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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.
GE LESCO credit account holder
Caller & company. Query, concerns and updates received from customer. GUI to update.
Documentation of conversation.
Update & save conversation summary & provide ticket no. if any.
GE LESCO credit account holder
Call Master. End call with proper verbatim.
Customer’s satisfaction with needed information
GE LESCO credit account holder
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
11
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
14
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
15
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
16
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
19
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
Mood median test for Project YChi-Square = 6.80 DF = 2 P = 0.033
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
Overall median = 336
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
Point estimate for ETA1-ETA2 is 3.00
95.0 Percent CI for ETA1-ETA2 is (-35.97,41.01) W = 64169.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.8749
The test is significant at 0.8749 (adjusted for ties)
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
Point estimate for ETA1-ETA2 is 8.00
95.0 Percent CI for ETA1-ETA2 is (-30.01,49.02) W = 83501.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.6688
The test is significant at 0.6688 (adjusted for ties)
As P-value >0.05, the median of
AHT of Location C5 is significantly equal to the
median of AHT of Location C6. Hence, no further
analysis needs to be done.
aANALYSE
28
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
analysis needs to be done.
aANALYSE
30
AHT vs EDUCATION - MOOD’S MEDIAN TEST
Mood Median Test: Project Y versus Education
Mood median test for Project YChi-Square = 4.33 DF = 2 P = 0.114
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
Overall median = 336
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
As P-value >0.05, the median of
AHT of Married is significantly equal to the
median of AHT of Unmarried. Hence, no further
analysis needs to be done.
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
The test is significant at 0.9813 (adjusted for ties)
aANALYSE
32
AHT vs COMMUNICATION MODE-MANN WHITNEY TEST
As P-value >0.05, the median of
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
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.8065
The test is significant at 0.8065 (adjusted for ties)
aANALYSE
33
AHT vs PROCESS COMPLEXITY-MANN WHITNEY TEST
As P-value >0.05, the median of
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
95.0 Percent CI for ETA1-ETA2 is (-55.01,26.01) W = 64612.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.5179
The test is significant at 0.5179 (adjusted for ties)
`
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
35
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
41
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!