driven by excellence

P DA C

Driven by Excellence

‘Increase the Ppk of window runout from0.3 to 1.33 by Mar‘16”

Project Champion : Mr. Pawan KhuranaProject Leader : Mr. Atul Aggarwal

P DA C

Driven by ExcellenceDriven by Excellence

Amtek PowertrainLtd. Dharuhera

Welcome to “DL Shah QualityAward”

P DA C

Drive PlateRing Gear

This part is used to Start the Car Engine in automatic transmission VehicleThis part is used to Start the Car Engine in automatic transmission Vehicle

Our Product

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4

Nissan

General Motors

PSA

Ford

Mazda

FIAT

Our Customer

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Methodology

1. Problem understanding

2. Cross functional team working

3. Data Source and collection

4. Technical approach

5. Diagnosis of problem (RCA and Quality tools deployed )

6. Ingenuity and Innovative approach

7. Benchmarking of the project

8. Sustainability of the project

9. Standardization and horizontal deployment

10.Impact of the problem (Tangible, Intangible and Socio-economic etc.)

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Abbreviation used in presentation

• CTQ – Critical to Quality

• OD – Outside Diameter

• CTP– Critical to Process

• I-MR Chart – Individual Moving Chart

• Gage R&R - Gage Repeatability & Reproducibility

• R- Chart - Range Chart

• X bar Chart – Average Chart

• ANOVA – Analysis Of Variance

• SD- Standard Deviation

• RCA – Root Cause Analysis

• Cp – Process Capability

• CpK- Process Capability Index

• Pp – Process Performance

• Ppk – Process performance index

• PPM – Parts Per Million

• CTQ – Critical to Quality

• OD – Outside Diameter

• CTP– Critical to Process

• I-MR Chart – Individual Moving Chart

• Gage R&R - Gage Repeatability & Reproducibility

• R- Chart - Range Chart

• X bar Chart – Average Chart

• ANOVA – Analysis Of Variance

• SD- Standard Deviation

• RCA – Root Cause Analysis

• Cp – Process Capability

• CpK- Process Capability Index

• Pp – Process Performance

• Ppk – Process performance index

• PPM – Parts Per Million

• R/O-Run Out

• FMEA- Failure Mode Effect Analysis

• R/O-Run Out

• FMEA- Failure Mode Effect Analysis

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10.Impact of the problem ( Tangible,Intangible and Socio-economic etc.)

Methodology

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Business CTQ: Remove the 100% Inspection of OD run out

Customer: FORD

Customer CTQ: Improve Ppk of window run out from 0.3 to 1.33

Internal CTQ / CBP: Eliminate the rejection due to window run out more than0.3

Problem Statement

APT Ltd has 100 % Inspection of OD Grinding Operation for Puma FlexPlate Assembly .Removing of 100% which leads to save the Inspection

time.

Problem Selection

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Goal Statement: Increase the Ppk of window run out from0.3 to 1.33 by 31st Mar‘16

Goal statement

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Source of project

Historically we were maintaining the window runout ofT-6,Assembly Component within 0.50 , due to someIssue – Design Related, Customer wants to change thewindow runout specification to max 0.3 ,Our Process is not capable to maintain the revisedspecification, So our Parts got reject at our end and wewere managing the process by doing the rework &100% inspection . So this was challenge to achieve thewindow run out with in 0.3 in the flex plate .To reduce the rework and 100 % inspection this projectinitiated.

P DA C

100% InspectionOD Runout

Run out is Out of specMore than (0.3)

Cpk is Less Ppk is LessInternalCTQ

CTQ Drill Down tree

P DA C

Drive PlateRing Gear

Both side View of Assembly Component of T-6 Ranger

Ring Gear Timing Can

Component Introduction

P DA C

Window run out checking gauge

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Methodology

P DA C

Atul Aggarwal Divakar Singh DevendraKumar

BhavneetKuamr Pawan Tyagi

Operation Lean Engineering Maintenance Production

ProjectLeader

Support inprojectdocket

Trial planningand execution

Ideageneration

and feasibilitystudy

Plant Head Manager A.M A.M Engineer

Photo

2 3 4 51 5

Cross Functional team

Pawan Khurana

Business Head

ProjectChampionand activemember

Director

Photo

Machine upkeeping as

per standard ,Machine

modification

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Methodology

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Cam Piercing

Stacking Gear

Press

MIGWelding

Turning(Off Line at

supplierEnd)

Can FaceTurning

WindowPiercing

RingGear

Gear

Press

Inspection

ok

Reject

As is process flow diagram

Balancing

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Loss Opportunity

Long Term

Short TermRunout more than 0.3will effect the increasingIn cost of Rejection &Lead to Customercomplain

Decrease CustomerSatisfaction and gap

between Demandvs Supply . Decrease

Profitability

Improve the ProcessCapability Index as well as

reliability

Increase the customerSatisfaction

Profitability & business

Loss – Opportunity Matrix

P DA C

0.3

0.37

0.32

0.4

0.280.25

0.28

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45Pp

k Va

lue

Avg Ppk Ppk Value

Conclusion: Average Ppk of window runout is 0.3

Current trend of Ppk (Jul’15 - Dec’15)

P DA C

0.3

1.33

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Avg Ppk Target Ppk

Ppk

Valu

ePpk Value

Avg PpkTarget Ppk

Target Setting

Conclusion: Target for Average Ppk is 1.33

P DA C

Conclusion: Average Ppk is 0.3

Quality Metric Values

Mean 0.251

Std Dev 0.051

Pp Can Not Calculated

Ppk 0.32

Sigma Level 0.96

Baseline of Window runout

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Methodology

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Ring Gear

• Improve the Ppk from 0.3 to 1.33Improve theProcess

Capability

• Very costly as per customerfeedback, So Ignore this Solution

Provide Sensorin Car Engine

Approach

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Gauge R&R For Window Run out

Part-to-PartReprodRepeatGage R&R

100

50

0

Per

cent

% Contribution% Study Var

109876543211098765432110987654321

0.02

0.01

0.00

Part No

Sam

ple

Ran

ge

_R=0.00333UCL=0.00858

LCL=0

Abhay Ravinder Sandeep

109876543211098765432110987654321

0.25

0.20

0.15

Part No

Sam

ple

Mea

n

__X=0.2332UCL=0.2366

Abhay Ravinder Sandeep

LCL=0.2298

10987654321

0.25

0.20

0.15

Part No

SandeepRavinderAbhay

0.25

0.20

0.15

Operator

10987654321

0.25

0.20

0.15

Part No

Ave

rage

AbhayRavinderSandeep

Operator

Gage name: Window Runout GaugeDate of study: 14-1-16

Reported by: Mr.PradeepTolerance:Misc:

Components of Variation

R Chart by Operator

XbarChart by Operator

Response by Part No

Response by Operator

Part No * OperatorInteraction

Gage R&R (ANOVA) for Response

Graphical Representation of GRR

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Conclusion : GRR is 8.48% and NDC = 16 , MSA is Acceptable

Two-Way ANOVA Table Without Interaction

Source DF SS MS F PPart No 9 0.134810 0.0149789 1242.93 0.000Operator 2 0.000016 0.0000078 0.65 0.527Repeatability 78 0.000940 0.0000121Total 89 0.135766

Gage R&R%Contribution

Source VarComp (of VarComp)Total Gage R&R 0.0000121 0.72

Repeatability 0.0000121 0.72Reproducibility 0.0000000 0.00

Operator 0.0000000 0.00Part-To-Part 0.0016630 99.28Total Variation 0.0016750 100.00

Study Var %Study VarSource StdDev(SD) (6 * SD) (%SV)Total Gage R&R 0.0034715 0.020829 8.48

Repeatability 0.0034715 0.020829 8.48Reproducibility 0.0000000 0.000000 0.00

Operator 0.0000000 0.000000 0.00Part-To-Part 0.0407797 0.244678 99.64Total Variation 0.0409272 0.245563 100.00

Number of Distinct Categories = 16

Gauge R&R For Window Run out

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Control Chart for Window Run out

Conclusion :Run out data on control chart is stable because no data point is out of control limit

464136312621161161

0.4

0.3

0.2

0.1

O bser vation

Indi

vidu

alVa

lue

_X=0.2631

U C L=0.4160

LC L=0.1101

464136312621161161

0.20

0.15

0.10

0.05

0.00

O bser vation

Mov

ing

Rang

e

__M R=0.0575

U C L=0.1879

LC L=0

I-MR Chart of Run out

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Process Capability of window run out

Conclusion : Ppk of the window run out = 0.30

0.400.360.320.280.240.200.160.12

USL

LSL *Target *USL 0.3Sample Mean 0.258918Sample N 2200StDev (Within) 0.0418927StDev (O v erall) 0.0455507

Process Data

C p *C PL *C PU 0.33C pk 0.33

Pp *PPL *PPU 0.30Ppk 0.30C pm *

O v erall C apability

Potential (Within) C apability

PPM < LSL *PPM > USL 87727.27PPM Total 87727.27

O bserv ed PerformancePPM < LSL *PPM > USL 163384.22PPM Total 163384.22

Exp. Within PerformancePPM < LSL *PPM > USL 183556.94PPM Total 183556.94

Exp. O v erall Performance

WithinOverall

Process Capability of Window Runout

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Methodology










10.Impact of the problem ( Tangible , Intangible and Socio-economic

etc.)

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Cause & Effect Diagram for Window R/O

runoutWindowPpk ofImprove

Environment

Measurement

Methods

Material

Machines

Personnel

AbsenteeismOperator

Unskilled Operator

Die looseMachine Spindle

Machine SpindleButton Blunt

Punch BluntM&R Die problem

Can OD turningMaterial

Ring & CanCan OD O/SCan R/O

Sheet thickneesTaper in Child

ClampungPart Clamping

Part OD ContactRadius matching

Burr on ODDie Clamping

Fixture Run outWindow getting

blockPlay in gauge Id

CalibrationGauge

GRR Not Ok

Cause-and-Effect Diagram

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Multi -Voting

S.N Categ Probable Causes Dharmendr Pawan Dushyant Devendra Mohit Mahesh RatingIndex

1 Man Unskilled 6 6 6 9 6 9 42

2 Operator Absenteeism 3 3 3 6 3 3 21

3

Machine

M&R Machine Die problem 6 6 6 6 6 3 33

4 Punch Blunt 9 9 6 6 6 9 45

5 Button Blunt 3 6 6 3 3 6 27

6 Machine Spindle play 3 6 1 3 6 1 20

7 Machine Spindle Over load 6 6 3 6 6 6 33

8 Die loose 3 9 6 6 3 3 30

9

Method

Window getting taper at Campiercing & Window Piercing 6 3 6 6 6 6 33

10 Fixture Run out not proper 6 3 6 6 3 3 27

11 Die Clamping 3 3 3 1 3 6 19

12 Burr on OD 6 6 6 3 6 3 30

13Radius not match at CamPiercing & Window piercing 6 6 9 3 6 6 36

14 Part OD Contact area 6 6 6 6 9 6 39

15 Part Clamping Not OK 3 6 6 3 6 1 25

16 Clamping pressure 1 3 3 3 6 3 19

17 Dust particle in contact area 1 3 3 6 3 6 22

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Multi- Voting

S.N Categ. Probable Causes Dharmendr Pawan Dushyant Devendra Manjinder Baljeet RatingIndex

18

Material

Taper in Child part 9 3 3 6 6 3 30

19 Sheet thickness Variation 9 9 9 6 6 6 45

20Can runout increase afterwindow piercing operation 9 6 9 6 6 6 42

21 Material handling 6 3 6 3 9 6 33

22 Ring & Can Interference 6 9 6 3 3 6 33

23 Can OD O/S 6 9 6 6 9 6 42

24 Can OD turning 1 6 3 3 6 3 22

25Measure

ment

GRR Not Ok 6 3 6 6 3 1 25

26 Gauge Calibration 3 6 3 3 1 6 22

27 Play in gauge Id block 3 3 6 6 6 3 27

Rating Scale : 0,1,3,6 & 9 Importance/Impact

0 No

1 Less

3 Medium

6 Medium-High

9 HighPrioritized X’s Pick up rating index value above =40

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Categorization of prioritized Causes

1 1

3

0

0.5

1

1.5

2

2.5

3

3.5

Man Machine Material

Caus

es in

No

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Data Statistical validation plan

SNo. Potential Cause Data Type Test to be

performed

1 Unskilled Operator (X1) Discrete ANOVA

2 Punch Blunt (X2) Discrete ANOVA

3 Sheet thickness variation (X3) Continuous Regression

4

Can runout increase afterWindow Piercing operation (X4) Continuous Regression

5 Can OD O/S (X5) Discrete ANOVA

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ANOVA test for Operator Skill ( X1)

General Linear Model: Operator Skill versusRunout

Factor Type Levels ValuesOperator fixed 2 Operator Skilled, Operator Unskilled

Analysis of Variance for Operator not Skilled, using AdjustedSS for Tests

Source DF Seq SS Adj SS Adj MS F POperator 1 0.062161 0.062161 0.062161 14.22 0.002Error 17 0.074312 0.074312 0.004371Total 18 0.136474

S = 0.0661159 R-Sq = 45.55% R-Sq(adj) = 42.35%

General Linear Model Show that , Operator skill is a Significant factor (P<0.05)

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Why – Why analysis ( X1)

Defect

Why 2

Why 3

Operator does not know how to clamp the part

Operator is not Skilled

Why 1

Window Runout moreWindow Runout more

RootCause

Conclusion : Provide the training to Operator

Part Clamping process is not proper

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Training Imparting ( X1)

Provide the Training to All Operator.

COUNTERMEASURE

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Validation of Punch Blunt (X2)

General Linear Model: Run out versus Punch Blunt

Factor Type Levels ValuesPunch Blunt fixed 2 Punch Blunt, Punch Not Blunt

Analysis of Variance for Run out, using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F PPunch Blunt 1 0.054797 0.054797 0.054797 21.37 0.000Error 17 0.043582 0.043582 0.002564Total 18 0.098379

S = 0.0506326 R-Sq = 55.70% R-Sq(adj) = 53.09%

Conclusion : General Linear Model Show that , Punch Blunt is a Significant factor (P<0.05)

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Why – why analysis Punch Blunt (X2)

Defect

Why 2

Why 3

Slot Punching tool Blunt

No trigger is provided for tool reshape

Why 1

Window Runout is moreWindow Runout is more

RootCause

Window Piercing is not proper

Conclusion : OPL prepared and Audio alarm to be installed on window piercing

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Developing Solution Punch Blunt (X2)

Through Brain Storming

COUNTERMEASURE

Starting tool history card system to monitor toollife and reshaping frequency.

Audio Alarm to be installed on window piercingmachine

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Conclusion : Scatter diagram show that there is weak negative relation between sheet thickness &Window R/O

Scatter plot for Sheet thickness Variation (X3)

2.702.652.602.552.50

0.36

0.34

0.32

0.30

0.28

0.26

0.24

0.22

0.20

Sheet thickness

Run

out

Scatterplot of Run out vs Sheet thickness

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Correlation b/w Sheet thickness & R/O

r=-0.114

Correlations: Runout, Sheet thickness

Pearson correlation of Run out and Sheetthickness = -0.106

P-Value = 0.675

Correlations: Runout, Sheet thickness

Pearson correlation of Run out and Sheetthickness = -0.106

P-Value = 0.675

Conclusion : Correlation coefficient = - 0.106 Show that weak negative relation ship between sheetthickness and run out and not significant because P value > 0.05

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Regression of sheet thickness & R/O

Regression Analysis: Run out versus Sheet thickness

The regression equation isRun out = 0.4940 - 0.0867 Sheet thickness

S = 0.0499520 R-Sq = 1.1% R-Sq(adj) = 0.0%

Analysis of Variance

Source DF SS MS F PRegression 1 0.0004545 0.0004545 0.18 0.675Error 16 0.0399232 0.0024952Total 17 0.0403778

Conclusion : Regression Show that sheet thickness is not a significant factor for run out because p value is > 0.05

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Conclusion : there is a strong Positive relation between Can runout increase after window piercing operation &Response

0.500.450.400.350.30

0.40

0.35

0.30

0.25

0.20

Can OD Runout up to 0.5

Res

pons

e

Scatter Diagramfor CanODRunout vs Response

Scatter diagram for Can runout increase after windowpiercing operation & Response (X4)

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Correlation b/w Can runout increase after windowpiercing operation & Response (X4)

r=-0.114

Correlations: Can runout increase, afterwindow piercing operation

Pearson correlation of Can OD Runout up to 0.5and Response = 0.861P-Value = 0.000

Correlations: Can runout increase, afterwindow piercing operation

Pearson correlation of Can OD Runout up to 0.5and Response = 0.861P-Value = 0.000

Conclusion : r=0.861 show that there is a strong Positive relation between Can Run out up to 0.5 & Response

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Conclusion : Regression Show that , Can runout increase after window piercing operation is aSignificant factor (P< 0.05)

Regression test for Can runout increase after windowpiercing operation (X4)

Regression Analysis: Response versus Can runoutincrease after window piercing operation

The regression equation isResponse = - 0.00645 + 0.7325 Can runout increaseAfter window piercing operation

S = 0.0287929 R-Sq = 74.1% R-Sq(adj) = 72.6%

Analysis of Variance

Source DF SS MS F PRegression 1 0.0403591 0.0403591 48.68 0.000Error 17 0.0140935 0.0008290Total 18 0.0544526

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Why - Why Analysis (X4)

Due to performing Window Piercing Operation,after Turning

Defect

Why 2

Why 3

Natural distortion due to piercing operation

Process limitation

Why 1

RootCause

Conclusion : Introduce Grinding operation for can OD Grinding

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Validation of Can OD Over Size (X5)

General Linear Model: Run Out versus Can OD O/S

Factor Type Levels ValuesCan OD O/S fixed 2 Can OD Over Size, Can OD Size OK

Analysis of Variance for Run Out, using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F PCan OD O/S 1 0.000050 0.000050 0.000050 0.04 0.838Error 16 0.018578 0.018578 0.001161Total 17 0.018628

S = 0.0340751 R-Sq = 0.27% R-Sq(adj) = 0.00%

General Linear Model Show that , Can OD Over Size is not a Significant factor (P>0.05)

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Summary of Data validation

SNo. Potential Cause Data Type P-Value Impact

1 Unskilled Operator (X1) Discrete 0.002 Significant

2 Punch Blunt (X2) Discrete 0.000 Significant

3Sheet thickness Variation(X3) Continuous 0.643 Non

Significant

4

Can runout increase afterWindow Piercing operation(X4)

Continuous 0.000 Significant

5 Can OD O/S (X5) Discrete 0.838Non

SignificantSignificant

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Methodology










10.Impact of the problem ( Tangible , Intangible and Socio-economic

etc.)

P DA C

Solution Drill down tree

Reduce R/Ounder 0.3

Designprocess or

product

Reduce R/Ounder 0.3

Process needsredesign

Introduce Grindingoperation

Design productin a Car

Sensor Designfor R/O >0.3mm

HighInvestment

ProductProcess

√

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Action plan for validated X’s

S.N Action Plan Responsibility Status

1 Prepare a training Plan & Provide the “On job training” to all Operators Pawan Tyagi Done

2 Starting tool history card system to monitor Punch life and regrindingfrequency Atul Aggarwal Done

3 Customer has been agreed to Introduced Grinding operation Atul Aggarwal Done

4 New Machine procurement process has been finalized & gotManagement approval Atul Aggarwal Done

5 Machine capability has been proved at manufacturer end Devendra Kumar Done

6 New Machine has been procured for grinding operation Atul Aggarwal Done

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WI for tool life monitoring

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Pictures of Grinding Machine

Grinding Machine has been Installed for Grinding Operation

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Methodology










10.Impact of the problem (Tangible,Intangible and Socio-economic etc.)

P DA C

55

Bench Marking

Bench Marking activity is not applicable because of below

reasons:- Runout specification on other products is 0.5mm (max.)

- No Flex plate Manufacturer does Grinding to maintain the runout as the

specification is unique in nature

Although we have tried to bench mark the process within the organization by

comparing both the processes based on the product specification, without Grinding

and with grinding and significant improvement is noticed.

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Process Benchmarking

0.3

1.33

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Avg Ppk Benchmark Ppk

Ppk

Valu

e

Conclusion : Before and after control chart shows significant improvement in Ppk

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Control chart of window run out before and after

Conclusion : Before and after control chart shows significant improvement in window run out

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Window run out before and afterBEFORE AFTER

Before 70% data is below the Target ( 0.3) & After 100% data is below the target (0.3) Window Runout

Too many outliers in after conditions

W indow Runout AfterW indow Runout Before

0.40

0.35

0.30

0.25

0.20

0.15

0.10

Wind

owRu

nout

data 0.3

Boxplot of Window Runout Before, Window Runout After

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Methodology











P DA C

Control chart of window run out before and after

Conclusion : Before and after control chart shows significant improvement in window run out

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0.37 0.320.4

0.28 0.25 0.28

1.4 1.45 1.48

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Jul'15 Aug'15 Sep'15 Oct'15 Nov'15 Dec'15 Jan'16 Feb'16 Mar'16

Ppk

Valu

e

Conclusion: Above trend chart shows improvement in Ppk value of window run out

Sustenance of PpkBefore After

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Effectiveness of solutions

0.3

1.45

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Avg Ppk Achieved Ppk

Ppk

Valu

eTarget is

1.33

Conclusion : Before and after control chart shows significant improvement in Ppk

P DA C

Methodology










10.Impact of the problem (Tangible , Intangible and Socio-economic

etc.)

P DA C

Control Plan

Control Plan has been modified for Grinding Operation

P DA C

Cam Piercing

Stacking Gear

Press

MIGWelding

Turning(Off Line at

supplierEnd)

Can FaceTurning

WindowPiercing

RingGear

Gear

Press

Modified process flow diagram ( After)

Balancing

GrindingGrinding machineinstalled in aftercondition

P DA C

Process Capability before and after comparison

Conclusion : Window run out capability Ppk Shows significant improvement to meet the customer requirement

P DA C Methodology











P DA C Financial Benefit saving sheet Post project

Net Saving per Annum=68.23 Lac INR

P DA C

•Customer Satisfaction Improve

•High Morale

•Improve process capability

•Increase in Confidence

•100% Inspection stop

•Reduce Inspection time

•Team Spirit Enhancement

•Product Knowledge Increase

Intangible Benefit

P DA C

driven by excellence

Documents