This document outlines the steps and deliverables for a project aimed at reducing attrition rates. Key deliverables include process maps, measurement plans, and analysis of impacting factors. Analysis identified four statistically significant factors impacting attrition: poor facilities/food quality, hiring overqualified resources, better opportunities elsewhere, and overtime compensation. These factors will be focused on in the improvement phase to develop solutions for reducing attrition rates.

2. D1. Map Project
D2. Approve Project
M1. CTQ Characteristics& Standards
M2. Measurement System Analysis
M3. Data Collection
A1. Baseline Process
A2. Performance Objective
A3. Identify Drivers of Variation
I1. Screen for Vital Xs
I2. Screen for vital Xs
I3. Define Improved Process
C1. MSA on Xs
C2. Improved Process Capability
C3. Establish Control Plan
Key Deliverables:
• List of Customer(s) and Project
CTQs
• Team Charter
• High Level Process Map (COPIS)
• CAP Plan (Optional)
• Preliminary CBA, if applicable
• QFD / CTQ Tree
• Operational definition,
Specification limits, target, defect
definition for Project Y(s)
• Data Collection Plan
• Measurement System Analysis
• Baseline of Current Process
Performance
• Normality Test
• Statistical Goal Statement for
Project
• List of Statistically Significant Xs
• List of Vital Few Xs
• Transfer Function(s)
• Optimal Settings for Xs
• Confirmation Runs/Results
• Tolerances on Vital Few Xs
• MSA Results on Xs
• Post Improvement Capability
• Statistical Confirmation of
Improvements
• Process Control Plan
• Process Owner Signoff
• Final CBA, if applicable
Tollgates:
Planned
Completed
02/Aug2015 05/Aug/2015 20/08/2015 10/09/2015 23/09/2015
XXXXXX 15/March/2010 mm/dd/yyyy mm/dd/yyyy mm/dd/yyyy
Steps:
Tools:  Survey
 Focus Groups
 Interviews
 ARMI, Stakeholder Analysis
 In/Out of Frame
 Threat vs. Opportunity Matrix
 Other ______________
 Data Collection Plan
 Continuous Gage R&R
 Attribute Gage R&R
 Sample Size Calculator
 Other ______________
 Basic Statistics
 Histogram
 Dot Plots
 Box and Whisker Plots
 Run Charts
 Normality Testing
 Continuous/Discrete Zst, Zlt
 Benchmarking
 Detailed Process Mapping
 Moments of Truth
 Nature of Work
 Flow of Work
 Fishbone
 Hypothesis Testing
 Regression Analysis
 Other ______________
 DOE
 Pugh Matrix
 New Process Mapping
 FMEA on new process
 Process Modeling / Simulation
 Other ______________
 Continuous Gauge R&R
 Discrete Gauge R&R
 Control Charts
 Hypothesis Testing
 CAP Plan
 Control Plan
 Other ______________
Define Measure Analyze Improve Control
OVERVIEW Project Progress Tracking

3. Define Deliverables
DEFINE
SIPOC
Process Map
Communication Plan
ARMI/RASIC
Terms & Acronyms Used
Project Charter
Map the Project

4. Project Mapping
DEFINE
Customer Sample Comments
Key Output Characteristics
Important to Customer (CTQ's)

5. Project Charter
Business Case:
XYZ is an Indian Multinational IT consulting firm having offices in India,
Philippines, US. It deal with providing consultancy services to insurers,
brokers and investors. It has been observed that Attrition for brokering
account in India has been on increasing trend since last one year.
Criticality of doing the Project:
Failing which would lead to backlog of inventory, revenue loss and loss of
knowledgeable resources.
Worthiness of Project:
Project has to be undertaken to reduce to restore the knowledge
retention and increase the bottom-line profitability worth $720,000.
Business Initiative:
XYZ company plans to extend the employee base and take on more
business processes/projects to become market leader in consulting.
Improvement in attrition would help XYZ to achieve the same.
6 Months Data Overview:
Problem Statement:
Having looked at last six months data, process is performing at 28% attrition against
target of 20%. From the total employee strength of 1200 at the end of FY 2013-14, it
is observed that employee strength at present is 450 against the acceptable limit of
600 considering the global slump.
Organization is getting impacted with $900,000 annually for loss of 8% more attrition
than target.
(150x500x12)
Attrition number above set limit = 150
Monthly Employee Rate = $500
Number of Months = 12
Goal Statement:
To reduce attrition percentage in two phases from 28% to 24% by 5th Sept
(phase I) and 20% by 31st October (phase II)
DEFINE
Month Attrition %
January 45%
February 43%
March 38%
April 39&
May 41%
June 34%

6. D E F I N E Project Scope Design
ABC
InternationalCompany
India
Insurers Brokers
USA
Insurers Brokers

7. D E F I N E Project Charter
Target Date Actual Date
Start Date 2nd Aug 2nd Aug
Define 2nd Aug 2nd Aug
Measure 5th Aug 15th Aug
Analyze 20th Aug 25th Aug
Improve
10th
Sept
12th Sept
Control 23rd Sept 24th Sept
High Level Project Plan
In Scope:
 FTEs
Out Scope:
Rest of the Functional team
Team:
Sponsor : Pranay Kumar
Champion: Neha Sharma
MBB: Sunil Dubey
BB: Ravi Ranjan/Manish Yadav
GB: Kapil Mohan Sharma
Process Owner: Mithlesh Nautiyal

8. Terms & Acronyms Used
DEFINE
Indicators Definition
FTE Full Time Employees
SME Subject Matter Experts
CTQ Critical to Quality
P.O. Process Owner
HOD Head of Department

9. ARMI
DEFINE
Key Stakeholders Define Measure Analyze Improve Control
HOD A/I I I A/I A/I
MBB R A/R/I A/R R/I R/I
BB R R/M R R R
GB R/M R/M R/M R/M R/M
Process Owner M R/M R/M M/I M/I
When Populating the Stakeholder, consider the ARMI:
• A= Approver of team decisions
• R= Resource or subject matter expert (ad hoc)
• M= Member of team
• I= Interested Party who will need to be kept informed

10. Communication Plan
DEFINE
Message Audience Media Who When
Project Review Sponsor/Champion/Black Belt Email/Conference Call Black Belt Monthly/Weekly/Bi-weekly
Project Approval Sponsor/Champion Email/PPT Black Belt
As per Plan
Tollgate Review HOD/MBB Conference Call/Announcement Black Belt As per Plan
Scope Change Client/HOD/P.O. Conference call/Email Black Belt Whenever required

11. Process Map
DEFINE

12. COPIS
DEFINE
Customer Output Process Input Supplier
XYZ Company HR Department
Reduced attrition % Attrition
Reports/Dashboards
Hiring Indent raised
CVs are scrutinized
Offer letter issued
Hiring Indent Closed
Training imparted
License to operate
certified

13. Measure Deliverables
MEASURE
Process Capability
Measurement System Analysis (MSA)
Data Collection Plan

14. Data Collection Plan
MEASURE
Y
Operational
Definition
Defect Definition
Performance
Standard
Specification Limit Opportunity
Annual Attrition %
increasing
Any individual working as
Full time or Part time
decides to move on by
serving one months notice
If an attrition reported takes
the attrition percentage to
greater than 30%, it is a
defect
Organization should work
at minimum attrition
percentage to keep
knowledge retention intact
Management considers
30% as the maximum limit
for attrition
Monthly
Y Data Type
Unit of
Measurement
Decimal Places
Database
Container
Existing/New
Database
If new, when
would the
database be
ready
Planned Start
date for Data
Collection
Attrition %
increasing
Discrete Percentage
Rounded off to
nearest zero
Shared Folder Existing NA 9th
August 2015
Equipment Used for
Measurement
Equipment Calibration
Info
Responsibility Training Need Operation Details
HR Tool
NA HR Manager NA
Attrition has to be vetted by
Operation Manager
Mode of Collecting Data

15. Measurement System Analysis
MEASURE
Percentage of
As the agreement is more than 70%, we can go ahead with the data.

16. Process Capability
MEASURE
Designation Attrition Count
ASSISTANT MANAGER 1
ASSOCIATE TEAM LEADER 1
EXECUTIVE 83
JUNIOR EXECUTIVE 24
SENIOR EXECUTIVE 28
TEAM LEADER 1
Total 138
Designation wise Attrition Data for last one year Since data collected is discrete, we will perform
DPMO analysis and the same is shown below
DPMO = Defects
* 1000000
# of Units* opportunities/Unit
DPMO = 138
* 1000000
500
DPMO = 2760000%

17. Analyze Deliverables
ANALYZE
MSA results of Impacting Factors
Hypothesis Summary
Checking for Impact of Xs on Y
Basic Analysis for Project Y
DCP for Potential Xs
Fishbone
Identify Potential Xs

18. Cause & Effect Diagram
ANALYZE

19. Cause & Effect Diagram
ANALYZE

20. A N A LY Z E DCP for Potential Xs
X
Operational
Definition
Employee Attitude Behavior shown by an
individual during work
hours
X
Operational
Definition
Female Attrition due to
Relocation
Any Female leaving the
organization due to
shifting to other office
location
X
Operational
Definition
Travel Time Time taken by the
employee to reach office
from his/her home.
X
Operational
Definition
Overtime
Compensation given to
employees for working
more than their scheduled
working hours
X
Operational
Definition
Better Opportunity Similar/Better job prospect
available to the employees
in other companies
X
Operational
Definition
Perks & Benefits
Compensation (Fixed and
Variable) and other
benefits (like T&L, Medical
reimbursement) provided
to employees
X
Operational
Definition
JD not clear
Job descriptions for
different positions not
detailed enough to be
understand by employees
X
Operational
Definition
Poor Food Quality Food available/provided in
office cafeteria is low
standard and unhygienic
X
Operational
Definition
Hiring Over Qualified
Resources
Employees hired have an
educational background
over and above what the
job description states

21. Basic Data Analysis for Project Y
ANALYZE
Spread Study
Spread & Central Tendency Study
Central Tendency Study
Since Data is non-normal, we will use median approach and 95% confidence interval for median is 2.0 and 95% confidence interval for StDev is
between 1.0881 and 1.2536

22. Checking for Impact of x1 (Female Attrition) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Inference: Since the Y is discrete and data for potential x is also discrete, have performed Chi-Square (I Variable) test.
P-Value > 0.05, hence X does not have a significant impact on Y.

23. Checking for Impact of x2 (Travel Time) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant impact on Y.

24. Checking for Impact of x3(Lack of Learning Opportunities & Interest in Job
on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since the Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant impact on Y.

25. Checking for Impact of x4(Poor Facilities & Food Quality) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since the Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.

26. Checking for Impact of x5(Security Concerns) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since the Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant impact on Y.

27. Checking for Impact of x6 (JD not clear) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant impact on Y.

28. Checking for Impact of x7(Hiring over Qualified Resources) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.

29. Checking for Impact of x8(Better Opportunity) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.

30. Checking for Impact of x9(Overtime) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.

31. Checking for Impact of x10(Perks & Benefits) on Y
ANALYZE
Graphical Depiction Statistical Interpretation of Relationship
Hypothesis Result
Inference: Since Y is discrete and data for potential x is continues, have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.

32. Hypothesis Summary
ANALYZE
Summary of Impacting Factors
S. No. Factor p – Value Graphical Tool Used Inference Next Steps
1 Female Attrition 0.376 Chi Square (1 Variable Test)
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant
impact on Y.
Since null hypothesis is
true, will not work on this
factor.
2 Travel Time 0.061 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant
impact on Y.
Since null hypothesis is
true, will not work on this
factor.
3
Lack of Learning
Opportunities &
Interest in Job
0.110 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant
impact on Y.
Since null hypothesis is
true, will not work on this
factor.
4
Poor Facilities &
Food Quality
0.002 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.
Since alternate hypothesis is
true, will work on this factor
further.
5 Security Concerns 0.167 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant
impact on Y.
Since null hypothesis is
true, will not work on this
factor.
6 JD not clear 0.711 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value > 0.05, hence X does not have a significant
impact on Y.
Since null hypothesis is
true, will not work on this
factor.

33. Hypothesis Summary
ANALYZE
Summary of Impacting Factors
S. No. Factor p – Value Graphical Tool Used Inference Next Steps
7
Hiring Over
Qualified
Resources
0.040 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.
Since alternate hypothesis is
true, will work on this factor
further.
8 Better Opportunity 0.012 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.
Since alternate hypothesis is
true, will work on this factor
further.
9 Overtime 0.007 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.
Since alternate hypothesis is
true, will work on this factor
further.
10 Perks & Benefits 0.029 Binary Logistic Regression
Since Y is discrete and data for potential x is continues,
have performed BLR (Binary Logistic Regression) test.
P-Value < 0.05, hence X has a significant impact on Y.
Since alternate hypothesis is
true, will work on this factor
further.

34. Improve Deliverables
IMPROVE
Improve Summary – Take Aways
Pre-Post Analysis of Factor
Pre–Post Analysis of Project Y
Basic Analysis of Improved Y
Action Plan for Improving the Factors
Screening of the Impacting Factors

35. Screening of Impacting Factors
IMPROVE
Prioritize the Impacting Factors
Perks &Benefits
Better
Opportunity
PoorFacilities &
Food Quality Overtime
Hiring Over
Qualified
Resources
0.036 0.035 0.014 0.002 0.002

36. QFD
IMPROVE
Vital X's
CTQ
Rating
(1-5)
Offer
letter
to
be
explained
to
new
Joiners
Career
Path
Roadmap
Maintenance
of
company
resources
Audit
of
Food
Supplied
Process
Management
System
Clarity
of
Payout
Clarity
of
JD
Completeness
Matrix
Perks & Benefits 3 2
0
0
0
0
0
0
6
Better Opportunity 4 0
3
1
0
0
2
2
32
Poor Fac ilities & Food
Quality 1 0
0
3
3
0
0
0
6
Overtim
e 2 0
0
0
0
3
3
0
12
Hiring Over Qualified
Resourc es 2 0
0
0
0
0
0
3
6
6 12 7 3 6 14 14
Action Items

37. FMEA
IMPROVE
2129
Risk Category Risk Impact
Severity
Cause(s) of Failure
Occurencec
Current Controls
Detection
RPN Action Plan
Responsibility (Key
Persons)
IT System Downtime /Slow Response Time
-
'-Bandwidth redundancies
'-Geographic or location constraints
Transactions left incomplete.
Timeliness SLA's affected
7
Bandw idth availability
Application maintenance schedule conflicts w ith w ork
timings
3
Testing Plan, Monitoring tools that
ensure that committed bandw idth
by provider is indeed operational.
Though needs evaluation before
Pilot start in India 3 63
As per IT Implementation and acceptance
testing plan - any post implementation issues to
be routed from Operationss to IT via published
issue escalation plan
Scheduled Maintenance / Dow n time plan to be
shared by Client in advance
Phil Rush - IT Head
Sunil Dubey - Genpact
IT Latency --Attachments don't open,
latency, image files are really slow
1. Transactions left incomplete.
2. Timeliness SLA's affected,
3. Rew ork, Inventory increases
4. Customer dissatisfaction
10
1. Inherent Citrix or application limitations
5
1. Most applications w orking w ith
existing Aon teams in India
2 100
1. A thorough testing routine to be conducted
during know ledge transfer betw een AIG &
ABC International to detect any loss of
application features and functionalities
2. Alternative connectivity
Phil Rush - IT Head
Sunil Dubey - Genpact
SLAs SLAs not met Dissatisfaction at ABC
International, End
customer/service delivery
affected
9
Client set up by ABC International gets delayed
Poor base lining or lack of sufficient historical data or
data not accurate,
Incorrect FTE Estimation,
Insufficient Volume Understanding
Systems not capable of capturing or monitoring the
important performance metrics and specifications for
the output not clearly defined
4
No historical data available - FTEs
considered as a ball park number
(based on inputs provided by
respective process ow ners
3 108
1. Identify ABC International team for client set
up completion.
2. Revisit FTE estimation and SLAspost Pilot.
3. Volumes to be done post Pilot completion.
4. Operational definition of SLAs to be validated
priot to start of pilot
Chris Simmons - ABC
International
Pranay Kumar/Ravi Ranjan - AIG
Total Risk Score

38. Action Plan for Improving the Factors
IMPROVE
S. No. Pain Area Root Cause Improvement Idea
Implementation
Owner
Implementation
Status
1 Perks & Benefits
Offer Letter not explained to
New Joiners
HR to explain the benefits
and take sign off after
explanation
HR In Progress
2 Better Opportunity Career Path Roadmap
Promotion policies to be
discussed with employees
in detail
HR/Team Manager In Progress
3
Poor Facilities & Food
Quality
Maintenance of Company
Resources
Regular audit to be
conducted for basic
facilities
Maintenance Department In Progress
4
Poor Facilities & Food
Quality
Bad Food Quality
Regular audit of food
supplied by the vendor by
Quality team
Maintenance/Quality
Assurance Department
In Progress
5 Overtime
Process Management
System
Overtime data to be
recorded in the
management system
properly
Operations Manager In Progress
6 Overtime Clarity of Payout
Overtime Commercials to
be communicated to
employees in advance
Operations Manager In Progress
7
Hiring Over Qualified
Resources
Clarity of JD
Job Description for a
particular role to be
formulated in detail in order
to hire right skill set
Operations Manager/HR In Progress

39. IMPROVE
Spread Study
Spread & Central Tendency Study
Central Tendency Study
Basic Data Analysis of Improved Y
Since Data is non-normal, we will use median approach and 95% confidence interval for median is 2.0 and 95% confidence interval for StDev is
between 1.0881 and 1.279

40. Pre – Post Analysis of Project Y
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference: Post implementation of the project, attrition level has gone down by 8%.

41. Pre – Post Analysis of Factor (Poor Facilities & Food Quality)
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference: Post Implementation, P value > 0.05, null hypothesis is true.

42. Pre – Post Analysis of Factor (Hiring over Qualified Resources)
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference: Post Implementation, P value > 0.05, null hypothesis is true.

43. Pre – Post Analysis of Factor (Better Opportunity)
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference: Post Implementation, P value > 0.05, null hypothesis is true.

44. Pre – Post Analysis of Factor (Overtime)
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference: Post Implementation, P value > 0.05, null hypothesis is true.

45. Pre – Post Analysis of Factor (Perks & Benefits)
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference: Post Implementation, P value > 0.05, null hypothesis is true.

46. Improve Summary – Take Away
IMPROVE
S. No. Factor p – Value Graphical Tool Used Inference Status
1
Poor Facilities &
Food Quality
0.987 Binary Logistic Regression
Post Implementation, P value > 0.05, null hypothesis is
true.
Solution implemented and
closed
2
Hiring Over
Qualified
Resources
0.694 Binary Logistic Regression
Post Implementation, P value > 0.05, null hypothesis is
true.
Solution implemented and
closed
3 Better Opportunity 0.738 Binary Logistic Regression
Post Implementation, P value > 0.05, null hypothesis is
true.
Solution implemented and
closed
4 Overtime 0.597 Binary Logistic Regression
Post Implementation, P value > 0.05, null hypothesis is
true.
Solution implemented and
closed
5 Perks & Benefits 0.705 Binary Logistic Regression
Post Implementation, P value > 0.05, null hypothesis is
true.
Solution implemented and
closed

47. Control Deliverables
CONTROL
Cost Benefit Analysis and Sign Off
Control Charts & Inference (for X2)
Control Charts & Inference (for X1)
Establish Process Capability
Basic Analysis of Improved Y
Control Charts & Inference for Y – Pre & Post
Time Series Study of Y – Pre & Post
Control Plan & FMEA on Control Plan

48. Control Plan & FMEA on Control Plan
CONTROL
What’s Controlled Goal/Spec Limits Control Method Who/What Measures Where Recorded
Decision Rule / Corrective
Action
SOP
SLAs not met
SLA target should be less
than 100 RPN
No historical data
available - FTEs
considered as a ball park
number (based on inputs
provided by respective
process owners
1. Identify ABC International
team for client set up
completion.
2. Revisit FTE estimation and
SLAs post Pilot.
3. Volumes to be done post
Pilot completion.
4. Operational definition of
SLAs to be validated prior to
start of pilot
Process Share Drive
Root cause analysis to
be done for FTE
estimation and SLAs not
meeting the requirement
Operational Rigor
HR-Turnover at AIG
Attrition RPN should be
less than 100
Knowledge of project is
shared between a
minimum of 3 AIG
employees - and all
findings are documented
ABC International
management to be informed of
attrition, if unavoidable -
remaining team members to
steer project and help
replacement to come up to
speed
HR Share Drive
Bench staff to be trained
and given volumes to
process to overcome the
scenario
Attrition SOP
New Hire Training
RPN should be less than
100
Training plan made and
shared with the client
Daily Assessments to be
taken and results to be
shared with Client
Training plan made and
shared with the client
Daily Assessments to be
taken and results to be shared
with Client
Training Share Drive
Re-training to be done
and performance to be
gazed in a span of 30
days to decide to
continue with the
resource
Training SOP
Low Accuracy Levels
Accuracy Level RPN
should be less than 100
1. QC process defined &
agreed with customer
2. QC scores to be shared
with customer on weekly
basis
1. QC process defined &
agreed with customer
2. QC scores to be shared with
customer on weekly basis
Quality Share Drive
If accuracy score is less
than expected level, re-
training to be done
Quality SOP
Volume Fluctuations -
AIG not able to process
the volumes received
RPN should be less than
100
1. Base lining to be done
for 9 months & results to
be reported on a monthly
basis
2. Volume Cap model is
WIP
3. Productivity levels of
the team to be aligned to
VIC
1. To ensure regular reporting
of FTE estimation
2. Volume cap model to be
signed-off with business
3. VIC model to have
Productivity as parameter
4. Cross Training & back up in
place to meet target in high
volumes
Operations Share
Drive
Volume Base lining to be
done again and agreed
with customer sign-off
Volume Base Lining
SOP

49. Control Plan & FMEA on Control Plan
CONTROL
What’s Controlled Goal/Spec Limits Control Method Who/What Measures Where Recorded
Decision Rule / Corrective
Action
SOP
Resources - AIG
Resources Unavailability
RPN should be less than
100
1. Leave planning tracker in
place
2. Cross Training plan
3. EWS
1. Ops Manager to communicate
the leave plan to Ops Dirs/SMEs
pro-actively
2. Ops Manager to ensure cross
training implementation
3. Ops Mgr to have enough back
up to handle volumes during
peaks
4. Ops Manager will ensure
back-up/bench in place
Operations Share
Drive
Defaulting employees to
be put on Performance
Improvement plan
Company Leave SOP
Workflow - Work Not
Prioritized
RPN should be less than
100
'1. All SMEs backup for
monitoring central Inbox
2. Work tracker in place
3. Supervisor access to all
Inboxes
'1. Work tracker data input
accuracy as a part of VIC
(Variable Incentive
Compensation)
2. Random Audits by
FLMs/SMEs
2. Proposed Visual Management
to be implemented for the
process
Operations Share
Drive
Automation of workflow
and appointment of
workflow Manager
Work Volume SOP
Workflow Report - Unable
to access
RPN should be less than
100
1. Ops Manager to contact
ABC International SME to
provide Workflow report
2. Ops Manager to pull up
report by the shift end
1. Ops Manager to contact ABC
International SME to provide
Workflow report
2. Ops Manager to pull up report
by the shift end
Operations Share
Drive
Level 1,2,3 to be created
to extract workflow
report
Work Volume SOP
Accuracy not Met
RPN should be less than
100
No historical data available
- FTEs considered as a ball
park number (based on
inputs provided by
respective process owners
1. Identify ABC International
team for client set up
completion.
2. Revisit FTE estimation and
SLAspost Pilot.
3. Volumes to be done post Pilot
completion.
4. Operational definition of
SLAs to be validated priot to
start of pilot
Operations Share
Drive
Three tier QC model to
be introduced to curb
accuracy issues
Process Accuracy SOP

50. Time Series Study of Y – Pre & Post
CONTROL
Pre Scenario Post Scenario
Post Implementation of this project, we are able to bring the attrition percentage down from 28% to 20% which is evident from Time series graphical
plot.

51. Control Charts & Inference for Y – Pre & Post
CONTROL
Post Implementation of this project, we are able to bring the attrition percentage down from 28% to 20% which is evident from Control Charts graphical
plot.

52. CONTROL
Spread Study
Spread & Central Tendency Study
Central Tendency Study
Basic Data Analysis of Controlled Y
Since Data is non-normal, we will use median approach and 95% confidence interval for median is 9.0 and 95% confidence interval for StDev is
between 0.6838 and 1.6390

53. Establish Process Capability
CONTROL
Process Capability – Post Implementation
Designation wise Attrition Data for last one year Since data collected is discrete, we will perform DPMO
analysis and the same is shown below:
DPMO = Defects
* 1000000
# of Units* opportunities/Unit
DPMO = 100
* 1000000
500
DPMO = 200000%
Designation Count of Designation
ASSOCIATE TEAM LEADER 1
EXECUTIVE 55
JUNIOR EXECUTIVE 20
SENIOR EXECUTIVE 23
TEAM LEADER 1
Grand Total 100

54. Cost Benefit Analysis & Sign Off
CONTROL
Benefit Source Unit Benefit Units Impacted Total Benefit
Cost Reduction $500 80 $40,000
Enhanced Revenues NA NA NA
Labor Reduction NA NA NA
Decreased Overhead NA NA NA
COPQ Reduction NA NA NA

55. Advance Innovation Group
www.advanceinnovationgroup.com
E-26, Sector 8
Noida, UP – 201301
India
Advance Innovation Group
3 continents. One team.
AIG is headquartered in Boston, Massachusetts and maintains several consulting and training delivery centers across Asia Pacific including India. Asia Pacific operations is headquartered at
Noida, India with several offices and training facilities.
Global offices allow us closer client contact to better serve your needs, while enriching our services with global perspective and experience.