Six Sigma Project : Quality
Understanding VOCCustomer Customer Comments Customer (CTQ's)
Champion – Assistant Vice President
“The objective is to maximize the process level quality and raise the overall quality of the process without compromising on the process productivity. I expect at least 85% percent of my associate population to meet or exceed the process quality.”
Number of agents
meeting target
Process Productivity
Process Manager I am looking towards a target of 85% of my agent population meeting or exceeding the quality target. This project will lead to increasing the Quality /Efficiency/Client satisfaction
Number of Agetns meeting target
Client satisfaction
Efficiency
D M A I C
Project Charter Project Leader: Vivek kumar
Team Members
Business Case: ABC Ltd is a one of the leading recruiting & consulting company. They have back end canter in Gurgaon. One of the Acoount Payable process which is migrated in March’12 not meeting the process Quality SLA target Its been now 3 months and there is no Improvement in the process performance,
looking for Immediate actions to Improve the performance. As an outcome this will have an impact on potential business.
Stakeholders Business LeaderChampion Vice President
Sponsor Assistant Vice President
LBB Mithlesh Nautiyal
Team Member Pradeep
Problem Statement: For the period March 2012 to May 2012 the average quality score for the process was 75.43% against a target of 85%.
Currently 117 agents) out of the total population (181) are not meeting the Quality Target .
Goal Statement: To increase the median value for the overall Quality of the process from 75.4% (monthly) to at least 85% by
30th Aug’2012
Project In Scope: 1. Associates in production effective March 2012
Project Out of Scope: 1. Associates in training as on March2012
2. Any new work or queue added effective March 2012
Timelines/Phases Start Date End Date
Start date: 1st June’2012 -DEFINE 1st June’2012 6th June’2012
MEASURE 7th June’2012 10th June’2012ANALYZE 11th June’2012 12th July’2012IMPROVE 13th July’2012 30th July 2012CONTROL 1st Aug’2012 30th Aug’2012
D M A I C
ARMI and Communication PlanKey
StakeholdersARMI Worksheet
Define Measure Analyze Improve Control
Stakeholders I I I I ISponsor I I I I I
Champion I & A I & A I & A I & A I & ALBB I & R I & R I & R I & R I & R
Process Manager I & M I & M I & M I & M I & MVivek kumar R R R R R
A Approval of team decisions I.e., sponsor, business leader, MBB.R Resource to the team, one whose expertise, skills, may be needed on an ad-hoc basis.M Member of team – whose expertise will be needed on a regular basis. I Interested party, one who will need to be kept informed on direction, findings.
Communication Plan
Information Or Activity Target Audience Information Channel Who When
Project Status Leadership E-mails Pradeep BI-Weekly
Tollgate Review BB,LBB & Champion E-mails or Meetings Pradeep As per Project Plan
Project Deliverables or Activities Members Emails, Meetings Pradeep Weekly
D M A I C
CTQ Tree
Improvement in Process Quality CTQs
Percentage of Quality Parameters met by each
advisor per week.
85% weekly quality score per advisor
(Target)
85%(Lower Specification
Limit)
Weekly Audit Score less than 85%
(Defect Definition)
Weekly(Opportunity)
D M A I C
Quality improvement monthly/daily/weekly/ag
ent wise??
COPIS
D M A I C
ABC Ltd
Data Collection PlanProject-Y Operational Definition Defect Def Performance Std
Specification Limit Opportunity
LSL USL
Improvement in Process Quality
Percentage of Quality Parameters met by each advisor per week.
Weekly Audit Score less than
85% 85% weekly quality
score 85% NA Weekly
Project-Y Data Type
Data Items Needed
Formula to be used Unit
Plan to collect Data Plan to sample
What Database
will be used to record
the data?
Is this an existing
database or new?
When will the new database be ready for use?
When is the planned
start date for data
collection?
Date Range
Improvement in Process Quality Monthly target
DiscreteTotal calls
audited in a week
1 –Total Defects/Total opportunities
(No of successful audits / Total no of audits)
*100Excel Yes NA Already
startedJune’12
to Aug’12
D M A I C
Measurement of Efficiency and Effectiveness of Data
Number of Operators Data Type Sample size
2 Discrete 181
Agreement 98.78%Disagreement 1.22%
Number of Opportunities 181
Defects 117DPU 0.6022099PPM 602209.9
D M A I C
The above sample size of 181 transaction has been evaluated by 2 different evaluators and the variance between the 2 evaluators is pretty low as they both are in agreement up to a level of 98.78% which is enough to conclude that the evaluation scores are accurate and precise
Current Capability
Current sigma level of the process is 1.76
Defect Opportunities
per unitNumber of units Total number of defects Defects per unit PPM Sigma
1 181 117 60.22 602209.9 1.76
D M A I C
Stability
The above patterns suggest that the variation observed is due to "special causes” and further investigation needs to be done to ascertain the causes of trends and clusters.
The data is free from mixtures and oscillations
There is clustering and trends in the data
D M A I C
P-Value is less than 0.05
P-Value is greater than 0.05
Normality Test
Based on the above Normality test it can be said that the data is non normal as the
p value is less THAN 0.05.
D M A I C
Graphical Summary
Normality P value is < 0.005
Shape of data Non-Normal
Since the data is non-normal so the measure of central tendency will be median = 0.7543
Stability factor = Q1/Q3
Stability factor = 0.55779//0.90500 = 0.6164
On the basis of the above summary the project shall aim at shifting the central tendency and reduction in variation. Stability factor is 0.61
D M A I C
Box Plot (Shift Wise)
It has been observed that there is a high variation in Night shift employees compare to Morning & Evening shift employee. Also there is an outlier in night shift.
Target
D M A I C
Box Plot (Gender Wise)
It has been observed that there is variation in the Male and Female employees. Female employees has high performance issues.
Target
D M A I C
Spread of Data D M A I C
Organizing Potential Causes
All these factors affecting the overall quality score have been identified through brainstorming with all the stakeholders
D M A I C
Proposed tests as per the factorsD M A I C
S.No Measure Type Data Type Operational Definition Type of Test
1 Quality Score Y Discrete Percentage of Quality Parameters met by each
advisor per week
2 Team Leader X Discrete Advisor's Team leader profiling
Chi Square Cross tabulation
3 Shift X DiscreteCall scoring parameters
as defined Chi Square Cross tabulation
in the Quality form
4 Trainer X Discrete Advisor's Trainer profiling
Chi Square Cross tabulation
5 Gender X Discrete Marital Status Record of the Employee
Chi Square Cross tabulation
6 Age X Continous Employee's Age as per HR Records B.L.R Test
7Experience Type
X Discrete Experience type as per HR record Chi-sq Test
8
Qualification
X DiscreteHighest Educational Qualification of an
Employee as per HR Records
Chi Square Cross tabulation
9 Process Complexity X Discrete As per process definitions
Chi Square cross Tabulation
Statistically Significant X’s Relationship between Quality Scores and Experience Type
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 88.160, DF = 93, P-Value = 0.623Likelihood Ratio Chi-Square = 115.956, DF = 93, P-Value = 0.054
Since , P value is >0.05 , we thus conclude that There is no significant relationship between Experience Type profiling and Quality Score
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 34.324, DF = 62, P-Value = 0.998Likelihood Ratio Chi-Square = 37.841, DF = 62, P-Value = 0.993
Since , P value is >0.05 , we thus conclude that There is no significant relationship between Shift and Quality Score
Relationship between Quality Scores and Shift
D M A I C
Statistically Significant X’s
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 0.881, DF = 3, P-Value = 0.830Likelihood Ratio Chi-Square = 0.875, DF = 3, P-Value = 0.831
Since , P value is >0.05 , we thus conclude that there is no significant relationship between Quality Score and Trainer
Relationship between Quality Scores and Trainer
Since , P value is >0.05 , we thus conclude that there is no significant relationship between Quality Score and Team Leader
Relationship between Quality Scores and Team Leader
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 0.383, DF = 4, P-Value = 0.984Likelihood Ratio Chi-Square = 0.384, DF = 4, P-Value = 0.984
D M A I C
Statistically Significant X’s Relationship between Quality Scores and Age
Since, P value is > 0.05 , that means there is no significant relationship between Quality Scores and Age
Binary Logistic Regression: Met/Not Met versus Age
Link Function: Logit
Response Information
Variable Value CountMet/Not Met Good 64 (Event) Bad 117 Total 181
Logistic Regression Table Odds 95% CIPredictor Coef SE Coef Z P Ratio Lower UpperConstant -0.551695 0.818219 -0.67 0.500Age -0.0019486 0.0303466 -0.06 0.949 1.00 0.94 1.06
Log-Likelihood = -117.583Test that all slopes are zero: G = 0.004, DF = 1, P-Value = 0.949
D M A I C
Statistically Significant X’s Relationship between Quality Scores and Gender
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 6.394, DF = 1, P-Value = 0.011Likelihood Ratio Chi-Square = 6.385, DF = 1, P-Value = 0.012
Since, P value is < 0.05 , that means there is a significant relationship between Quality score and Gender
Since , P value is >0.05 , we thus conclude that there is no significant relationship between Quality score and Qualification
Relationship between Quality Scores and Qualification
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 0.177, DF = 2, P-Value = 0.915Likelihood Ratio Chi-Square = 0.179, DF = 2, P-Value = 0.914
D M A I C
Statistically Significant X’s Relationship between Quality Scores and Process complexity
Chi-Square Test (Cross Tabulation)
Pearson Chi-Square = 0.005, DF = 1, P-Value = 0.943Likelihood Ratio Chi-Square = 0.005, DF = 1, P-Value = 0.943
Since, P value is < 0.05 , that means there is a significant relationship between Quality score and Process Complexity
D M A I C
Summary of Statistical Analysis
Highlighted factors will be worked upon in the next phase
D M A I C
S.No Measure Type Data Type Operational Definition Type of Test P Value Conclusion
1 Quality Score Y Discrete Percentage of Quality Parameters met by each advisor per week
No significant relationship
2 Team Leader X Discrete Advisor's Team leader profiling Chi Square Cross tabulation
0.984 No significant relationship
3 Shift X Discrete Call scoring parameters as defined Chi Square Cross tabulation
0.993 No significant relationship
4 Trainer X Discrete Advisor's Trainer profiling Chi Square Cross tabulation
0.831 No significant relationship
5 Gender X Discrete Marital Status Record of the Employee
Chi Square Cross tabulation
0.012 Significant relationship
6 Age X Continous Employee's Age as per HR Records B.L.R Test 0.949 No significant relationship
7Experience Type
X Discrete Experience type as per HR record Chi Square Cross tabulation
0.54 No significant relationship
8Qualification
X Discrete Highest Educational Qualification of an Employee as per HR Records
Chi Square Cross tabulation
0.914 No significant relationship
9 Process Complexity X Discrete As per process definitions Chi Square cross Tabulation
0.943 No significant relationship
Summarizing Findings on Root CausesD M A I C
Cause Root cause
Process KnowledgeWhile doing the Brainstorming with the team It has been observed that there is a difference in the understanding.
Certain process concepts are not clear.
Process DocumentationIt has been observed after doing the Brainstorming that
there is no formal procedure for ongoing updation of the process query & resolutions. There is no central approach
for the same.
Quality Function DeploymentD M A I C
Action Items for Quality Function Deployment
Cause Improvement Action Responsibility
Process Knowledge
Weekly Refresher Trainings
Process TrainerWeekly Assessment Process Trainer
Use cheat sheets to roll out updates Quality AnalystBest practice sharing by top associates Team LeaderSpecific session for Bottom Performer Quality Analyst
Process Documentation
An update dismintaiton process needs to be in place so that all the queries
resoltuion can be captured at common location
Quality Analyst
Paste case documentation guidelines workstations
Team LeaderTeam huddles to include case
documentation discussion Team LeaderRoll out cheat sheets for documentation
for top issues Quality Analyst
Failure Mode Effect AnalysisD M A I C
Actionable Items Failure Mode Effect Severity (1-10)
Occurance (1-10)
Detection (1-10)
RPN (Risk Priority No.)
RMS (Risk Management
Strategy)RTP (Risk
Treatment Plan) Responsibility Start Date End Date
Hiring Criteria Non Availability of concern resource
Hiring not having the desired benefits 9 9 3 243 Reduce
Different applicants short
listedRecruitment Team 1/7/2012 1/8/2012
Applied candidates not meeting the requirements Hiring delay 8 8 3 192 Reduce Speeding the
hiring the process Recruitment Team 1/7/2012 1/8/2012
Interview panel not aware of requirement
Not getting the correct resource 9 4 2 72 Transfer
Creating the awarness by Knowledge
TransferRecruitment Team 1/7/2012 1/8/2012
Reward on experience Retainment issue No resource to deliver 9 3 2 54 ReduceCreating awarness
on experience program
Talent acq. Team 1/7/2012 15/07/2012
Non satisified Experienced staffs Motive not fullfilled 9 5 1 45 Reduce Creating the
Motivated StaffsHR-Operations
Team 1/7/2012 15/07/2012
Wrong data base Wrong resource on Stage 9 2 1 18 Reduce Updating the
existing Data Database Team 1/7/2012 15/07/2012
Experience Sharing Session Retainment issue No resource to deliver 9 3 1 27 ReduceCreating awarness
on experience Sharing program
HR-Operations Team 10/7/2012 28/07/2012
Wrong data base Wrong resource on Stage 7 5 2 70 Reduce
Creating awarness on experience
Sharing programHR-Operations
Team 10/7/2012 28/07/2012
Update tracking processAll updates does not come to team Manager and difficult to
keep a track from each individual
Process steps can be missed 5 6 1 30 Reduce
Creating awarness on experience
Sharing programHR-Operations
Team 10/7/2012 20/7/2012
Lack of knowledge amogts the team
Impact on team performance (Quality of
work)6 7 1 42 Reduce
Creating awarness on experience
Sharing programHR-Operations
Team 10/7/2012 20/7/2012
Development Plan for communication Awarness missing on Plan
Not getting the information on
resources7 5 1 35 Reduce Creating awarness
on plan Recruitment Team 10/7/2012 30/07/2012
New Mode of communication No idea of new mode 7 2 3 42 Reduce Getting trained Recruitment Team 10/7/2012 30/07/2012
Local Hiring No trained localist Delay in hiring 7 2 1 14 Reduce Revisit Screening process Recruitment Team 10/7/2012 30/07/2012
Out stationed candidates turned up as localist failure of objective 8 3 2 48 Reduce Revisit Screening
process Recruitment Team 10/7/2012 30/07/2012
Pilot Run Snapshot D M A I C
• We decided to conduct 50 random audits in the team.
• Quality Analyst has conducted 20 quality checks each per Team leader (Stratified Sampling)
• 1 transaction was monitored per week for each associate
• Audits were conducted for both top and bottom performers (Based on Historical Data)
• 2 different quality analyst have given the audit task to gauge the variance as well.
• Quality monitoring has been done for 5 days and the daily findings were reported to the Quality Manager.
Observations before and while conducting the pilot tests for the sample population
• There is a lack of process knowledge• Majority of the agents were not aware of different SLAs in the process.• Timely feedback post any audit has was not being delivered • A centralized update sharing process is not in place.• Work allocation mechanism need to improve which will help the agent to have optimize work load.
Pilot Observation D M A I C
Below is the comparison of Pilot run results and Historical data which clearly evident that both variation and of outliers has significantly gone down after the Implementation of Improvement
plan.
Pilot Observation D M A I C
Based on the chart given below it is concluded that the action items implemented have significantly benefited the sample population as the number of defects has considerably decreased and the population under ‘Good’ is higher than ‘Bad’
Findings • Team is now up-to-date with current process updates & query resolutions.• There is no repetition of any of the top errors which was noted previously.• Centralized process is established to capture the ongoing updates for the proce
Box Plot Comparison (Shift wise)
The above box plot depicts the Shift wise improvement in Quality Scores
D M A I C
Box Plot Comparison (Gender Wise)
Improvement can also be seen amongst both male and female employees
D M A I C
Graphical Summary
Stability factor = Q1/Q3
Stability factor =.55/.91= 0.61
Stability factor = Q1/Q3
Stability factor = 88/91 = 0.967
The above graphical summary clearly shows the shift of median as the median has now exceeded the target of 85% whereas previously median was below target.
D M A I C
Box Plot Comparison (Overall Quality)
Finally we can see that the overall Quality of the process has increased from an average of 70.83% to an improved figure of 90.50%
D M A I C
Stability for Quality
It has been observed there is improvement in the process stability
D M A I C
D M A I CCONTROL PLAN
Process Manager is responsible to update and publish the control plan to the Sr Manager and all interested parties.
Actionable Items Periodicity Owner Status
Reasons (If Not Done) Impact
RnR - SPOT award intiated for the top performer of the week Weekly Team Leader
Quality score weightage has been increased for agent's
monthly performanceMonthly/Ongoing Sr Manager and Manager
Process knowledge test will be conducted on monthly basis
Monthly/Ongoing Trainer & Team Leader Test scores will be a criteria for monthly performance
Test scores will be a criteria for monthly performance Monthly/Ongoing Quality Evaluator & Team
Leader
Callibration process has been intiated between client and
Team Leader. This will ensure the adequate process
understanding
Monthly/Ongoing Team Leader & Client team
Menter - Mantiee programme has been established for
Bottom performer and new joinees
Ongoing Quality Evaluator & TL
Capability Analysis (Post implementation)
Sigma level of the process is now at 2.82
Defect Opportunities per
unitNumber of units Total number of
defects Defects per unit PPM Sigma
1 181 17 0.9392265 93922.65 2.82
D M A I C
Thanks