Six Sigma Project for Improving Quality Score

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


5 Gender X Discrete Marital Status Record of the Employee


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


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



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



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



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



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



D M A I C

Statistically Significant X’s Relationship between Quality Scores and Process complexity



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


4 Trainer X Discrete Advisor's Trainer profiling Chi Square Cross tabulation


5 Gender X Discrete Marital Status Record of the Employee


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


8Qualification

X Discrete Highest Educational Qualification of an Employee as per HR Records



9 Process Complexity X Discrete As per process definitions Chi Square cross Tabulation


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



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



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 =.55/.91= 0.61


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