7qc ppt modified

8/6/2019 7QC PPT Modified

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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


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



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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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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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7qc ppt modified

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