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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
P DA C
4
Nissan
General Motors
PSA
Ford
Mazda
FIAT
Our Customer
P DA C
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.)
P DA C
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
P DA C
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.)
Methodology
P DA C
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
P DA C
Goal Statement: Increase the Ppk of window run out from0.3 to 1.33 by 31st Mar‘16
Goal statement
P DA C
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
P DA C
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.)
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
P DA C
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.)
Methodology
P DA C
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
P DA C
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
P DA C
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.)
Methodology
P DA C
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
P DA C
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
P DA C
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
P DA C
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
P DA C
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
P DA C
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.)
P DA C
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
P DA C
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
P DA C
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
P DA C
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
P DA C
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
P DA C
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)
P DA C
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
P DA C
Training Imparting ( X1)
Provide the Training to All Operator.
COUNTERMEASURE
P DA C
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)
P DA C
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
P DA C
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
P DA C
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
P DA C
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
P DA C
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
P DA C
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)
P DA C
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
P DA C
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
P DA C
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
P DA C
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)
P DA C
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
P DA C
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.)
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
√
P DA C
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
P DA C
WI for tool life monitoring
P DA C
Pictures of Grinding Machine
Grinding Machine has been Installed for Grinding Operation
P DA C
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.)
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.
P DA C
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
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
P DA C
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
P DA C
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.)
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
P DA C
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
P DA C
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
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.)
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
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.)
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