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To identify and improve the To identify and improve the key factor(s) contributing to key factor(s) contributing to operator attritionoperator attrition
Kaustubh KulkarniHyderabad Plant
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Project Charter, TeamProject Charter, Team
Project TitleProject Title
To identify and improve the key factor(s) contributing to operator attrition
Project SponsorProject Sponsor Nagaraja Rao, Plant Head
Black BeltBlack Belt Abraham Chacko
Project LeaderProject Leader Kaustubh Kulkarni
Team MembersTeam Members Vijaya Reddy, HR Executive
Revi Vasudevan, Mgr - Production
D M A I C
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Project Charter, DescriptionProject Charter, Description
Project DescriptionProject Description
Purpose of the project is to identify and improve the key factor(s) contributing the operator attrition
Process and Project PerimeterProcess and Project Perimeter
Operators at the Hyderabad Plant, India
Project GoalsProject Goals
�Reduce attrition rate from 12% to less than 6%
�Reduce replacement recruitment cost
�Reduce Re-training hours
�Reduce potential for product non-conformities
D M A I C
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Project Charter, FinancialsProject Charter, Financials
Financial Savings for the CompanyFinancial Savings for the Company� Cost of Operator replacement is Rs. 3,000
� An operator takes at least 2 weeks (initial learning curve) to get trained and deliver required output
� Other savings include reduced potential for non-conformities leading to possible customer dissatisfaction
� Material scrap generated as a consequence of faulty manufacturing
D M A I C
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Project Charter, TimelinesProject Charter, Timelines
Project TimelinesProject Timelines
Start Date: 5th April 2007 End Date: 30th September 2007
Project PhasesProject Phases
Define and Measure 5th April 2007 – 15th May 2007
Analyze 16th May 2007 – 15th June 2007
Improve and Control 16th June 2007 – 30th September 2007
D M A I C
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
D M A I CSS--II--PP--OO--CC
Employee ReferralEmployee Referral
AdvertisementsAdvertisements
WalkWalk --InsIns
Recruitment Recruitment ConsultantsConsultants
Supplier
Potential Potential CandidateCandidate
Input
Selection and Selection and Retention Retention
of right of right candidatecandidate
Process Output
Production Production FunctionFunctionTrained and Trained and
Retained Retained CandidateCandidate
Customer
ManagementManagement
EndEnd --UserUser
DefectDefect --Free Free ProductsProducts
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Attrition Trend, Oct 06 Attrition Trend, Oct 06 –– Mar 07Mar 07
O c t - 0 6 1 6 4 7 3 5 . 4 8 %N o v - 0 6 7 2 7 8 2 . 5 6 %D e c - 0 6 3 7 2 1 1 3 1 . 7 7 %J a n - 0 7 2 1 7 1 2 7 5 . 5 1 %F e b - 0 7 1 8 1 5 1 3 0 1 1 . 5 4 %M a r - 0 7 1 5 1 6 1 2 9 1 2 . 4 0 %
E m p l o y e e s E m p l o y e e s E m p l o y e e s E m p l o y e e s L e f tL e f tL e f tL e f t
M o n t h - E n d M o n t h - E n d M o n t h - E n d M o n t h - E n d H e a d c o u n tH e a d c o u n tH e a d c o u n tH e a d c o u n t
S p o t S p o t S p o t S p o t A t t r i t i o n %A t t r i t i o n %A t t r i t i o n %A t t r i t i o n %
2 0 0 72 0 0 72 0 0 72 0 0 7
Y e a rY e a rY e a rY e a r M o n t hM o n t hM o n t hM o n t hE m p l o y e e s E m p l o y e e s E m p l o y e e s E m p l o y e e s
J o i n e dJ o i n e dJ o i n e dJ o i n e d
2 0 0 62 0 0 62 0 0 62 0 0 6
D M A I C
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
D M A I CDefinition and Sampling PlanDefinition and Sampling Plan
Data PatternData Pattern
The Hyderabad Plant started with the high volume 2 shift production of Industrial Control products from January 2007. At this time we started experiencing a high rate of operator attrition suddenly, leading serious concerns on being able to ramp up production to meet demanding market schedules. The hypothesis was that the shift operations were contributing to the high rate of attrition that got introduced in January of 2007.
Resignation Resignation –– Operational DefinitionOperational Definition
The last working day of the the employee is the date of relieving of the employee.
Sampling Plan and StrategySampling Plan and Strategy
The data for all the employees being available from inception in October 2005, the entire population was used as part of the analysis for this project.
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
FishFish --Bone/Ishikawa DiagramBone/Ishikawa Diagram D M A I C
Organizational Aspects
Personal ReasonsCandidate Profile
Shift Working
Logic Score
Distance from Plant
Product Line IC, LV, MV
Work Strain
Qualification
Age
Pursue furtherEducation
Domiciliary Status
Marriage
Health ReasonsOther
Opportunities
XX
XX
XX
Operator Attritionat the
Hyderabad Plant
YY
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Data Collection Sample SheetData Collection Sample Sheet D M A I C
# Name DOB DOJ DORProduct
LineService Length
Distance from Plant
Age in Yrs
Education ShiftsG 10 Score
Dom. Status
Work Status
107 P.Bhavani 5/May/1984 8/Jan/2007 Tesys 283 12 23 Inter Y 26 N A108 D.Srividya 7/Feb/1988 8/Jan/2007 Activa 283 24 19 Inter Y 25 Y A109 Ch.Aswini 28/Jun/1988 9/Jan/2007 2/Feb/2007 Tesys 258 6 19 Inter Y 29 N R110 K.Mamatha 16/Jul/1988 18/Jan/2007 5/Jul/2007 Tesys 105 63 19 Inter Y 28 Y R111 P.Swapna 10/Jun/1984 22/Jan/2007 2/Feb/2007 Tesys 258 6 23 Graduate Y 26 N R112 G.Anuradha 4/Feb/1985 22/Jan/2007 26/Mar/2007 Tesys 206 5 22 Graduate Y 17 Y R113 Ms.T.Anuradha 25/Mar/1986 22/Jan/2007 18/Apr/2007 Tesys 183 5 21 Graduate Y 26 N R114 V.Lakshmi 8/Apr/1987 24/Jan/2007 30/Mar/2007 Tesys 202 12 20 Inter Y 23 N R115 K.Srilatha 19/Jul/1985 24/Jan/2007 2/Jul/2007 Tesys 108 1 22 Inter Y 30 N R116 K.Swetha 18/Aug/1986 24/Jan/2007 Tesys 267 5 21 Inter Y 23 Y A117 A.Srivani 31/Oct/1987 24/Jan/2007 Tesys 267 13 19 Inter Y 24 N A118 Ch.Pranitha 14/Jun/1987 24/Jan/2007 Stores 267 30 20 Inter N 24 N A119 G.Jyothi 10/Jul/1984 24/Jan/2007 Tesys 267 19 23 Inter Y 15 Y A120 K.Vijayalakshmi 14/Jun/1985 24/Jan/2007 Tesys 267 13 22 Inter Y 20 N A121 B.Swapna 6/May/1983 5/Feb/2007 Tesys 255 40 24 Inter Y 20 N A122 T.Sujatha 21/Jun/1987 7/Feb/2007 8/Feb/2007 Tesys 252 63 20 Inter Y 22 Y R123 P.Nagarani 19/May/1986 7/Feb/2007 6/Mar/2007 Tesys 226 19 21 Inter Y 20 N R124 J.Bhavani 10/Jun/1988 7/Feb/2007 Tesys 253 13 19 Inter Y 22 N A125 T.Lavanya 14/Jul/1988 7/Feb/2007 Stores 253 22 19 Inter N 25 N A
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Normality Plot for Data Normality Plot for Data -- YY D M A I C
y
Percent
6005004003002001000-100-200-300
99.9
99
95
90
80
7060504030
20
10
5
1
0.1
M ean
<0.005
151.4
S tD ev 128.9
N 71
A D 2.574
P -Valu e
P robabil ity P lot of Y (D is tance from P lant)Norm a l
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Residuals and Data NormalizationResiduals and Data Normalization D M A I C
Residua l
Percent
4002000-200-400
99.9
99
90
50
10
1
0.1
F itted V a lue
Residual
300200100
300
150
0
-150
-300
Residua l
Frequency
2001000-100-200
16
12
8
4
0
O bse r vation O r der
Residual
7065605550454035302520151051
300
150
0
-150
-300
No rmal P ro b ab ilit y P lo t o f t h e R esid u als R esid u als Versu s t h e Fit t ed Valu es
H ist o g ram o f t h e R esid u als R esid u als Versu s t h e Ord er o f t h e Dat a
Res idua l P lots for y
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Statistical Tests for SignificanceStatistical Tests for Significance D M A I C
Two Sample TestsTwo Sample Tests
�� Age
� Logic Test Scores
� Distance
ChiChi --Square TestsSquare Tests
�� Working in Shifts – Yes/No
� Staying with Parents – Yes/No
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
TwoTwo --Sample T and Box PlotSample T and Box Plot
Age Age –– Active vs. ResignedActive vs. ResignedD M A I C
Two-Sample T-Test and CI: Age A, Age R
Two-sample T for Age A vs Age R
N Mean StDev SE Mean
Age A 163 17.65 1.86 0.15
Age R 99 17.94 1.95 0.20
Difference = mu (Age A) - mu (Age R)
Estimate for difference: -0.289087
95% CI for difference: (-0.770619, 0.192444)
T-Test of difference = 0 (vs not =):
T-Value = -1.18
P-Value = 0.238
DF = 199
Data
Age RAge A
25.0
22.5
20.0
17.5
15.0
Individual Value Plot of Age A, Age R
Data
Age RAge A
25.0
22.5
20.0
17.5
15.0
Boxplot of Age A, Age R
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
TwoTwo --Sample T and Box PlotSample T and Box Plot
Logic Test Scores Logic Test Scores –– Active vs. ResignedActive vs. ResignedD M A I C
Two-Sample T-Test and CI: Test Score A, Test Score R
Two-sample T for Test Score A vs Test Score R
N Mean StDev SE Mean
Test Score A 158 23.71 4.64 0.37
Test Score R 94 25.07 3.99 0.41
Difference = mu (Test Score A) - mu (Test Score R)
Estimate for difference: -1.36561
95% CI for difference: (-2.45518, -0.27603)
T-Test of difference = 0 (vs not =):
T-Value = -2.47
P-Value = 0.014
DF = 218
Data
Test Score BTest Score A
40
35
30
25
20
15
10
Individual Value Plot of Test Score A, Test Score B
Data
Test Score BTest Score A
40
35
30
25
20
15
10
Boxplot of Test Score A, Test Score B
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
TwoTwo --Sample T and Box PlotSample T and Box Plot
Distance Distance –– Active vs. ResignedActive vs. ResignedD M A I C
Data
Dist. RDist. A
70
60
50
40
30
20
10
0
Individual Value Plot of Dist. A, Dist. R
Data
Dist. RDist. A
70
60
50
40
30
20
10
0
Boxplot of Dist. A, Dist. R
Boxplot of Dist. A, Dist. R
Two-Sample T-Test and CI: Dist. A, Dist. R
Two-sample T for Dist. A vs Dist. R
N Mean StDev SE Mean
Dist. A 163 17.0 10.6 0.83
Dist. R 99 22.8 16.4 1.6
Difference = mu (Dist. A) - mu (Dist. R)
Estimate for difference: -5.72473
95% CI for difference: (-9.36979, -2.07967)
T-Test of difference = 0 (vs not =):
T-Value = -3.10
P-Value = 0.002
DF = 148
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
ChiChi --Square TestsSquare Tests D M A I C
Chi-Square Test: Active, Resigned for Candidate
Staying with Parents and Away from Parents
Expected counts are printed below observed counts
Chi-Square contributions are printed below expected counts
Active Resigned Total
1 81 43 124
77.15 46.85
0.193 0.317
2 82 56 138
85.85 52.15
0.173 0.285
Total 163 99 262
Chi-Sq = 0.968, DF = 1, P-Value = 0.325
Chi-Square Test: Active, Resigned for Candidate
Working in Shifts and Not Working in Shifts
Expected counts are printed below observed counts
Chi-Square contributions are printed below expected counts
Active Resigned Total
1 64 53 117
72.62 44.38
1.023 1.675
2 98 46 144
89.38 54.62
0.831 1.361
Total 162 99 261
Chi-Sq = 4.890, DF = 1, P-Value = 0.027
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Data Collection Sample SheetData Collection Sample Sheet D M A I C
Binary Logistic Regression: C2 versus C1Binary Logistic Regression: C2 versus C1
Link Function: Logit
Response Information
Variable Value Count
C2 1 83 (Event)
0 172
Total 255
Logistic Regression Table
Odds 95% CI
Predictor Coef SE Coef Z P Ratio Lower Upper
Constant -1.55402 0.258814 -6.00 0.000
C1 0.0408698 0.0106133 3.85 0.000 1.04 1.02 1.06
Log-Likelihood = -152.675
Test that all slopes are zero: G = 16.429, DF = 1, P-Value = 0.000
Distance is statistically significant
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Statistical Findings and Statistical Findings and ConclusionsConclusions
D M A I C
Two Sample TestsTwo Sample Tests pp--ValueValue
�� Age 0.238
� Logic Test Scores 0.014
� Distance 0.002
ChiChi --Square TestsSquare Tests
�� Working in Shifts – Yes/No 0.027
� Staying with Parents – Yes/No 0.325
P-value being less than 0.05, indicates statistically
significant process influence
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Statistically Significant AspectsStatistically Significant Aspects
� Logic Test Scores, 0.014
� Distance, 0.002
�� Working in Shifts – Yes/No, 0.027
.
This indicates that individuals with lower scores tend to continue in service with us, while the ones with higher scores are more likely to pursue other options. While the entry level criteria cannot be diluted, this aspect has the potential for a future six sigma to correlate test scores and their impact on operator efficiency
Both Distance and Shift Working have an influence on each other and summary explanation with recommended actions is provided below:
From the analysis it is clear the individuals staying further away from the company are more likely to resign. This has also been validated through a one-on-one interaction with the operators. This is on account of the hardship they face when they have to come in the first shift (start from home at 4 am) and the time they reach home in the second shift (as late as 12 am in some instances).
Analysis of FindingsAnalysis of Findings D M A I C
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
D M A I C
Solutions generated and actions implemented from Ju ne 2007 Solutions generated and actions implemented from Ju ne 2007
� Distance, 0.002
� Working in Shifts, 0.027
.
� Earlier, during the interview process there was no specific focus on the distance of the candidate from the company. Now we have included this aspect in the interview selection and short-listing stage itself by flagging this question in the “Candidate Personal Information Form”. The attempt is to control and select candidates to within 25 kms of the plant radius.
� We have also added smaller, additional vehicles for the early morning pick-up and late night-drop to facilitate easier and quicker employee movement as our entire operator population is female, and it is a concern and responsibility to ensure this
Improvement RecommendationsImprovement Recommendations
� The shift working is a business requirement and cannot be altered. However to address this hardship we have introduced the concept of shift allowance for all the operators who work in shifts other than the general shift
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
Target Level of 6%Target Level of 6%
D M A I C
We had higher attrition in this month when about 7-8
employees left to pursue further education. This was a
spot incidence. Excluding these numbers attrition is
within the 6% target
Attrition Trend, Jan 07 Attrition Trend, Jan 07 –– Sep 07Sep 07
Prior to Six Sigma D & M A Improve and Control
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
D M A I COverall Improvement Overall Improvement –– Before Before and Post Implementation of 6Sand Post Implementation of 6S
B e f o r e S i x - S i g m aB e f o r e S i x - S i g m aB e f o r e S i x - S i g m aB e f o r e S i x - S i g m a A f t e r S i x - S i g m aA f t e r S i x - S i g m aA f t e r S i x - S i g m aA f t e r S i x - S i g m aR e c r u i t e d 1 7 9 8 3
A c t i v e 9 0 7 3R e s i g n e d 8 9 1 0
%%%% 4 9 . 7 2 %4 9 . 7 2 %4 9 . 7 2 %4 9 . 7 2 % 1 2 . 0 5 %1 2 . 0 5 %1 2 . 0 5 %1 2 . 0 5 %
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
D M A I CKey Learnings and Key Learnings and ReccomendationsReccomendations
Key LearningsKey Learnings� Define well – This is extremely critical as this is what provided the ‘anchor’ as you
navigate through the project complexities. Think ahead of how you expect to proceed, what tools you potentially intend to use. This helps avoid reaching the IC stage and finding out the only meaningful tool you could have used is a Pareto
� Expect the Unexpected – Hyderabad Plant being a new plant, the team was not aware of the key issues that would surface. Distance was not imagined as a constraint as we were providing transport facility. It was only when we went into shifts and started analyzing the situation were we able to control for this critical aspect
� Involve All – When a situation arises, don’t adopt a stance of management knows best. Make cross functional teams that cut-across hierarchies
� Be data and fact driven – Avoid preconceived biases from coloring your analysis phase. Be open to all ideas and creative brain-storming suggestions
� Be patient – there is a tendency to rush through some stages of the DMAIC cycle. Each stage is equally important, and more so the improve and control stages as this is where the rubber meets the road – the final validation of your assumptions and solutions!.
To identify and improve the key factor(s) contributing to operator attrition
Kaustubh Kulkarni, GB, Hyderabad Plant
ThankThank --You!You!