wipro bpo case study

2009 Wipro Ltd - Confidential

Improving Overall Claim Handling

Accuracy

Methodology : DMAIC - Services

WIPRO Ltd (BPO Division)

Green Belt : Ashish Dubey

Team Members:

Hitesh Mhatre, Amit Vaikunthe, Mangesh Pokale,

Anoob Varghese, Vijaykumar Gupta, Praveen

Nayyar, Sanket Mungekar, Anwar Shaikh

Black Belt : Pradeep Khabrani

Champion : Amrish Sharma

LBB : Sunanda Sharma

MBB : Devender Malhotra


Lean and Six Sigma Expertise

Wipro BPO

Wipro Limited

Company Overview

Wipro Ltd has 4,600+ employees trained in Six Sigma & 2600+ trained in Lean.

Other methodologies followed are Kaizen workouts, Continuous improvements,

Transformations, 350 + certified black belts, 21 Lead Black Belts, 18 Master black belts,

410 Certified Lean Facilitators and few certified Kaizen facilitators. We have successfully closed

5800+ six sigma projects through any of the three methodologies such as Improving Existing Process (DMAIC),

Developing Six Sigma Software (DFSS), and Designing Six Sigma Process or Product (DSSP). This has given us an

overall financial savings of INR 15.77 Billion Savings.

The BPO division of Wipro Technologies is one of the largest BPO service providers on a global delivery platform.

Wipro BPO provides a broad spectrum of services across CRM, Back Office Transaction Processing - Finance &

Accounting, Procurement & Supply Chain, Legal Process Outsourcing, Human Resources Outsourcing &

Knowledge Services - Industry Specific Solutions and BPO Consulting. With annual revenue of USD 395 Million (FY

2009-10), Wipro BPO caters to 75 clients (FY 2009-10) across various geographies and industries with 26 state-

of-the-art delivery centers spread across 11 countries. Wipro BPO is equipped to offer services on a 24 x 7 x365

basis.

Incorporated in 1945, Wipro Limited (NYSE: WIT) is a diversified US$ 5 Billion (FY

2009-10) conglomerate with approximately US$ 19 billion in market capitalization.

Wipro is known for its strong relationship, client commitment and the value added

to clients. Major group companies include Wipro Technologies, Wipro

2009 Wipro Ltd - Confidential3 2009 Wipro Ltd - Confidential3 3

Process Overview & Funneling approach

3

Claim filed by

Provider

RMO Scans the

claim into

digital format

Auto-

Adjudicator

processes the

claim

Manual Claim

processing &

Adjudication

Provider

appeals/

disputes

Contact Center

resolves the query or

routes for

reconsideration

Fallouts

Y

N

Year End

audit

conducted

by external

3rd party

auditors

Low Productivity

$29,226 in last 3 months

Mis-payment of Claims

$539,585 in last 3 months

Re-processing or Re-Adjudication

22% of rework due to processor errors

Penalty charged by end-

customers to our client

Penalty data unavailable

TAT for claim Payment

$6452 in last 6 months

Revenue leakage for

the client

Our client is one of the Largest Health care insurance provider in US serving more than 50 million Customers.

Our Client has healthcare contracts with top companies like Boeing, Xerox, HP, etc. for their employees.

Some premium (performance guaranteed) customers have penalty based contracts with our client basis service

catered to them.

Wipro commits 5% year on year revenue benefit to premium clients by means of productivity improvement or

business process optimization

Prioritized basis

revenue impact to

the client

(contributes to 93%

of the overall

leakage)

Process Overview

2009 Wipro Ltd - Confidential4 2009 Wipro Ltd - Confidential4

Business Case:

Mis-payment of claims has a maximum impact on the client bottom-line in the form of penalties, incorrect payouts and rework/re-adjudication of claims

Partnering with the client in an initiative to optimize their business will further enable Wipro to maintain a long term relationship and emerge as a service partner instead of a vendor.

Problem Statement:

The average Overall Claim Handling Accuracy for the last 15 weeks is 96.87% which has resulted in a financial loss of $616,705 to the client (15 weeks).

Pre - Project remediation implemented :

(8 weeks before project initiation):

Refresher sessions conducted for top error codes

Weekly SME and QA calibration sessions

Client update dissemination process standardized

Automated update dissemination and signoff mechanism

Increased internal audits for Fatal errors

However the implementation failed to show improvement

Goal Statement

The intent of doing this project is to improve the claim handling accuracy to 98% (statistically validated) post identification of root causes and hence reduce the financial impact to the client.

Project Benefits:

Reduction in financial impact to the client

Establish a gain share model with the client

Stay ahead of the competition

Reduction in factors of inaccuracy and revenue leakage

Business Case and VOC


Scoping

Longitudinal:

Start - Processor picks a Claim for

processing.

End Processor finalizes the claim

(Closes/Denies/Pays/Routes)

Lateral:

In Scope:

All processors / All locations

Outside Scope:

Training / Data Entry / Voice business

CTQ Drill Down

Define 19-Jun-09

Measure 10-Jul-09

Analyze 11-Sept-09

Improve 30-Sept-09

Control 5-Nov-09

Project Milestones


CTQ CharacteristicsA I CD

Overall Claim Handling Accuracy (OAR) is defined as Percentage of Claim without any Financial or Procedural

error to the total claims audited.

Overall Accuracy = Total Claims audited Errors identified

Total Claims Audited

Any processed Claim with any amount of Financial error or Procedural Error is a defect.

Data Collection Plan:

CTQ MeasureType of Data

Discrete/ContTarget Upper spec Lower spec

Describe your

measure

Overall Claim

Handling

Accuracy

Ratio of Claims without

any error to the total

claims audited

%Continuous 98.0% 98.0%-

Data Required Data Source & Location Sample Size Responsibility

Agent and Team Level Data Weekly External Data Last 15 weeks of Data by Client Vinay Kumar

Error Codes / Reasons External Audit Team 15 Weeks data Ashish D

Agent and Team Level Data Weekly Internal Data Last 15 weeks of Data Bipin Saxena

Productivity Data Production reports 15 weeks data MIS

Claims processed details Client CRM Dump 15 weeks data Sumeet Patil

Claim Skip Data Production Files 15 weeks of Data MIS

Date of Joining and alignment HR Dump and Converter 15 weeks of Data MIS

Defects/Errors data Int. and Ext. Error Trending 15 weeks of Data Vijay Gupta

Operational Definition of Project CTQ:

Defect Definition:

100% External Claims

audited for 15 weeks

Data Collection Plan:


Validating Measurement System

Month

Audit

_QA

Erro

r_QA

Audit_

Client

Error_

Client

Audit

Delta

Error

Delta

Feb-09 4009 191 3962 188 47 3

Mar-09 5377 167 5334 169 43 2

Apr-09 4864 156 4826 156 38 0

May-09 4925 134 4921 135 4 1

Jun-09 6370 209 6364 211 6 2

5 steps of extracting reports:1. Select the Duration

2. Select the Site

3. Select report type

4. Select level of report

5. Select expected report fields

Root Cause for Reporting issues identified:

Certain Policy samples do not get reported when pulling the report through the Individual processor measures (Report level)

Calibration was attained with the client MIS and the methodology adopted by them was rectified with a retrospective effect.

QA_LeadClient_MIS

100

80

60

40

20

0

Appraiser

Pe

rce

nt

95.0% C I

Percent

QA_LeadClient_MIS

100

80

60

40

20

0

Appraiser

Pe

rce

nt

95.0% C I

Percent

Date of study: 8th July 2009

Reported by: Ashish Dubey

Name of product: External Accuracy Reporting

Misc: Check for reporting Variance

Assessment Agreement

Within Appraisers Appraiser vs Standard

1. Discrepancy identified in reporting by client MIS and Wipro from

the same audit tool

2. The steps for report extraction were mapped and a attribute

assessment analysis was conducted

3. The agreement rate between both parties showed as 20%

4. A calibration call was scheduled with the business team of the

client to re-map the steps for report extraction

5. Basis the client business team inputs, it was identified that the

appraiser versus the True value was 20% for client MIS team

Conclusion: Quality reporting to be done using the Detailed error

trending report instead of Individual processor measures

The Delta in Audit # is eliminated Rectified the data retrospect with the client and

corrected data considered for baselining and analysis

Weekly reporting to evaluate any discrepancies Any duplicate transactions audited, must be removed

Highlight during monthly business reviews

A I CD


0.9800.9750.9700.9650.9600.955

LSL

LSL 0.98

Target *

USL *

Sample Mean 0.968674

Sample N 15

StDev (Within) 0.00593917

StDev (O v erall) 0.00654083

Process Data

C p *

C PL -0.64

C PU *

C pk -0.64

Pp *

PPL -0.58

PPU *

Ppk -0.58

C pm *

O v erall C apability

Potential (Within) C apability

PPM < LSL 1000000.00

PPM > USL *

PPM Total 1000000.00

O bserv ed Performance

PPM < LSL 971734.91

PPM > USL *

PPM Total 971734.91

Exp. Within Performance

PPM < LSL 958320.28

PPM > USL *

PPM Total 958320.28

Exp. O v erall Performance

Within

Overall

Capability Analysis of Overall Claim Handling Accuracy Continuous Data: Normality Plot

As p > 0.05, the data is normal

Continuous Data: Run chart and Control Chart

Data was found to be random, As p > 0.05 for Trends, Oscillations, Mixtures and Clustering

Control chart was plotted, which shows that the data is in control

Capability Analysis was conducted

Zst = -0.24

Mean = 96.86%

151413121110987654321

0.99

0.98

0.97

0.96

0.95

Weeks

Ov

era

ll C

laim

Ha

nd

ling

Accu

racy

_X=0.96867

UCL=0.98649

LCL=0.95086

I Chart of Overall Claim Handling Accuracy

Process Baseline and Target ValidationM I CD

Baseline process sigma

multiple is -0.24

Z st = 1.50 + (3 * P pk) = -0.24

Data Analysis Normality Test and stability check:

Test of mu = 0.98 vs < 0.98

95% Upper

Variable N Mean StDev SE Mean Bound T P

OAR 15 0.96867 0.00654 0.00169 0.97165 -6.71 0.000

Improvement Target:

0.9850.9800.9750.9700.9650.9600.9550.950

99

95

90

80

70

60

50

40

30

20

10

5

1

OAR

Pe

rce

nt

Mean 0.9687

StDev 0.006541

N 15

AD 0.260

P-Value 0.660

Probability Plot of OARNormal

0.9800.9750.9700.9650.9600.955

X_

Ho

OAR

Boxplot of Overall Claim handling Accuracy(with Ho and 95% t-confidence interval for the mean)

Accuracy target of 98% is

statistically significant

( p-value < 0.05)


Prioritization of Causes

Focus groups and Brainstorming activities conducted across locations to generate

possible causes impacting Accuracy

Potential Factors identified from Cause & Effect (Fishbone) Diagram

Brainstorming &

Fishbone:

Multi-Voting (Criterion based):

M I CD


Quick-win Implemented

Sr Description of X Quickwins Implemented Impact Status

1

Overlooking

Information by

employees

Devised 5 golden rules which will be mandatory on all claims Mandatory Checks Closed

Andon - 5 Golden Rules of claims adjudication were formulated and

rolled on the floor with fliers put across on every agents workstationReady Reckoners Onging

2

Lack of RnR and

Fun activities /

Only

reprimanding

and no

appreciation

Initiated Monthly and Quarterly RnR along with awards like Microwave,

Mp3 players, camera, TV, LCD, DVD Player, etc

Awards and

RecognitionOngoing

Procured a Foosball table and initiated Championship across teams to

promote fun activitiesFun Element Closed

Initiated Fun Fridays which includes fun events like Bingo, Volleyball

and cricketFun Element Ongoing

3

Miscommunicati

on of OPH

Targets / Sups

unsure of daily

tasks and KRAs

Standard template for Daily Production Review (DPR Format)

implemented, which is used to review agents by supervisors

Awareness of

performanceOngoing

Daily review of supervisors using the DPR by Floor ManagersEnabling

accountabilityOngoing

DPR includes actual performance along with targets and highlights KRAs Signoffs enables focus Ongoing

4

Time wasted

due to Idling /

Supervisors

Refrain from

laborious tasks

Daily review of supervisors on team metrics like absenteeism,

Utilization, Occupancy (DPR includes)

Awareness of

performanceOngoing

Andons

Implemented

M I CD


X.No. Prioritized Xs Description Tool Used Validated

X1 Misinterpretation of policies and

instructions

Literature issues with client documents and P&P resulting

in Misinterpretation of policies and instructions.

Pareto and Pie Chart

Analysis of Prioritized Causes M I CD

Cnt 32 30 28 18 18 17 16 2369 61 57 49 45 44 44 39

Percent 5 5 5 3 3 3 3 412 10 10 8 8 7 7 7

Cum % 75 80 84 87 91 93 96 10012 22 32 40 48 55 63 69

Error Category

Oth

er

Pro

vid

er B

ene

fits In

terp

reta

tion

Keyi

ng /

Typo

Errors

Pena

lty E

val

uatio

n / A

ppl ic

atio

n

Nur

ses R

evie

w

Cal

cul

atio

n Error

Pro

vid

er S

elect

ion

SPI

Inte

rpre

tati o

n

Rem

ark

Cod

e

Pro

vid

er Con

trac

t Cal

cula

tion

Pay

ment

Err

or

Dup

licat

e Serv

ices

Han

dlin

g

Adj

us t

ment

Err

or

Co-

ord

ina t

ion

of B

ene f

it

Addi

tiona

l Info

rmat

ion

Han

dlin

g

Mem

ber Bene

fits In

terp

reta

tion

600

500

400

300

200

100

0

100

80

60

40

20

0

De

fects

Pe

rce

nt

Pareto Chart of Error Category


X2 Tenure in Complex Queue Vintage in the process impacts the ability to gain

accuracy in claim processing

Moods Median and Test

of Equal Variance

Defects due to - Member / Provider Benefit

Interpretation

- Calculation

- SPI Interpretation

Are due to misinterpretation

of policies and instructions,

which contribute to 67% of

overall defects

Analysis shows that Claim

Handling accuracy across

various tenure groups does

not differ statistically

Test of Equal Variance shows

that variation in performance

across Tenure groups is

insignificant



X3 Handling Difficult and Dedicated

Policies

Dedicated policies and specific work types result in

higher defects

2 Sample t test



X4

X9

Lack of productive count for routes

Productivity Pressure results in

accuracy defects

Routes require research and time for reviewing the

claim, however if routed, reaps no productive count for

the processors resulting in production pressure for

meeting targets and hence rush resulting in accuracy hit

Best Subset

Regression and

Correlation

Policy Type has a statistically significant impact on Claim handling

accuracy, however Dedicated policies depict better performance

as compared to Non Dedicated policies.

Hence it can be statistically stated that dedicated

policies have no negative impact on Overall Claim

Handling Accuracy

Overall CPH and Claims

Handled have an impact on

Accuracy, however R-Sq (Adj)

is very low

Correlation Analysis shows

that Routes, CPH and Claims

Handled (Productivity

Pressure) has no Correlation

with Overall Accuracy.



X5 High Dollar Claims and aged ORS Claims which are aged in the system or bear a

high Paid or Billed amount result in more defects

Anova, Regression, Test of equal

Variance & Chi Square


Billed Charges and Paid Charges have

a significant impact on Sample Size of

External Audits

Claims with Paid amount < $2500 has

observed defect count greater than

expected defect counts

Chi-Square value for $10000


X6 Focus on Payment Accuracy only Financial accuracy is critical to process and a Penalty

metric in SOW. Process focus stays on Financial

accuracy only, however procedural errors also are

included in Claim Handling accuracy

One way Anova, Box

Plot and Pareto

63% of the errors are due to

financial defects, it will be incorrect

to forego the procedural errors in

totality

Top 5 Procedural Error Codes

identified for improvement, which

contribute to 51% defects:

K002, P001, F009, K001, B004




X7 Calculation Errors while paying

claims

SMEs expressed that agents commit calculation mistakes resulting in overpayment or

underpayment

Correlation and Multiple Linear

Regression

Multiple Regression helped in identifying

that Underpayment, Over payment and

Mispaid amount shows no correlation with

Overall Claim Handling Accuracy and

Financial Accuracy

P>0.05, No Correlation of overall

accuracy or Financial accuracy

observed with Over, Under or Mis

Payment


X8 Incorrect Markdown by Sam Auditors External Errors charged by auditors include cases which

are incorrectly marked as an error affecting process

scores

2 Sample t test and

Pie chart

Post Reburtal_Pre Reburtal

0.98

0.97

0.96

0.95

0.94

0.93

0.92

State

Cla

ims

Ha

nd

lin

g A

cc

ura

cy

Pre and Post Reburtal Comparison Incorrect markdown has an average

impact of 1.4%.

Basis analysis, 46% of the

challenged errors are approved and

reversed by the external SAM

Team.

Suggested Ops team to identify a

SPOC to validate errors charged on

a daily basis



X10 Assumption based processing Agents tend to assume steps while processing claims,

which may be erroneous

Correlation and

Scatter plot



X11 SME will handle Please Audits and attitude to relinquish owners of the

claim

Agents classify claims under doubt and pass it over to

SME for audits. Post classifying, agents relinquish

ownership and do not track the claim status

Impact Analysis and Pie

Chart

% errors (Focus / Knowledge) were

correlated to Overall Claim

handling accuracy and analysis

shows that there exists a strong

negative correlation between Claim

handling Accuracy and Errors due

to focus issues

21% of the Please audits were

found to be erroneous

Analysis shows that the impact of

the Please audit sample on

Overall Claim Handling accuracy

is negligible

4.00%3.50%3.00%2.50%2.00%1.50%1.00%0.50%

0.98

0.97

0.96

0.95

0.94

Focus Issues

Ov

era

ll C

laim

ha

nd

ling

Accu

racy

Scatterplot of Claim Handling Accuracy vs Focus Issues


X13 SME Busy with mails or meetings Agents highlighted that SME stay busy handling mails

or attending meetings and this results in deficiency in

support

Correlation and

Regression

Correlation shows that

Accuracy and AHT are not

correlated. Hence it can be

validated that though timely

support is necessary, its

impact on Accuracy is

insignificant


X.No Prioritized Xs Description Tool Used Validated

X1 Misinterpretation of policies and

instructions

Literature issues with client documents and P&P resulting in

Misinterpretation of policies and instructions.

Pareto and Pie Chart

X2 Tenure in Complex Queue Vintage in the process impacts the ability to gain accuracy in claim

processing

Moods Median and Test of

Equal Variance

X3 Handling Difficult and Dedicated

Policies

Dedicated policies and specific work types result in higher defects 2 Sample t test

X4 Lack of productive count for routes Routes require research and time for reviewing the claim, however if

routed, reaps no productive count for the processors resulting in

production pressure for meeting targets and hence rush resulting in

accuracy hit

Best Subset Regression and

Correlation

X5 High Dollar Claims and aged ORS Claims which are aged in the system or bear a high Paid or Billed amount

result in more defects

Anova, Regression, Test of

equal Variance & Chi Square

X6 Focus on Payment Accuracy only Financial accuracy is critical to process and a Penalty metric in SOW.

Process focus stays on Financial accuracy only, however procedural errors

also are included in Claim Handling accuracy

One way Anova, Box Plot

and Pareto

X7 Calculation Errors while paying

claims

SMEs expressed that agents commit calculation mistakes resulting in overpayment or underpayment

Correlation and Multiple

Linear Regression

X8 Incorrect Markdown by Sam Auditors External Errors charged by auditors include cases which are incorrectly

marked as an error affecting process scores

2 Sample t test and Pie

chart

X9 Productivity Pressure results in

accuracy defects

Agents complained that the rush for production resulted in defects Best Subset Regression and

Correlation

X10 Assumption based processing Agents tend to assume steps while processing claims, which may be

erroneous

Correlation and Scatter

plot

X11 SME will handle Please Audits and attitude to relinquish owners of the

claim

Agents classify claims under doubt and pass it over to SME for audits. Post

classifying, agents relinquish ownership and do not track the claim status

Impact Analysis and Pie

Chart

X12 Only Threats and no Support by

supervisors

Agents highlighted that supervisors threaten to place on action plan and

PCCP rather than assisting in improving

Champions Input

X13 SME Busy with mails or meetings Agents highlighted that SME stay busy handling mails or attending

meetings and this results in deficiency in support

Correlation and Regression

Validated Causes M A I CD


Counter Measures for Validated Xs

X Description of X Counter Measure Owner Status

X1

Misinterpretation

of policies and

instructions

Extract all possible SPI and formulate a module for Vitality Sanket M Closed

Include Commonly used Jargons in the SPI Vitality Training Module Anoob V Closed

Implement Lean - Standard Work Tenet Mangesh P Closed

X2Aged ORS and High

dollar claims

Redefine Internal sampling methodology and devise a real time audit

mechanismAshish D Closed

All claims with paid charge of over $10,000 to be prioritized for audit Ashish Dubey Closed

Implement Gemba boards to control aged inventory Anwar Shaikh Closed

Sampling plan to be revised for BL certified processors (PR Samples) Ashish Dubey Closed

X3Focus on payment

accuracy

Root cause sheet to be initiated wherein agents accept their errors and

individually identify the root cause of the error made, post detailed

feedback

Pravin Nayyar Closed

Daily Agenda in Shift end huddles to include floor level error trends

irrespective of its type

Amit

VaikuntheClosed

Special Audits to be initiated with higher focus on claims other than Paid Vijay Gupta Closed

X4

Incorrect

markdown by SAM

auditors

SPOC from QA Team identified, who must review all errors on a daily basis

and challenge discrepant errors charged by SAMHitesh Mhatre Closed

Incorrect markdowns to be highlighted in client visit and Quarterly

business review

Amrish

SharmaClosed

X5Assumption of

processing steps

Outlier team identified and aligned with a dedicated auditor for additional

focus

Mangesh

PokaleOngoing

Develop possible automated scripts which can eliminate manual

intervention and automate calculations and proceduresVijay Gupta Closed

100% audits for OJT agents, by a dedicated OJT Auditor Ashish Dubey Ongoing

M A CD


Action Plan for X1: Standard Work (Lean Tenet)

Challenges Actions and Benefits

Interpreting the benefits correctly for varying claim types

Inventory piling up in case of difficulty in understanding claim types

Performance Guaranteed Policies

The work allocation process was modified A dedicated personnel for work allocation Allocation rules defined basis

Policy TypeSkill Level of processor

Segmentation conducted on the floor dividing processor groups as

Performance Guaranteed processorsDedicated and High Dollar processors

CCOD Router

Medicare Processor

High Dollar Processor

Hospital Claims

processor

Performance

Guaranteed -

Dedicated processors

Simple Claims

Medicare / Medicaid

High Dollar

Dedicated Policy

Mixed

Specialty Processor

M A CD


Action Plan for X1: Misinterpretation of policies and Contracts

Claim Processors Special Processing

Instructions

Claim Payment

Claim

OFE

Gap - Technical jargon (Logical)

SME intervention enables better interpretation of SPI

SPI Defects Actions and Benefits

Interpreting technical language

Interpreting logical and conditional expressions (Greater than or, if then else, if then else if, etc)

Understanding English vocabulary during processing and trainings

Module devised and delivered which included Jobaids for understanding medical language Jargons and logical expressions (direct & multi

level)

Trainings delivered in local language thus easing out understanding of logical and conditional jargons and hence improving documentation

Research

Claim Flow:

M A CD


Action Plan for X2: Revised Sampling Approach

Claim processor Claim processed Auditor

Real Time

SamplingClaim Filters Claim

Java / ASP based tool deployed to capturing productivity real time

Dump extracted every 2 hours for all claims processed

A sampling plan put in place to identify possible errors

Normal Audits @ 10/week per agent

Special audits to enable hygiene

Macros run to flag off identified opportunities

Real time audits conducted

Financial errors identified rectified with void process

Daily Agenda to cover all errors identified

Automation for Real Time Audits:

Revised Sampling:

M A CD


Action Plan for X2: Gemba (Lean Tenet)

Gemba means workplace and is one of the most commonly used tool in manufacturing industry

Gemba boards have been implemented to enable review and estimation of the floor performance for the day/Week/Month on critical standards at a glance

Gemba boards include Inventory volumes, Age of ORS on a team level and bi-hourly productivity along with floor level updates and critical metrics

All Gemba boards are updated by floor supervisors for their team and business manager for the location happens every 2 hours

This is an important review tool across middle and senior leadership compounded with Omega Real Time Manager

M A CD


Action Plan for X2 and X3:Automation (Omega)


Delay of 24 hours for data availability

All monitoring are retrospective and not proactive

Errors are identified post 24 hours and option available to minimize impact to client

Lack of monitoring tools like Avaya, IEX as in a voice process

A tool was internally developed to cater the following Real time productivity capture Real-time performance monitoring on supervisor

console (productivity, utilization, quality) Real time sampling of claims, basis sampling

plan for each segment of processors Real-time feedback/coaching on performance Ability to rectify any defects identified

internally before they hit client system

M A CD


Action Plan for X5:Mistake Proofing


Assumption of steps and contract related information

Due to rush for productivity targets, processors tend to consider calculations which may not be correct

Employees tend to miss out critical information that needs to be extracted from claim policy document

List of defects was reviewed to identify possible opportunities of automation

VB Scripts were internally developed and deployed to mistake proof possible mistakes Processors need to answer the series of

questions The tool will automatically calculate and

populate required processing fields to eliminate manual intervention

M A CD


Action Plan for X5:Variation based Team Mix

Agent controllable defects Team Level

Action Plan Description Status

All teams were analyzed for variationThe floor allocation was restructured considering variation based Team

mixClosed

Outlier managementThe bottom 25 agents on accuracy were identified and assigned to 1

QAOngoing

QA Accountability

The KRAs for the QA included turnaround of these set of agents

including reduction in financial and procedural errors

The agent set revised every quarter

Ongoing

Module design and deliveryVitality training conducted by the QA team instead of trainers and SME

to eliminate calibration gapsOngoing

SAM Error Review

SAM Review SOP formed and SPOC identified

Review of all errors conducted and discussed in a weekly client call for

reburtals

Ongoing

UMESH KOYANDE

SWAROOP SHIVRAM

SANDRA JOSEPH

NITESH MISHRA

MIRZA MUEEN A.W

MANOJKUMAR KODWANI

LOKESH SHETTY

KUMARSEN S MUDLIAR

JISHA JOHNY

GAURAV BORDE

DONALD SETHI

DICKSON D'COSTA

ASHISH DUBEY

ANITHA G NAIR

ANAND SHINTRE

AMOS CHRISTIAN

AMIT OBEROI

ALISTAIR ANTHONY GOUVEIA

AGITH CHERIAN

0.50.40.30.20.10.0

Su

pe

rvis

or

95% Bonferroni Confidence Intervals for StDevs

Test Statistic 222.37

P-Value 0.000

Test Statistic 0.92

P-Value 0.552

Bartlett's Test

Levene's Test

Test for Equal Variances for Claim Handling Accuracy

Cnt 32 30 28 18 18 17 16 2369 61 57 49 45 44 44 39

Percent 5 5 5 3 3 3 3 412 10 10 8 8 7 7 7

Cum % 75 80 84 87 91 93 96 10012 22 32 40 48 55 63 69

Error Category

Oth

er

Pro

vid

er B

ene

fits In

terp

reta

tion

Keyi

ng /

Typo

Errors

Pena

lty E

val

uatio

n / A

ppl ic

atio

n

Nur

ses R

evie

w

Cal

cul

atio

n Error

Pro

vid

er S

elect

ion

SPI

Inte

rpre

tati o

n

Rem

ark

Cod

e

Pro

vid

er Con

trac

t Cal

cula

tion

Pay

ment

Err

or

Dup

licat

e Serv

ices

Han

dlin

g

Adj

us t

ment

Err

or

Co-

ord

ina t

ion

of B

ene f

it

Addi

tiona

l Info

rmat

ion

Han

dlin

g

Mem

ber Bene

fits In

terp

reta

tion

600

500

400

300

200

100

0

100

80

60

40

20

0

De

fects

Pe

rce

nt

Pareto Chart of Error Category

M A CD


FMEA on Improved ProcessM A ID

1. FMEA was conducted post implementation of action plans

2. The prioritized failure modes were actioned and revised process map was implemented


New Process Performance

Process Sigma has increased from -0.24 to 2.42

0.9950.9900.9850.9800.975

LSL

LSL 0.98

Target *

USL *

Sample Mean 0.984882

Sample N 13

StDev (Within) 0.0048851

StDev (O v erall) 0.00529751

Process Data

C p *

C PL 0.33

C PU *

C pk 0.33

Pp *

PPL 0.31

PPU *

Ppk 0.31

C pm *

O v erall C apability

Potential (Within) C apability

PPM < LSL 153846.15

PPM > USL *

PPM Total 153846.15

O bserv ed Performance

PPM < LSL 158792.09

PPM > USL *

PPM Total 158792.09

Exp. Within Performance

PPM < LSL 178361.31

PPM > USL *

PPM Total 178361.31

Exp. O v erall Performance

Within

Overall

Capability Analysis - Improved Process

Z Calculation for Continuous Data

Phase LSL Mean SLT

ZLT

ZST

DPMO Yield

Baseline 98.00% 96.87% 0.65% -1.74 -0.24 958935.24 4.11%

Improved process 98.00% 98.49% 0.53% 0.92 2.42 177605.66 82.24%

Overall Accuracy:

M A ID


Validation of Improvements

P-value = 0.00 Improvement in Overall

Accuracy is statistically

significant

Mean has increased from 96.87% to 98.49%

Improvement sustained for more than 6 months (Oct

2009 to April 2010)

Post_Pre

1.00

0.99

0.98

0.97

0.96

0.95

Status

Ov

era

ll C

laim

Ha

nd

ling

Accu

racy

Boxplot of Accuracy Pre and Accuracy Post

M A ID


Control Plan

Sr.

NoSustenance Measures Frequency Responsibility

1 Daily Reporting of MTD and Weekly Scores Daily Vinay Kumar

2Implementation of mandatory steps in Omega as a ready reckoner (Golden

Rules)Weekly Ashish Dubey

3 Daily Team Agenda and Shift End huddle for update dissemination. Daily Mangesh Pokale

4 Monthly Dipsticks to enable compliance check on Sampling plan implemented Monthly Ashish Dubey

5 Quarterly revision of outlier list and allocation to 1 QA and revisiting FMEA Quarterly Ashish Dubey

6 Daily review of SAM errors by 1 dedicated resource following Reburtal SOP Daily Hitesh Mhatre

7 Real time feedbacks for Procedural errors with RCA signoff Daily Ashish Dubey

8 Daily DPR Review with supervisors & agents DailySupervisors and

SDL

9Special Audits to continue, targeting anomalies in floor performance and spike

in productivity and/or claim statusDaily Sam Parulekar

10 Weekly Assessments to check dissemination Weekly

Amit Vaikunthe

and

Anoob Varghese

11 OJT processors on 100% Audits Adhoc

Sanket Mungekar

and

Praveen Nayyar

The process excellence personnel (Black Belt) would conduct monthly dipsticks to

validate sustained implementation of control plan

Process Sustenance Owner: Ashish Dubey (QA Lead)

M A ID


28252219161310741

1.00

0.99

0.98

0.97

0.96

0.95

Weeks

Ov

era

ll C

laim

Pa

ym

en

t A

ccu

racy

_X=0.98488

UCL=0.99954

LCL=0.97023

Pre Post

Overall Claim Handling Accuracy

29

Project Benefits

28252219161310741

1.000

0.995

0.990

0.985

0.980

0.975

0.970

Weeks

Cla

im P

ay

me

nt

Accu

racy _

X=0.99256

UCL=0.99965

LCL=0.98548

Target = 98.3%

PostPre

Claim Payment Accuracy

Other Benefits:

1) Due to Standard work

implementation the leakages

due to TAT were eliminated

(validated by client VOC)

2) A lean project was run parallel

to this project to improve

productivity (improved from

3.17CPH to 4.50CPH)

3) A project on re-work reduction

has been initiated on 1st July

2010

1. Mis-paid amount of $616,705 reduced to $85,505 (15 week comparison).

86% reduction achieved in Mis-paid amount

2. Realized Financial Savings of $1,055,415 to the client and $201,000 to Wipro

(gain share for business optimization) across the last 9 months

3. Institutionalization: Replicated the learning across other client domestic and

captive locations. Improvement in accuracy realized across and hence additional

revenue gain (over and above the amount mentioned in point 1)

4. The project also won the Global Quality Challenge across all domestic, captive and

outsourced locations of the client. The client senior leadership awarded Wipro a

Trophy appreciating the efforts.

5. Synergy with the client to replicate best practices across their organization and

credited as a Partner to the client and much beyond a Vendor.

6. Currently in process of implementing OMEGA across client enterprise


Customer Commendations

From: Client DirectorTo: Kannan Iyer; Amrish SharmaCc: Prashant Kulkarni; Sent: Mon Jul 19 20:51:36 2010Subject: Thanks to the team!

Kannan and Amrish, As the June results are posted I see that your team has been 12months consistently GREEN on delivery of DAR, CPA and PAR! Very nice work!!

I would like to say "Thanks" for the work your team is doing on our account.I have seen consistent quality results and the teams are managingTAT in an effective manner. Please keep up the focus and the good work !!Please share my notes with the teams.

From: Prashant Kulkarni Sent: Saturday, April 24, 2010 8:50 AMTo: Ashish Dubey; Kannan IyerCc: Pradeep Khabrani; Amrish Sharma; Tuhin Tushar Sanyal; Mohit Subject: RE: Accuracy Scores - WIPRO - March 2010 - Updated

Very good performance team. As mentioned by Dirk during last week meeting, the client orgvery happy with our Quality scores. They are looking at us for continuation of such performance &also suggest them changes required to make their business more efficient and economical

This is 9th month of great performance and we are very proud of achievements. Please convey myappreciation to your team.

RegardsPrashant- Account Head

From: Srijit Rajappan Sent: Sunday, April 25, 2010 7:15 PMTo: Prashant Kulkarni; Ashish Dubey; Kannan IyerCc: Pradeep Khabrani; Amrish Sharma; Tuhin Tushar Sanyal; Mohit GoelSubject: RE: Accuracy Scores - WIPRO - March 2010 - Updated

Team,Nice to see the consistent performance from the floor. It sounds very

encouraging when the top executives in client Org commend our performance. Keep up the great work. Consistently superlative performance is the only way to grow our relationship with client. Thank you..

Regards,Srijit - BU Head


Pradeep Khabrani

Manager Mission Quality

[email protected]

Thank You

Wipros Value Proposition

wipro bpo case study

Documents

sigma process

wipro limited nyse

bpo consulting

certified black belts

bpo division green belt

claim payment

process overview

largest bpo service