Six Sigma Project - Operators Attrition

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


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



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



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



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



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



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



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.



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



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



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



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



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



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




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


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)


95% CI for difference: (-2.45518, -0.27603)


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




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


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)


95% CI for difference: (-9.36979, -2.07967)


T-Value = -3.10

P-Value = 0.002

DF = 148



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



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



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



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



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



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



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 %



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!.



ThankThank --You!You!

Six Sigma Project - Operators Attrition

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