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
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
usage, domain experiences, requirements skills and experiences with methods and tools used,
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