As Capgemini, as part of the CMMI ML5 program, further enhances systematic measurement programs to monitor, control and improve process and product quality, they also keep in mind the right tradeoff between benefit and cost to achieve this. The presentation emphasizes on selection of the right set of X (leading) and Y (Lagging) factors distributed over Cost, Time and Quality and trade off between them. Every engagement has an end goal that needs to be achieved within a certain budget, with available resources and within a given timeline. In an perfect world, an engagement manager would have as much time, money and resources needed to achieve the highest-quality result; however, this is seldom the case in reality. Think of the three factors – cost, quality and time – as legs of a stool drifting away from each other. When the X and Y factors are identified it gives the engagement manager the opportunity of making an informed decision in selecting the right delivery strategy.

1. Together. Free your energies
Implementing a level 5 metrics programme @Capgemini Netherlands
Selecting The Right Set...
Niteen Kumar - 26/11/2013

2. MEASUREMENT
OBJECTIVE
SCOPE
LEADING &
LAGGING
INDICATORS
LAGGING
INDICATORS
SPECIFIC
MEASUREABLE
Are the measurement target
oriented?
Can it be measured?
RELEVANT
What story will it tell?
ATTAINABLE
Does it cost too much?
TIME BOUND
By When?
Capgemini Leading and Lagging Indicators – NESMA Presentation
Niteen Kumar
2

3. MEASUREMENT
OBJECTIVE
SCOPE
LEADING &
LAGGING
INDICATORS
REGRESSION
EQUATION
APPLICATION DEVELOPMENT KPI PORTFOLIO
COST
QUALITY
SCHEDULE
APPLICATION MAINTENANCE KPI PORTFOLIO
Capgemini Leading and Lagging Indicators – NESMA Presentation
Niteen Kumar
3

4. MEASUREMENT
OBJECTIVE
SCOPE
LEADING &
LAGGING
INDICATORS
REGRESSION
EQUATION
LIST OF X FACTORS
code complexity, encapsulation, program language & tools, code review checklist, coding skills and experiences with the program
languages and tools used, code review skills and experiences, %
of tickets having existing solution in
KEDB, quality of reused source code, requirements volatility, integration test methods and tools, Integration test skills
and experiences with methods and tools used, quality of reused test cases ,domain, requirements volatility, quality
attributes, readability of documents, architecture measures, code complexity, encapsulation, requirements methods and
tools, Right shore Ratio requirements inspection checklist, high-level design methods and tools, high-level design
inspection checklist, detailed design methods and tools, detailed design review/inspection checklist, program language &
tools, code review checklist usage, domain experiences, requirements skills and experiences with methods and tools
used, requirement
inspection skills and experiences, high-level design skills and experiences with the methods and
tools used, high-level design inspection skills and experiences, detailed de-sign skills and experiences with the methods
and tools used, detailed design review/inspection skills and experiences, # of CR’s Rolled Back, coding skills
domain, architecture measures, high-level design methods and tools, high-level design skills and experiences with methods and
tools used, quality of reused high-level design documents, Rework
Effort
Capgemini Leading and Lagging Indicators – NESMA Presentation
Niteen Kumar
4

5. MEASUREMENT
OBJECTIVE
SCOPE
DEVELOPMENT
PROJECT –
INDICATORS
REGRESSION
EQUATION
APPLICATION DEVELOPMENT KPI PORTFOLIO
COST
Y - FACTORS
 % EFFORT VARIANCE
 CONTRIBUTION MARGIN
X - FACTORS
 Requirements Volatility
 Skill Index
 Reusability
 Effort by SDLC Phase
 Review , Rework Effort
 Resource Cost
 Code Complexity / Quality
 Overrun / Underrun
 # of times resource
changed during build
QUALITY
Y - FACTORS
 DEFECT REMOVAL
EFFICIENCY
 DELIVERED DEFECT
DENSITY
 COST OF QUALITY
X - FACTORS
SCHEDULE
Y - FACTORS
 % SCHEDULE VARIANCE
X - FACTORS
 Resource Availability
 Requirements Volatility
 Skill Index
 Reusability
 Rework Effort
 # of times resource
changed during build
The “X” factors
influencing the
outcome of “Y” was
identified during the
workshops.
The identified “X”
factors are logical in
nature and may
change during
statistical validation
 Rework Effort
 Test Coverage
 Testing Rate
 Review Effort
 Skill Level
 Code Complexity / Quality
 Test Preparation Effort
Capgemini Leading and Lagging Indicators – NESMA Presentation
Niteen Kumar
5

6. MEASUREMENT
OBJECTIVE
SCOPE
DEVELOPMENT
PROJECT –
INDICATORS
REGRESSION
EQUATION
APPLICATION DEVELOPMENT KPI PORTFOLIO
SPRINT
SPRINT
NUMBER
i
Total # of
Features /
Use Case
Planned
i
Estimated
Size (SP)
i
COST
Planned
Actual Size Productivity
%
(SP)
Factor
Completion
i
i
i
Total
Planned
Effort
(P.Hrs)
i
TOTAL AT ENGAGEMENT LEVEL g
Total # of
User
Stories
Total
Total
Total Actual MODIFIED Features /
Features / Overall Effort
Effort
During the Use Case
Use Case Variance in
(P.Hrs)
Iteration. COMPLETED ACCEPTED
%
i
i
i
i
i
ACTUAL EFFORT For Below Activities (Person Hours)
P.Hrs
902,00 1985,00 2177,00 1561,00 1153,00 1412,00 1805,00 1089,00 1478,00 1498,00
7935,00
174,00
628,00
534,00
h
h
h
i
MODL
COD
TST-P
TST-E
REFTR
SCRUM
MASTER
REV
REW
h
g
g
g
g
g
38,00
g
g
g
63,00
7,00
56,00
70,00
4,00
2,00
19,00
25,00
10,00
25,00
5,00
223,00
4
7
6
-
67,00
9,00
27,00
48,00
45,00
22,00
27,00
23,00
18,00
22,00
2,00
243,00
0
12
8
263%
120,00
11,00
12,00
19,00
27,00
19,00
16,00
32,00
26,00
21,00
21,00
204,00
5
9
10
70%
i
i
i
i
i
i
h
SCOPE &
REFTR -1
g
g
g
g
g
50,00
25,00
1
8
139
152
7
60
245,00
2
12
200
175
8
100
3
9
150
130
7,5
100
SCOPE DSGN
QUALITY DETAILS
Total Number Of
Planned Test
Cases
i
Total Number Of
Total Number Of
Test Cases
Total Number Of
EXTERNAL
Executed
INTERNAL Defects
Defects
i
i
i
QUALITY
DOD Performed
i
% DOD Steps
Performed
i
Total # Of
Impediments
Reported
i
Total # Of
Impediments
Removed
i
Defect Removal
Effeciency (%)
i
1289
1084
1443
472
46
-
284
163
h
h
h
h
h
h
h
h
i
28
20
17
9
YES
70
4
3
65%
34
30
21
16
NO
100
7
7
50%
12
12
25
12
YES
100
3
3
50%
Capgemini Leading and Lagging Indicators – NESMA Presentation
Niteen Kumar
6

7. MEASUREMENT
OBJECTIVE
SCOPE
MAINTENANCE
ENGAGEMENT
INDICATORS
REGRESSION
EQUATION
APPLICATION MAINTENANCE KPI PORTFOLIO
COST
Y - FACTORS
 % EFFORT VARIANCE FOR
KT and RELEASE
 PRODCTIVITY (AET)
 % BACKLOG OF TICKET
 CONTRIBUTION MARGIN
X - FACTORS
 Idle Time (under discussion)
 Resource Cost
 Right shore Ratio
 Skill Index
 Effort Spent on KT
 % of tickets having existing
solution in KEDB
 # of modules reoccurring
impacted
 System Downtime
 % Additional Work
QUALITY
Y - FACTORS
 % INCIDENT REDUCTION
 % FIRST TIME PASS
 % OF SYSTEM S’FULLY
TRANSITIONED DURING KT
STAGE
 DEFECT REMOVAL
EFFICIENCY FOR RELEASE
 DELIVERED DEFECT
DENSITY FOR RELEASE
 COST OF QUALITY
X - FACTORS
 Rework Effort
 Test Coverage
 Testing Rate
 Test Preparation Effort
 System Downtime
 # of CR’s Rolled Back
 % RCA Compliance
 # of reoccurring modules
impacted
SCHEDULE
Y - FACTORS
 % SCHEDULE VARIANCE
FOR KT PHASE
 % RESPONSE &
RESOLUTION COMPLIANCE
 % SCHEDULE VARIANCE
FOR RELEASE
The “X” factors
influencing the
outcome of “Y” was
identified during the
workshops.
X - FACTORS
 Resource Availability
 Skill Index
 Reusability
 Rework Effort
 % of tickets having existing
solution in KEDB
 Elapsed Time to Assign /
Investigate / Testing /
Implementation Per Ticket
 # of times incident/service
request assigned within and
between teams
Capgemini Leading and Lagging Indicators – NESMA Presentation
The identified “X”
factors are logical in
nature and may
change during
statistical validation
Niteen Kumar
7

8. MEASUREMENT
OBJECTIVE
SCOPE
INDICATORS
MAINTENANCE
PROJECT
REGRESSION
EQUATION
APPLICATION MAINTENANCE KPI PORTFOLIO
INCIDENT / PROBLEM MANAGEMENT - LEADING & LAGGING INDICATORS
5811
Reporting
Month
Priority
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
Jun-13
P0
P1
P2
P3
P4
P5
S0
S1
S2
S3
S4
S5
926
5895
Tickets
Backlog
Number of
Received Tickets of
Tickets
Current
Previous
Resolved
Month
Month
11
13
807
44
1
4
132
35
12
17
845
47
2
10
437
53
1
2
82
46
3
10
475
52
842
20545
3
Number of
Backlog
Tickets
Effort Spent
in Closing
The Ticket
P. Hours
Average
Effort per
Ticket
0
0
94
32
0
2
44
47
-
29,75
40,5
2552
216
4
1134
259
2,48
2,38
3,02
4,60
0,00
0,40
2,39
4,98
-
64
98,91
263
95,54
# of
# of
% SLA
% SLA
Response
Resolution
Compliance
Compliance
Breach
Breach
2
3
5
1
0
6
0
0
83,33
82,35
99,41
97,87
100,00
40,00
100,00
100,00
-
0
0
45
0
0
2
55
0
100,00
100,00
94,67
100,00
100,00
80,00
88,42
100,00
-
Capgemini Leading and Lagging Indicators – NESMA Presentation
0
FTR
0
-
Average
Elapsed Time
To Closure
H:MM:S
1:30:22
0:33:00
0:02:00
0:02:00
4:54:00
11:33:00
30:10:00
149:38:00
0:05:00
43:34:00
0:02:00
0:16:00
%First Time
Right
Average
Elapsed Time
To Assign
Ticket
Hours : Mins:
Sec
10:56:00
52:14:00
24:25:00
169:45:00
Niteen Kumar
8

9. MEASUREMENT
OBJECTIVE
SCOPE
INDICATORS
MAINTENANCE
PROJECT
REGRESSION
EQUATION
Example: Delivered Defect Density
*DDD = 3.0-0.05*RAE-0.06*TPE – 0.025CRE
RAE = Requirement Analysis Effort
TPE = Test Preperation Effort
CRE = Code Review Effort
* Example
Capgemini Leading and Lagging Indicators – NESMA Presentation
Niteen Kumar
9

10. About Capgemini
With more than 120,000 people in 40 countries, Capgemini
is one of the world's foremost providers of
consulting, technology and outsourcing services. The Group
reported 2011 global revenues of EUR 9.7 billion.
Together with its clients, Capgemini creates and delivers
business and technology solutions that fit their needs and
drive the results they want. A deeply multicultural
organization, Capgemini has developed its own way of
working, the Collaborative Business ExperienceTM, and
draws on Rightshore ®, its worldwide delivery model.
Rightshore® is a trademark belonging to Capgemini
www.capgemini.com
The information contained in this presentation is proprietary.
© 2012 Capgemini. All rights reserved.