Reduce AHT Through DMAIC Process

Define Deliverables
DEFINE
Project Mapping and Pre DMAIC Analysis
Project Charter
Terms & Acronyms Used
ARMI/RASIC
Communication Plan
Process Map (Flow Chart)
SIPOC/COPIS

Project Mapping
DEFINE
Customer Sample Comments
Key Output Characteristics
Important to Customer (CTQ's)
Mr. Vineet Unny, Process owner of
Advance Health.
He is worried as the C-Support is a new LOB which started 5 months
ago, and after completing ramp, team is not achieving the desired
AHT target. Moreover, company have to pay extra cost by OT so that
employees achieve the required call volume for the day.
Need to reduce AHT
Mr Rahul Gupta, Process Manager of
C-Support LOB.
Looking at the daily performance of the team, we are still not able
reduce the AHT. We have to work on the employee Handling time. So
that, we can amuse our customer and fulfil their needs.
Increase the Process performance by reducing
handling time.
Advance Health Pvt. Ltd. Process improvement Data Type : Continuous

Graphical Summary
DEFINE
6.00
5.25
4.50
3.75
3.00
2.25
1.50
Median
Mean
3.6
3.5
3.4
3.3
3.2
3.1
3.0
1st Q uartile 2.1500
Median 3.2400
3rd Q uartile 4.7150
Maximum 5.9900
3.2121 3.5940
2.9700 3.5801
1.3042 1.5756
A -Squared 2.84
P-V alue < 0.005
Mean 3.4031
StDev 1.4271
V ariance 2.0365
Skewness 0.14667
Kurtosis -1.18654
N 217
Minimum 1.0200
A nderson-Darling Normality Test
95% C onfidence Interv al for Mean
95% C onfidence Interv al for Median
95% C onfidence Interv al for StDev
95% Confidence Intervals
Summary for HT
C-SAT
LOB
AHT
Performance
8
6
4
2
0
99.9
99
95
90
80
70
60
50
40
30
20
10
5
1
0.1
HT
Percent
Mean 3.403
StDev 1.427
N 217
AD 2.838
P-Value <0.005
Probability Plot of HT
Normal
Inference: As we have seen that the AHT of the LOB, the handling time is hovering around 3.24, which is not good for the process . We have
seen the huge difference between the AHT when we have done the normality test for the same. We have seen that there are more than 40%
of employees whose AHT are above 4.00 . So we are aiming our Target around 2.80 for LOB and Process improvement.

Project Charter
Business Case- Advance Health is a Health Care Insurance company which provide various types of policy to their
customer. C-Support is one of the LOB where we provide customer support via call and verify patient details and
provide information related to covered and non-covered services. C-Support LOB facing issue related to AHT and it
leads to customer dissatisfaction due to not resolving queries on time. As per our analysis if we will improve the
AHT and make it to 2.80 then we can save approximately 1. 3 million dollars yearly.
Problem Statement- After analysis of the last months data it is observed that our process AHT is now 3.24 which is
way behind the process target of 3.00. If we calculate in monetary term then we have deprivation of $2,593,66.7 in
last 5 months. Moreover, we have to give overtime to our employees to achieve the desired target which is also the
cost for the company. If we will not control the AHT in coming months then it will impact more to our company
revenue.
Goal Statement- We will reduce the AHT to 2.80 till 19th April ‘20.
In Scope- Gurgaon Site, C-Support LOB
Out Scope- Rest other site and LOB.
DEFINE

Milestones
DEFINE
Milestone Reviews
M1 Close Define Phase M2 Close Measure Phase
M3 Close Analyze Phase M4 Close Improve Phase
M5 Close Control Phase
Months Month 1 Month 2 Month 3 Month 4
Weeks
Week
1
Week
2
Week
3
Week
4
Week
5
Week
6
Week
7
Week
8
Week
9
Week
10
Week
11
Week
12
Week
13
Dates
26-Jan
2-Feb
9-Feb
16-Feb
23-Feb
1-Mar
8-Mar
15-Mar
22-Mar
29-Mar
5-Apr
12-Apr
19-Apr
Define &
Measure M1 & M2
Analyze M3 M3
Improve M4 M4 M4 M4
Control M5 M5 M5 M5

Terms & Acronyms Used
DEFINE
Indicators Definition
AHT Average Handling Time
Production No. of Calls employee takes
Target Total production that need to be done
CSAT Customer Satisfaction
C-Support LOB
TAT Turn Around Time
OC Office Communicator
SLA Service Level Agreement

ARMI
DEFINE
When Populating the Stakeholder, consider the ARMI:
• A= Approver of team decisions
• R= Resource or subject matter expert (ad hoc)
• M= Member of team
• I= Interested Party who will need to be kept informed
Key Stakeholders Define Measure Analyze Improve Control
Mr. Unny (Process Owner) I,A I I I I,A
Operation Managers I,M I,M I,M I,M I,M
Green Belt (GB) I,R,M I,R,M I,R,M I,R,M I,R,M
Training & Quality Team I,R I,R I,R I,R I,R
Team Leaders, SME & QA’s I,R,M I,R,M I,R,M I,R,M I,R,M
IT Department I I I I,M I

RASIC
DEFINE
RASIC Chart for Define & Measure
Activities
DPE
Process
Manager
MBB(Coach)
Green
Belt
Quality
&
Training
Team
Team
Leader,
SME,
QA
IT
Department
Team
Member’s
Collect VOC from all stakeholders I,A,C I,S I,C R S S S C
Conduct Stakeholder analysis - - I,C R - - - -
Collect data for the last 12 months - - I,C R - - - -
Analysis of data I I,S I,C R - - - C
Report out on the Pre DMAIC Analysis I I,S I,C R - - - -
Create Project Charter - I,S I,C R S S - -
Send Charter for Executive Approval A - I,C R - - - -
Approve Charter A - I,C R - - - -
Build SIPOC - I,S I,C R - - - -
Build Process Map - I,S I,C R - - - -
Build the data collection plan I I I,C R C,S S - -
Get the DCP Approved I I I,C R C,S - - -
Approve DCP A I I,C R C,S - - -
Collect Data I I I,C R C,S - - C
Validate data - I I,C R C,S - - C
Publish next steps to stakeholder - I I,C R - - - -
• Responsible (R) : Solely and directly responsible for the activity (Owner) - Includes approving authority (A)
• Approve (A) : Reviews and assures that the activity is being done as per expectations
• Support (S) : Provides the necessary help and support to the owner
• Inform (I) : Is to be kept informed of the status/progress being made
• Consult (C) : Is to be consulted for this activity for inputs

Communication Plan
DEFINE
Message Audience Media Who When
Project Charter Sr. DPE & DPE E-mail, Call & OC ME 28th
Jan ‘21
Team Meeting Team Member (All Stakeholders) E-mail Invite ME Alternate Days
Project Progress – 1st
Phase Team Member (All Stakeholders) E-mail, OC ME 31st Jan’21
Mitigate Review – 2nd
Phase Approvers E-mail ME 5th
Feb’21
Technology Change – Process
Requirement
IT & Ops E-mail ME & Process Manager Tentative
Project Progress – 3rd
Phase Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 17th
Feb’21
Project Progress – 4th
Phase Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 1st
March ’21
Project Progress- Improvement
Trending
Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 10th
March ‘21
Project Progress – 4th
Phase Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 22nd March ’21

Process Map
DEFINE
Start
Patient visit hospital for treatment
and call CCE for policy verification
Call picks by the Agents. Agent
greets and probe the required
verification and reason for making
the call
CCE will verify the patient
eligibility for the DOS
is Agents
able to
verify the
eligibility
Escalate the issue.
CCE verify if services
are covered or not
Is agents
able to
verify the
services.
End
Yes
No
No
Yes
Caller provides the patient
details for policy verification

COPIS
DEFINE
Customer Output Process Input Supplier
Patient visit hospital for treatment
Caller will provide the patient
details over call.
CCE will verify the patient eligibility
for the date of service.
After eligibility, policy holder will verify
the service taken by the patient by
submitting scanned copy.
CCE will verify the service is covered or
not by inputting details in application.
CCE will escalate the issue if he/she is
not able to resolved the issue.
Call Close
Patient details
Scanned Copy of
Services taken.
Contract Details.
Caller
Resolved
WIP
Decline
Caller
Respective
Department
Management

Measure Deliverables
MEASURE
Data Collection Plan
Measurement System Analysis
Process Capability

Data Collection Plan
MEASURE
Y Operational Definition Defect Definition
Performance
Standard
Specification Limit Opportunity
Average Handling Time of
team per week
Start time and End time for each call is
captured by avaya. Calculated call
duration for each call by subtracting
start time from end time. Calculate
duration in a week and divide that by no
of calls taken in a week.
If AHT of team for any
given week is greater
than 2.80.
2.80 minutes
USL- 3.00
LSL- N/A
Every Week
Y Data Type
Unit of
Measurement
Decimal Places
Database
Container
Existing/New
Database
To date- From Date
Average Handling
Time of team per
week
Continuous Minutes 2 Excel Existing 31st
Dec 01st
Oct
Data Items
Needed
Formula to be
Used
Equipment Used
for Measurement
Equipment
Calibration Info
Responsibility Training Need
Operator
Information
Hold Time, Probing
Time, Resolution Time,
Summary Time
Total time spend on a
call in a week / No of
calls taken in a week
Avaya
NA SME Yes Team A
Mode of Collecting Data

MEASURE
EFFECTIVENESS
OP2
OP1
100
90
80
70
60
50
Appraiser
Percent
95.0% C I
Percent
OP2
OP1
100
90
80
70
60
50
Appraiser
Percent
95.0% C I
Percent
Date of study:
Reported by:
Name of product:
Misc:
Assessment Agreement
Within Appraisers Appraiser vs Standard
EFFECICIENCY
10 existing samples were picked and measured by 2 different operators and master calibrator(standard). Each operator has
measured each sample twice.
AIAG: Automotive Industry Action Group
1. AAA>=90%, Accept
2. 70%=<AAA<90%, Your Call
3. AAA<70%, Reject
MSA: Pass
Practice Purpose Only

MEASURE
Minitab Descriptive Statistics Rule
Rule
Description
Acceptable Result
A
R&R % of
Tolerance
< 10%
(9.65)
Pass
B
% Contribution
(R&R Std
deviation)
Smaller than
Part to Part
Variation
(.93)
Pass
C
Number of
distinct
categories
>=4
(14)
Pass
Overall Gage Result – “MSA Passed”
Gage R&R (ANNOVA)– Crossed

Process Capability
MEASURE
DPMO
• Discrete data
Z - SCORE
• Numerical (Continuous, Count, %age)
• Normal Distribution
• At least one specification applicable
Cp, Cpk
• Continuous Data
• Normal Distribution
• Both specification applicable

Process Capability-DPMO
DPMO: Defects Per Million Opportunities
DPMO= DPO*1000000 = 0.5*1000000 = 500000
DPO: Defects Per Opportunity
DPO=Total number of defects/Total opportunities
=10/20=0.5
%age Fail=DPO*100=0.5*100=50%
%age Pass = 100%-%age Fail = 100%-50%=50%
Calls Script Verification Enquiry resolved Tag
Call 1 P P P P
Call 2 P P P F
Call 3 P P F F
Call 4 P F F F
Call 5 F F F F
PPM: Parts Per Million
PPM= DPU*1000000 = 0.8*1000000 = 800000
DPU: Defects Per Unit
DPU=Total number of defective units/Total units audited = 4/5 = 0.8
Practice Purpose Only

Process Capability-Cp & Cpk
Z. Bench : - 0.24
Z Short Term : 1.26

Analyze Deliverables
ANALYZE
Identify Potential Factors
Fishbone
DCP for Potential Factors
Basic Analysis for Project Y
Checking for Impact of Factors on Y
Hypothesis Summary
MSA results of Impacting Factors

Cause & Effect Diagram
ANALYZE

DCP for Potential Xs
ANALYZE
Potential Cause Type of Data Collection Method Test to be Used
Visualization plot
Used
Handling Time Continuous Automated 1 Sign Test Box Plot
Case Type Discrete Automated Mann Whitney Box Plot
Sub Query Discrete Automated Moods Median Box Plot
Product(Policy Type) Discrete Automated Moods Median Box Plot
Supervisor Discrete Automated Mann Whitney Box plot
Shift Discrete Automated Moods Median Box Plot
Gender Discrete
Automated Mann Whitney
Box Plot
Tenure Continuous
Automated
Regression / Co-relation Scatter
Hold Time Continuous Automated Regression / Co-relation Scatter
Probing Time Continuous Automated Regression / Co-relation Scatter
Resolution Time Continuous Automated Regression / Co-relation Scatter
Outlining Data Collection Steps for Xs

Basic Data Analysis for Project Y
ANALYZE
Randomness Study
Randomness & Shape Study
Normality Study
As per Run Chart, Clustering, Trend, Mixture, Oscillation P
value is > .05, which means our HT data is random and
stable.
As per Normality Test, P value is < .05, which
means HT data is non – normal.

1 Sample Sign Test on Handling Time(Y)
ANALYZE
1 Sample Sign Test
Sign test of median = 2.800 versus > 2.800
N Below Equal Above P Median
217 82 1 134 0.0003 3.240
Inference – There are total 217 data
points of Y, and if we compare data
according to our proposed median
there are 134(61.75%) data points
which are above than the target.
Moreover, P value is < .05, which
means alternate hypothesis(Ha) is true
and according to it there is significant
impact

Checking for Impact of x1 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
N Median
HT_Complaint 17 2.970
HT_Inquiry 200 3.285
Point estimate for ETA1-ETA2 is -0.270
95.0 Percent CI for ETA1-ETA2 is (-1.060,0.440)
W = 1685.5
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at
0.5016
The test is significant at 0.5016 (adjusted for ties)
Inference – As ETA1 and ETA2 is significant at
.5016, which means P value is > .05 and hence null
hypothesis is true.
Mann-Whitney Test

ANALYZE
Graphical Depiction
Chi-Square = 7.74 DF = 5 P = 0.171
Sub Query Type N<= N> Median Q3-Q1
Claims query related grievance 0 4 4.01 1.40
Deductions in claims 2 1 3.16 2.30
Delay in claims settlement 4 1 1.96 1.47
Early Claim Settlement 3 3 3.23 2.84
Reimbursement Not received 0 1 4.95 *
Rejections in Claims 7 3 3.20 2.47
Status 93 95 3.29 2.56
Individual 95.0% CIs
Sub Query Type --------+---------+---------+--------
Claims query related grievance (----*--------)
Deductions in claims (--*---------------)
Delay in claims settlement (---*-------------)
Early Claim Settlement (-----------*------------------)
Reimbursement Not received
Rejections in Claims (---------*-----------)
Status (-*---)
--------+---------+---------+--------
2.4 3.6 4.8
Overall median = 3.24
* NOTE * Levels with < 6 observations have confidence < 95.0%
Inference – As P value is > 0.05, hence Ho applied
and there is no significant impact between the sub
query type.
Mood median test for HT

ANALYZE
Graphical Depiction
Chi-Square = 15.51 DF = 15 P = 0.415
Product (Policy No) ---+---------+---------+---------+---
Citibank Optima Restore Floater
Dengue Care (--------*-------)
Easy Health Floater Standard (--*---)
Easy Health Floater Standard Two Year (----------*---)
Easy Health Group- Floater- Canara (--------*------)
Easy Health Group- Individual- Canara (---*------)
Easy Health Group Floater Indian Overseas Bank (---------*--------
--)
Easy Health Group Individual Indian Overseas Bank
Easy Health Individual Exclusive
Easy Health Individual Premium
Easy Health Individual Standard (-*------)
Easy Health Individual Standard Two Year (---*-----)
Group Health Floater (--------*-----------------)
Group Health Individual (---*----------)
Individual Personal Accident Standard (---------------*
Optima Cash- Gold (-*-)
Optima Cash- Gold Two Year
Optima Restore Floater (----*---)
Optima Restore Floater Two Years (------*----)
Optima Restore Individual (------------*------)
Optima Restore Individual Two Years (-----*----)
---+---------+---------+---------+---
1.5 3.0 4.5 6.0
Inference:- Since P value is > 0. 05, which means
Null hypothesis is true, and hence there is no
signigicant impact between Product types
Mood Median Test: HT versus Product
(Policy No)

ANALYZE
Graphical Depiction
Supervisor N Median
HT_Danish 103 2.5300
HT_Kanwarpreet 114 4.1950
95.0 Percent CI for ETA1-ETA2 is (-1.7298,-
0.9599)
W = 8257.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is
significant at 0.0000
The test is significant at 0.0000 (adjusted for
ties)
Inference:- As per MW hypothesis
test between supervisors, P value is <
0.05, which means there is significant
impact.
Mann-Whitney Test and CI: HT_Supervisors

ANALYZE
Graphical Depiction
Mood Median Test: HT versus Shift
Chi-Square = 2.40 DF = 2 P = 0.302
Shift N<= N> Median Q3-Q1 ---+---------+---------
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Inference:- Since P value is > .05, which
means Ho is true and there is no
significant impact.

ANALYZE
Graphical Depiction
Results for: gender
Mann-Whitney Test and CI: HT_Female, HT_Male
N Median
HT_Female 171 2.8500
HT_Male 46 5.4100
95.0 Percent CI for ETA1-ETA2 is (-2.8999,-2.2501)
W = 14786.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at
0.0000
The test is significant at 0.0000 (adjusted for ties)
Inference:- Since P value is < .05, which
means Ha is true and there is significant
impact.

ANALYZE
Graphical Depiction
Chi-Square = 2.40 DF = 2 P = 0.302
Shift N<= N> Median Q3-Q1 ---+---------+---------+---------+---
Evening 31 41 3.65 2.53 (------------*---------)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Inference:- Since P value is > .05, which means Ho is true and
there is no significant impact.
The regression equation is
HT = 7.32 - 0.787 Tenure
Predictor Coef SE Coef T P
Constant 7.3183 0.2989 24.49 0.000
Tenure -0.78715 0.05835 -13.49 0.000
S = 1.05261 R-Sq = 45.8% R-Sq(adj) = 45.6%
Analysis of Variance
Source DF SS MS F P
Regression 1 201.67 201.67 182.01 0.000
Residual Error 215 238.22 1.11
Total 216 439.88
Inference:- Since P vale < .05, which
means Ha is true and there is
significant impact between HT and
Tenure.
Regression Analysis: HT versus Tenure

ANALYZE
Graphical Depiction
Chi-Square = 2.40 DF = 2 P = 0.302
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
significant impact.
HT = 2.13 + 2.67 Hold Time
Constant 2.1282 0.2344 9.08 0.000
Hold Time 2.6745 0.4539 5.89 0.000
S = 1.32722 R-Sq = 13.9% R-Sq(adj) = 13.5%
Source DF SS MS F P
Regression 1 61.156 61.156 34.72 0.000
Total 216 439.882
Inference:- As per the test P value is <0.05,
which means Ha is true and there is significant
impact between HT and hold time.
Regression Analysis: HT versus Hold Time

ANALYZE
Graphical Depiction
Chi-Square = 2.40 DF = 2 P = 0.302
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
significant impact.
HT = 0.207 + 11.6 Probing Time
Constant 0.20708 0.06849 3.02 0.003
Probing Time 11.6445 0.2294 50.76 0.000
S = 0.396975 R-Sq = 92.3% R-Sq(adj) = 92.3%
Source DF SS MS F P
Regression 1 406.00 406.00 2576.31 0.000
Total 216 439.88
Inference:- Since P valus is <.05 and R-
Sq>62%, which means there is strong
impact.
Regression Analysis: HT versus Probing Time

ANALYZE
Graphical Depiction
Chi-Square = 2.40 DF = 2 P = 0.302
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
significant impact.
HT = 2.52 + 0.523 Resolution Time
Constant 2.5239 0.2203 11.46 0.000
Resolution Time 0.5232 0.1188 4.40 0.000
S = 1.36995 R-Sq = 8.3% R-Sq(adj) = 7.8%
Source DF SS MS F P
Regression 1 36.377 36.377 19.38 0.000
Total 216 439.882
Inference:- Since R-Sq is 8.3, which
means there is weak impact between
HT and Resolution time.
Regression Analysis: HT versus Resolution Time

Hypothesis Summary
ANALYZE
Summary of Impacting Factors
S. No. Factor p – Value Graphical Tool Used Inference Next Steps
1 Handling Time .0003 Box Plot Ha Improve
2 Case Type .5016 Box Plot Ho -
3 Sub Query
0.171
Box Plot H0 -
4 Product(Policy Type)
0.415
Box Plot H0 -
5 Supervisor
0.0000
Box plot Ha Improve
6 Shift .302 Box Plot Ho -
7 Gender
0.000
Box Plot Ha Improve
8 Tenure
0.000
Scatter Ha Improve
9 Hold Time
0.000
Scatter Ha Improve
10 Probing Time
0.000
Scatter Ha Improve
11 Resolution Time
0.000
Scatter Ha Improve

Improve Deliverables
IMPROVE
Screening of the Impacting Factors
Action Plan for Improving the Factors
Basic Analysis of Improved Y
Pre–Post Analysis of Project Y
Pre-Post Analysis of Factor(s)
Improve Summary – Take Aways
FMEA on Action Plan

Screening of Impacting Factors
IMPROVE
To Improve
AHT
(A)
Customer
Importance
(B)
Expected
total
project cost
(C)
Likelihood
of the
success
(D)
Expected
contributio
n to profit
(E)
Applicabali
ty to other
areas
(F)
Project
priority
number
(G)
Project
Order
Supervisor 7 9 9 7 5 19,845 2
Gender 5 3 7 8 9 7560 6
Tenure 7 2 9 9 7 7938 5
Hold Time 9 7 8 7 8 28,224 1
Probing Time 7 7 5 7 8 13,720 4
Resolution Time 8 5 8 5 9 14,400 3

Action Plan for Improving the Factors
IMPROVE
S. No. Pain Area Root Cause Improvement Idea
Implementation
Owner
Implementation
Status
1. Supervisor Lacking Skills
Should have trained on
basic skills before moving
anyone to people manager
position.
Process HR Pending
2. Gender Training
Need to improve interview
process so hiring should
be done based on skills not
on gender biasness.
HR/Manager Pending
3. Tenure Ramp Up
Need to increase Ramp
time for fresh hires, so they
can expertise in the
product.
Training Team Pending
4. Hold Time System
Speed up the device with
regular maintenance,
IT Team Pending
5. Probing Time Support
Need to provide extra
support and session.
TL/SMEs Pending
6. Resolution Time Checklist
There should be checklist
template to provide
resolution timely
SMEs Pending

FMEA for Action Plan
IMPROVE
FAILURE MODE AND EFFECT ANALYSIS
Process Step Failure mode Effect on EDR
Severity
Occurrence
Detection
RPN
Risk management
strategy
Risk treatment plan
Responsibility
End
date
Residual Risk
Severity
Occurrence
Detection
RPN
S*O*D (RMS) (RTP) S*O*D

IMPROVE
Randomness Study
Normality Study
Basic Data Analysis of Improved Y

IMPROVE
Spread Study
Spread & Central Tendency Study
Central Tendency Study
Basic Data Analysis of Improved Y

Goal Validation of Y
IMPROVE
Graphical Depiction
Statistical Validation of Improvement
Hypothesis Result
Inference:

Pre – Post Analysis of Project Y
IMPROVE
Graphical Depiction
Hypothesis Result
Inference:
Optional

Pre – Post Analysis of Factor
IMPROVE
Graphical Depiction
Hypothesis Result
Inference:
Optional

Improve Summary – Take Away
IMPROVE

Control Deliverables
CONTROL
Control Plan & FMEA on Control Plan
Time Series Study of Y – Pre & Post
Control Charts & Inference for Y – Pre & Post
Basic Analysis of Improved Y
Establish Process Capability
Control Charts & Inference (for X1, X2, X3…)
Cost Benefit Analysis and Sign Off

Control Plan & FMEA on Control Plan
CONTROL
What’s Controlled Goal/Spec Limits Control Method
Who/What
Measures
Where Recorded
Decision Rule /
Corrective Action
SOP

Time Series Study of Y – Pre & Post
CONTROL
Need Based

Control Charts & Inference for Y – Pre & Post
CONTROL

CONTROL
Randomness Study
Normality Study
Basic Data Analysis of Controlled Y

CONTROL
Spread Study
Spread & Central Tendency Study
Central Tendency Study
Basic Data Analysis of Controlled Y

Establish Process Capability
CONTROL
Process Capability – Post Implementation
Z Bench
(Long Term Sigma)
Short Term Sigma
(Long Term Sigma + 1.5)

Control Charts & Inference (x1)
CONTROL

Control Charts & Inference (x2)
CONTROL

Cost Benefit Analysis & Sign Off
CONTROL
Benefit Source Unit Benefit Units Impacted Total Benefit
Cost Reduction
Enhanced Revenues
Labor Reduction
Decreased Overhead
COPQ Reduction

Stakeholder Analysis
Want to see More Of Want to see Less Of
Need Based

Advance Innovation Group
www.advanceinnovationgroup.com
F-39, Sector 6
Noida, UP – 201301
India
Advance Innovation Group
3 continents. One team.
AIG is headquartered in Boston, Massachusetts and maintains several consulting and training delivery centers across Asia Pacific including India. Asia Pacific operations is headquartered at
Noida, India with several offices and training facilities.
Global offices allow us closer client contact to better serve your needs, while enriching our services with global perspective and experience.

Reduce AHT Through DMAIC Process

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Reduce AHT Through DMAIC Process

Similar to Reduce AHT Through DMAIC Process (20)

Recently uploaded

Recently uploaded (20)

Reduce AHT Through DMAIC Process