190 1208 1199 18
11 1210 1196 1193 1210 1202.25 17
12 1215 1199 1250 1220 1221 51
13 1190 1203 1208 1250 1212.75 60
14 1185 1203 1215 1198 1200.25 30
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Average of Individual mean (x) = 1203.71Average of Range (R) = 33.14
A2 = 0.73, D3 = 0, D4 = 2.28
UCL = x + A2*R = 1203.71 + 0.73*2.28
UCL = 1227.902LCL = x - A2*R = = 1203.71 - 0.73*2.28
LCL = 1179.518
Let us plot line diagrams between individualmean, UCL, LCL & Average of individual mean
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S No. UCL LCL Average of individual
mean
IndividualMean (Avg)
1 1227.902 1179.518 1203.71 1205.25
2 1227.902 1179.518 1203.71 1208.75
3 1227.902 1179.518 1203.71 1207.25
4 1227.902 1179.518 1203.71 1209
5 1227.902 1179.518 1203.71 1201.75
6 1227.902 1179.518 1203.71 1203.75
7 1227.902 1179.518 1203.71 1191
8 1227.902 1179.518 1203.71 1199.25
9 1227.902 1179.518 1203.71 1190.75
10 1227.902 1179.518 1203.71 1199
11 1227.902 1179.518 1203.71 1202.25
12 1227.902 1179.518 1203.71 1221
13 1227.902 1179.518 1203.71 1212.75
14 1227.902 1179.518 1203.71 1200.25
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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 distn,
new method, etc.
Erratic readings
Causes: over adjustment,
measuring equipment not
capable, etc
Shift in readings
Causes: change in matl,change in operator, etc
Cyclic readings
Causes: work pattern,
Environment, etc.
GroupingOutlier Shift
Trend Erratic Cycle
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As all the observations lies withincontrol limit, therefore process is understatistical control
But according to mean chart there are7 readings lie below the central line, sothere is something change which
requires to be improved or modified
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Application of 7 QC Tools in Our
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Application of 7 QC Tools in OurOrganization
Flowchart1. All vendors & all OEMs process charts to
be submitted here in our organization.
Control Charts
1. OEM line processes, critical
parameters to be identified by lineQA.
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