reducing aht

By: Subhash Mandal Dated 30 Nov 2012

REDUCING AHT – BUSINESS CREDIT SERVICES

VOICE OF THE CUSTOMER - VOC

2

dDEFINE

Customer Comments Critical to Quality-CTQ’s

John McCune : CFO-GE Health Care

The client was unhappy due to missing SLA target in last two

quarters set by them.

Current AHT of process-BCS is at 405 seconds.

End Customers-General Electric.

The agents were unable to understand customer’s problems at first go which took them longer to

reciprocate & resolve their queries.

Need to meet the desired AHT target which is 300 seconds

per call.

Process Owner-AVPBCS has unable to meet the SLA

target of AHT last quarter.AHT = 300 Seconds per call

PROJECT CHARTER

Business case: Genpact is the leading outsourcing firm with clients from across the globe. GE HealthCare - Business Credit Service (GE-HC : BCS) is one of the prominent client for Genpact for over 5 years.

GE-BCS caters to end customer’s queries, concerns and requests. The process is unable to meet the AHT target for this quarter resulting to customer dissatisfaction. If the same persist consecutively for three quarters, the client might take away the business. Hence, lies the opportunity to satisfy the customer and increase company revenue.

Team: Sponsor-Kunal Giri MBB-Naresh Rao Champion-Alka Shukla Process owner-Gunjit Narang BB-Jatin Sharma GB-Jasjot Masuta Team member-Subhash Mandal

Problem Statement: AHT of the process-BCS was 405 seconds resulting to miss service-level in last quarter. 46.61% agents were below the target AHT of 300 seconds. This AHT target will improve the business delivery and eventually enhance opportunity to earn more revenue to the company. Client might pull back the business if the AHT target is not met in the next quarter.

Goal Statement: To improve AHT of the process to 300 seconds by the 30th November 2012, without impacting the quality.

In Scope: The BCS-collections and customer service team in Gurgaon, Delhi & Hyderabad.

Out Scope: Other GE processes.

Milestones Target Date Actual dateD 30/Nov/2012 30/Dec/2012M 31/Jan/2013 28/Feb/2013A 31/Mar/2013 30/Apr/2013I 31/May/2013 30/Jun/2013C 31/Jul/2013 31/Aug/2013

dDEFINE

ARMI

Key Stakeholders ARMI WorksheetDefine Measure Analyze Improve Control

Stakeholders—AM Tyagrajan I I I I I

Sponsor-Kunal Giri I I I I IChampion-Alka Shukla I & A I & A I & A I & A I & A

MBB-Naresh Rao A & I A & I A & I A & I A & IBB-Jatin Sharma I & R I & R I & R I & R I & R

Process Manager-Gunjit Narang I & M I & M I & M I & M I & M

GB-Jasjot Masuta R & M R & M R & M R & M R & M

Team Members-Subhash Mandal M M M M M

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 PlanInformation Or Activity Target Audience Information Channel Who When

Project Status Leadership E-mails Gunjit Narang/Alka Shukla/Naresh Rao

BI-Weekly

Tollgate Review MBB, Black-belt, GB & Champion

E-mails and/or Meetings Naresh Rao, Jatin Sharma, Jasjot Masuta & Alka Shukla

As per Project Plan

Project Deliverables or Activities Members Emails and/or Meetings Weekly

dDEFINE

SIPOC

Supplier Input Process Output Customer

AVAYA Software and networking.

AVAYA software and call-master.

Customer’s call pops-up and received.

Answering call with greeting & agent’s introduction.

GE LESCO credit account holder.

AVAYA Software and networking.

Call master Listening to customer’s query.

Provide necessary information as customer’s requirement.

GE LESCO credit account holder.

Human Resource Agent and call script. Probing, in case of any doubt.

Helps customer with necessary information

GE LESCO credit account holder

SOPProcess flow.

Information and/or documents.

Resolve customer’s query with needed information .

Satisfied & happy customer.


Caller & company. Query, concerns and updates received from customer. GUI to update.

Documentation of conversation.

Update & save conversation summary & provide ticket no. if any.


Call Master. End call with proper verbatim.

Customer’s satisfaction with needed information


dDEFINE

6

dDEFINE PROCESS MAP - FLOW CHART

STARTIs the call for any specific collector / account-manager?

NO

YES

Account-manager/collector answers call with proper greeting &

investigates into customer’s query.

Could customer’s query be resolved

at his end?

Customer service agent answers call with proper greeting & investigates

into customer’s query.

Is collector/account-manager able to

resolve customer's query/request?

YES

Resolve customer’s query/request

Update the conversation's gist & provide ticket no., if needed.

END

Is the special handling

team/supervisor able to resolve customer's

query/request?

NORoute call to special handling team/supervisor to take care.

NO

YES

Route the call to grievance handling team at different location.

Grievance handling team addresses customer’s issue & provides resolution.

Gives a TAT in case future follow-up.

NO

YES

Resolve customer’s query/request

Customer calls in requesting for key-code, account details,

invoice/statement copy, making payment on account etc.

End the call with propergreeting & verbatim.

DATA COLLECTION PLAN

KPI Operational Definition Defect Def Performance StdSpecification Limit

OpportunityLSL USL

AHT

The total time taken by an agent including Talk-time/Hold time/After Call Work time against total no. of

calls taken, expressed in seconds, in a month.

Any call duration exceeding 300 sec

will be considered a defect.

300 Sec NA 300 Sec Monthly AHT

KPI Data Type Data Items Needed

Formula to be used Unit

Sec Plan to sample

What Database or Container

will be used to record this data?

Is this an existing

database or new?

If new, When will

the database be ready for use?

When is the planned

start date for data

collection?

AHT Continuous

AHT, Talk time, Hold time, ACW, No. Of calls

taken

AHT=(Talk time+Hold

time+After call work time)/The

no. of calls taken

Seconds MS Excel Existing NA NA July 11’ to Dec 11’

mMEASURE

8

IMR CHART : PRE IMPROVEMENT

There are special cause variations so the process was statistically out of control.

mMEASURE

MSA - GAGE R&R ANOVA

Gage R&R

%ContributionSource VarComp (of VarComp)Total Gage R&R 4031.4 6.89 Repeatability 3847.0 6.58 Reproducibility 184.4 0.32 Operator 184.4 0.32Part-To-Part 54472.1 93.11Total Variation 58503.6 100.00

Process tolerance = 1

Study Var %ToleranceSource StdDev (SD) (6 * SD) (SV/Toler)Total Gage R&R 63.494 380.96 38096.10 Repeatability 62.024 372.15 37214.63 Reproducibility 13.579 81.48 8147.64 Operator 13.579 81.48 8147.64Part-To-Part 233.393 1400.36 140035.60Total Variation 241.875 1451.25 145125.06

Number of Distinct Categories = 5

As all the Rules for Gage R & R ANOVA method are satisfied by the data, so we can take this data for further Analysis

mMEASURE

3 Rules of GageR&R :1)GageR&R as a percentage of contribution towards total variation should be smaller that part-to-part variation.2)GageR&R as a percentage of tolerance towards total variation:

a) Accept, if less than 10%b) May accept with caution if between 10-30%c) Reject if greater than 30%

3)No. of distinct categories should be equal to or greater than 4

STABILITY - RUN CHART

As P-value for Mixture, Cluster, Trend & Oscillation are greater than 0.05, the Data is STABLE.

mMEASURE

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PROCESS CAPABILITYm

MEASURE

As the Process is working at a sigma level of 1.4 and the DPMO is 537,615. So, there is a great opportunity for Improvement in the process

Z-Value

Mean 405.65

Std. Dev. 273.23

USL 300

DPMO 537,614.68

SIGMA LEVEL 1.4

CAUSE & EFFECT DIAGRAMa

ANALYSE

13

aANALYSE POTENTIAL Xs

Sr. no. Potential X Description Data type Test to be done

1 Trainer A person, assigned to train & teach people about the new job which they will be doing after the learning completes. Data type Mood’s Median Test

2 Process knowledge

Overall knowledge of the process functionality, job description, conduct & other vital information needed to work efficiently and effectively.

DiscreteMann Whitney Test

3 Shift-timing The time when an agent logs in & starts his shift till he logs off.

Discrete Mood’s Median Test

4 Gender Whether an agent is a male OR female. Discrete Mann Whitney Test

5 Location Place from where the calls being taken Discrete Mann Whitney Test

6 Age Age of the agent in years Continuous Regression test

7 Tenure Duration of the agent being in the company. Continuous Regression test

8 Education Academic background and qualification of agent Discrete Mood’s Median Test

9 Marital status Whether the agent is married OR unmarried. Discrete Mann Whitney Test

10 Communication mode

Language in which the agent communicates withIts customers, i.e., English or Hindi

Discrete Mann Whitney Test

11 Process complexity The critical level of the process, i.e., P-I, P-II or P-III. Discrete Mann Whitney Test

AHT vs TRAINER - MOOD’S MEDIAN TEST

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Mood Median Test: Project Y versus Trainer

Mood median test for Project YChi-Square = 53.57 DF = 5 P = 0.000

Individual 95.0% CIsTrainer N<= N> Median Q3-Q1 +---------+---------+---------+------Amit 56 32 247 367 (--*-----)Atul 56 20 194 247 (--*-)Daniel 69 49 270 459 (---*----)Rashid 21 43 535 517 (-------*------)Ruby 42 91 494 454 (------*------)Sonia 29 37 435 359 (--------*----) +---------+---------+---------+------ 150 300 450 600

Overall median = 336

As P-value < 0.05, the median of

trainers are significantly

not-equal to each other. Hence, we

will do further analysis on trainers.`

aANALYSE

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AHT BOX PLOT : TRAINER

Agents trained under Atul have the least AHT & agents

trained under Rashid have highest

AHT. Hence, we’ll further break down this to Trainers in

Different Locations.

aANALYSE

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AHT BOX PLOT :TRAINERS IN DIFFERENT LOCATIONS

Trainees trained under Rashid at C5 have better AHT. Trainees under

Sonia have better AHT at C6. Amit’s

trainees have better AHT at C6 vs C5.

Hence, we’ll further break down this to

Trainers in Different Locations.

aANALYSE

17

AHT BOX PLOT:TRAINERS IN PROCESS COMPLEXITY

Amit : Trainees in L2 have lesser AHT vs

trainees in L1.

Rashid : Trainees in L1 have lesser AHT

vs trainees in L2.

Atul and Daniel both have more than 50 % trainees meeting

the AHT target of 300 seconds.

aANALYSE

18

Atul : More than 75% of male

trainees are meeting the AHT target of

300 Seconds.

Daniel : Female trainees have lesser

AHT vs male trainees.

AHT BOX-PLOT:TRAINERS WITH DIFFERENT GENDERSa

ANALYSE

AHT vs PROCESS KNOWLEDGE-MANN WHITNEY TEST

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Mann-Whitney Test and CI : Project Y_FAIL, Project Y_PASS

N MedianProject Y_FAIL 351 335.00Project Y_PASS 194 358.00

Point estimate for ETA1-ETA2 is 7.00

95.0 Percent CI for ETA1-ETA2 is (-30.99,48.98) W = 96567.0

Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.6727

Hence, P-Value is 0.6727

The test is significant at 0.6727 (adjusted for ties)

As P-value >0.05, the median of

Process-knowledge of FAIL is significantly equal

to the median of Process-knowledge of

PASS. Hence, no further analysis needs to be

done.

aANALYSE

20

AHT vs SHIFT-TIMING : MOOD’S MEDIAN TEST

Mood Median Test: Project Y versus Shift


Individual 95.0% CIsShift N<= N> Median Q3-Q1 +---------+---------+---------+------Evening 129 110 297 483 (------*-------)Morning 81 72 298 440 (------*-----------)Night 63 90 429 441 (-------*--------) +---------+---------+---------+------ 240 320 400 480


As P-value < 0.05, the median of AHT in three different

shifts are significantly

not-equal to each other. Hence, we

will do further analysis onshift-timing.

aANALYSE

21

50% agents are meeting AHT target in Morning and Evening shifts. Less than 50% agents are meeting AHT target of 300

seconds in Night shift.

AHT BOX-PLOT : SHIFTa

ANALYSE

22

100% agents trained by Atul in Evening shift

have AHT less than 300 Sec & more than 75% agents have AHT less than 300 sec in

Night shift under Rashid.

AHT BOX-PLOT : SHIFT WITH DIFFERENT TRAINERSa

ANALYSE

23

AHT BOX-PLOT:SHIFT WITH PROCESS COMPLEXITIES

More than 50% agents in L1, morning shift and L2 evening shift are meeting the AHT

target of 300 Seconds. In L1, night shift only 25% are meeting AHT target of 300 Seconds.

aANALYSE

24

AHT BOX-PLOT : SHIFT BY MALES & FEMALES

Females in evening shift and Males in morning shift are

meeting AHT target of 300 Seconds. In night shift, neither males nor the females are

meeting the AHT target.

aANALYSE

25

AHT BOX-PLOT : SHIFT IN DIFFERENT LOCATIONS

More than 50% agents at C6 evening and morning shift are meeting the AHT

target of 300 seconds. And in night shift

more than 25% agents are meeting the

target.

aANALYSE

26

AHT vs GENDER - MANN WHITNEY TEST

Mann-Whitney Test and CI: Project Y_F, Project Y_M

N MedianProject Y_F 234 334.50Project Y_M 311 344.00





As P-value >0.05,the median of AHT of Males is

significantly equal to the median of AHT of

Females. Hence, no further analysis needs to

be done.

aANALYSE

27

AHT vs LOCATION - MANN WHITNEY TEST

Mann-Whitney Test and CI: Project Y_C5, Project Y_C6

N MedianProject Y_C5 303 363.00Project Y_C6 242 299.00






AHT of Location C5 is significantly equal to the

median of AHT of Location C6. Hence, no further

analysis needs to be done.

aANALYSE

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AHT vs AGE - REGRESSION TEST

Regression Analysis: Project Y versus Age

The regression equation isProject Y = 377 + 1.03 Age

Predictor Coef SE Coef T PConstant 377.02 75.94 4.96 0.000Age 1.025 2.686 0.38 0.703

S = 273.632 R-Sq = 0.0% R-Sq(adj) = 0.0%

Analysis of Variance

Source DF SS MS F PRegression 1 10905 10905 0.15 0.703Residual Error 543 40656738 74874Total 544 40667643

As P-value >0.05, so there is no impact of age

on AHT. Hence, no further analysis needs to

be done.

aANALYSE

29

AHT vs TENURE - REGRESSION TEST

Regression Analysis: Project Y versus Tenure-Years

The regression equation isProject Y = 405 + 0.06 Tenure-Years

Predictor Coef SE Coef T PConstant 405.39 31.59 12.83 0.000Tenure-Years 0.058 6.381 0.01 0.993

S = 273.668 R-Sq = 0.0% R-Sq(adj) = 0.0%

Analysis of Variance

Source DF SS MS F PRegression 1 6 6 0.00 0.993Residual Error 543 40667637 74894Total 544 40667643

As P-value >0.05, so there is no impact of

Tenure on AHT. Hence, no further


aANALYSE

30

AHT vs EDUCATION - MOOD’S MEDIAN TEST

Mood Median Test: Project Y versus Education


Education N<= N> Median Q3-Q1Graduate 80 102 389 471Higher Secondary 99 83 297 478Post-Graduate 94 87 319 463

Individual 95.0% CIsEducation --------+---------+---------+--------Graduate (---------*-----------)Higher Secondary (-------*----------)Post-Graduate (--------*----------) --------+---------+---------+-------- 300 360 420


As P-value > 0.05, the median of

Education in four different cases are

significantly equal to each other. Hence, we will NOT

do any further analysis oneducation.

aANALYSE

31

AHT vs MARITAL STATUS - MANN WHITNEY TEST


AHT of Married is significantly equal to the

median of AHT of Unmarried. Hence, no further


Mann-Whitney Test and CI: Project Y Married, Project Y Single

N MedianProject Y Married 227 330.00Project Y Single 318 340.50

Point estimate for ETA1-ETA2 is -0.00

95.0 Percent CI for ETA1-ETA2 is (-40.02,36.97) W = 62014.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.9813


aANALYSE

32

AHT vs COMMUNICATION MODE-MANN WHITNEY TEST


AHT of English Communication is

significantly equal to the median of

AHT of Hindi Communication. Hence,

no further analysis needs to be done.

Mann-Whitney Test and CI: Project Y English, Project Y Hindi

N MedianProject Y English 303 366.00Project Y Hindi 242 317.50

Point estimate for ETA1 - ETA2 is 4.00

95.0 Percent CI for ETA1-ETA2 is (-33.00, 43.99)W = 83167.0



aANALYSE

33

AHT vs PROCESS COMPLEXITY-MANN WHITNEY TEST


AHT of Complexity at L1 is significantly equal to

the median of AHT of L2. Hence, no

further analysis needs to be done.

Mann-Whitney Test and CI: Project Y_L1, Project Y_L2

N MedianProject Y_L1 241 355.00Project Y_L2 304 319.00

Point estimate for ETA1-ETA2 is -11.00




`

aANALYSE

34

aANALYSE VITAL Xs OUT OF POTENTIAL Xs

Sr. no. Potential X Test done P-Value Impact-Y/N

1 Trainer Mood's median 0.0000 YES

2 Process knowledge Mann Whitney 0.6727 NO

3 Shift timing Mood's median 0.0330 YES

4 Gender Mann Whitney 0.8749 NO

5 Location Mann Whitney 0.6688 NO

6 Age Regression 0.7030 NO

7 Tenure Regression 0.9930 NO

8 Education Mood's median 0.1140 NO

9 Marital status Mann Whitney 0.9813 NO

10 Communication mode Mann Whitney 0.8065 NO

11 Process Complexity Mann Whitney 0.5179 NO

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aANALYSE VITAL Xs

Based on analysis done on the Eleven POTENTIAL Xs, we found two VITAL Xs out of all the POTENTIAL Xs which are:

1.Trainer

2.Shift-timings

We have some detailed results upon analysis of these two VITAL Xs, which have been mentioned in the next slide…

36

More than 50% candidates under Amit, Atul & Daniel are meeting the AHT target of 300 seconds

Trainees trained under Rashid at C5 have better AHT.

Trainees under Sonia have better AHT at C6. Amit’s trainees have better AHT at C6 vs C5

Trainees in location L2 have lesser AHT vs trainees in L1 under trainer Amit

Trainees in location L1 have lesser AHT vs trainees in L2 under Rashid

Atul and Daniel both have more than 50% trainees meeting the AHT target of 300 seconds

More than 75% of male trainees are meeting the AHT target of 300 Seconds under Atul and Female trainees have lesser AHT vs Male trainees under Daniel

In Morning & Evening shifts, 50% agents are meeting AHT target and in night shift less than 50% are meeting their target of 300 seconds

In Evening shift, 100% agents are meeting their target under Atul & in night shift more than 75% agents are meeting AHT target of less than 300 sec trained under Rashid

More than 50% agents in L1, morning shift and L2 evening shift are meeting the AHT target of 300 Seconds. In L1, night-shift, only 25% are meeting AHT target of 300 Seconds

Females in evening shift and Males in morning shift are meeting the target. In night shift, neither males nor the females are meeting the AHT target

More than 50% agents at C6 evening and morning shift are meeting target. And in night shift more than 25% agents are meeting the AHT target of 300 sec

IMPROVEMENT PLAN:BASED ON ANALYSIS

iIMPROVE

37

QUALITY FUNCTIONAL DEPLOYMENTi

IMPROVE

38

iIMPROVE FMEA-RISK TREATMENT PLAN : TRAINER

CONTROL PLANc

CONTROL

Activities Responsibilities Frequency

Share Best Practices Training team Weekly

Train the Trainer Training team Quarterly

Trainers' monthly rating Training team Monthly

Performance based annual growth HR Team Annual

R n R HR Team Quarterly

Shift rotation Operations Monthly

Provide night allowance Operations Monthly

Provide pickup/drops for night shift Operations Daily

Games & fun activities in night shift HR Team Weekly

Enhance night allowance for future HR Team Weekly

Take pre approval for night allowance Operations Monthly

Make proper rostering to avoid transport delay. Transport team Daily

Reduce time to cancel transport in case of self arrangement Transport team Daily

Put guards mandatory in cabs with female agents. Security Daily

Encourage agents to self manage breaks. Operations Daily

Mandate split off with Sat or Sun. Operations Daily

Take pre approval for night contests Operations Quarterly

40

Before the project, more than 50% of agents had AHT above 300 seconds

whereas after the project, AHT dropped down to less than 300

seconds for over 75% of the agents.

AHT BOX PLOT:PRE DATA vs POST DATAc

CONTROL

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GRAPHICAL SUMMARY: PRE DATA vs POST DATA

Post Project:

Mean:200.93 Median :209 StDev:102.4

Most of the agents have

AHT less than 300 seconds

after the project

cCONTROL

BAR GRAPH: PRE DATA vs POST DATA

MeanPre Improvement:405.65

Post Improvement: 200.93

MedianPre Improvement:336.00Post Improvement:209.00

St. DevPre Improvement: 273.42Post Improvement: 102.44

cCONTROL

43

SIGMA LEVEL & DPMO:PRE vs POST IMPROVEMENTc

CONTROL

Pre Improvement Data Post Improvement Data

No. of defects were 254 out of 545 opportunities

DPMO was 466,055

Sigma level was 1.6

No. of defects are 118 out of 545 opportunities

DPMO reduced to 216,514

Sigma level went up to 2.3

44

Prior improvement, there were special cause variation so the process was statistically out of control, however, post-improvement, there is no special cause variation and the process is statistically in control.

IMR CHART : PRE vs POST IMPROVEMENTc

CONTROL

45

cCONTROL LEADERSHIP APPRECIATION!

46

Thank you!

[email protected]

reducing aht

Education

target aht

i bbjatin sharma

customers queries

customers satisfaction

customers problems

customers requirement

customer service team

current aht of process