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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.
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Together. Free your energies
Implementing a level 5 metrics programme @Capgemini Netherlands Selecting The Right Set...Niteen Kumar - 26/11/2013
2 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE LEADING & LAGGING
INDICATORS
LAGGING INDICATORS
SPECIFIC MEASUREABLE ATTAINABLE
RELEVANT TIME BOUND
Are the measurement target oriented?
Can it be measured? Does it cost too much?
What story will it tell? By When?
3 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE LEADING & LAGGING
INDICATORS
REGRESSION EQUATION
COST QUALITY SCHEDULE
APPLICATION DEVELOPMENT KPI PORTFOLIO
APPLICATION MAINTENANCE KPI PORTFOLIO
4 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
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
5 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE DEVELOPMENTPROJECT –
INDICATORS
REGRESSION EQUATION
CONTRIBUTION MARGIN DELIVERED DEFECT DENSITY
COST QUALITY SCHEDULE
COST OF QUALITYX - FACTORS
X - FACTORS
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
Rework Effort Test Coverage Testing Rate Review Effort Skill Level Code Complexity / Quality Test Preparation Effort
Resource Availability Requirements Volatility Skill Index Reusability Rework Effort # of times resource changed during build
Y - FACTORS
% EFFORT VARIANCE
Y - FACTORS
DEFECT REMOVAL EFFICIENCY
Y - FACTORS
% SCHEDULE VARIANCE 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
APPLICATION DEVELOPMENT KPI PORTFOLIO
6 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE DEVELOPMENTPROJECT –
INDICATORS
REGRESSION EQUATION
APPLICATION DEVELOPMENT KPI PORTFOLIO
SPRINT COST
SPRINT NUMBER
i
Total # ofFeatures / Use Case Planned
i
Estimated Size (SP)
i
Actual Size (SP) i
Planned Productivity
Factori
% Completion
i
Total Planned
Effort(P.Hrs)
i
ACTUAL EFFORT For Below Activities (Person Hours)P.Hrs
Total Actual Effort
(P.Hrs)i
Total # of User
Stories MODIFIEDDuring the Iteration.
i
TotalFeatures / Use Case
COMPLETEDi
TotalFeatures / Use Case
ACCEPTEDi
Overall Effort Variance in
%i
TOTAL AT ENGAGEMENT LEVEL g 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
i i i i i i h SCOPE DSGN MODL COD TST-P TST-E REFTRSCRUMMASTER
REV REW h h h h i
SCOPE & REFTR -1
g g g g g 50,00 25,00 g g g g g 38,00 g g g 63,00
1 8 139 152 7 60 245,00 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 -
2 12 200 175 8 100 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%
3 9 150 130 7,5 100 120,00 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%
QUALITY DETAILS QUALITY
Total Number Of Planned Test
Casesi
Total Number Of Test Cases Executed
i
Total Number Of INTERNAL Defects
i
Total Number Of EXTERNAL
Defectsi
DOD Performedi
% DOD Steps Performed
i
Total # Of Impediments
Reportedi
Total # Of Impediments
Removedi
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%
7 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE MAINTENANCEENGAGEMENT
INDICATORS
REGRESSION EQUATION
APPLICATION MAINTENANCE KPI PORTFOLIO
CONTRIBUTION MARGIN
DEFECT REMOVAL EFFICIENCY FOR RELEASE DELIVERED DEFECT DENSITY FOR RELEASE
COST QUALITY SCHEDULE
COST OF QUALITYX - FACTORS
X - FACTORS
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
Rework Effort Test Coverage Testing Rate Test Preparation Effort System Downtime # of CR’s Rolled Back % RCA Compliance # of reoccurring modules impacted
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
Y - FACTORS
% EFFORT VARIANCE FOR
KT and RELEASE
PRODCTIVITY (AET)
% BACKLOG OF TICKET
Y - FACTORS
% INCIDENT REDUCTION % FIRST TIME PASS % OF SYSTEM S’FULLY TRANSITIONED DURING KT STAGE
Y - FACTORS
% SCHEDULE VARIANCE FOR KT PHASE
% RESPONSE & RESOLUTION COMPLIANCE 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
% SCHEDULE VARIANCE FOR RELEASE
8 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE INDICATORSMAINTENANCE
PROJECT
REGRESSION EQUATION
APPLICATION MAINTENANCE KPI PORTFOLIO
INCIDENT / PROBLEM MANAGEMENT - LEADING & LAGGING INDICATORS
5811 926 5895 842 20545 3 64 98,91 263 95,54 0 0
Reporting Month
Priority
Tickets Received Current Month
Backlog Tickets of Previous
Month
Number of Tickets
Resolved
Number of Backlog Tickets
Effort Spent in Closing The TicketP. Hours
Average Effort per
Ticket
# of Response
Breach
% SLA Compliance
# of Resolution
Breach
% SLA Compliance
FTR %First Time Right
AverageElapsed Time To
Assign TicketHours : Mins:
Sec
AverageElapsed Time
To ClosureH:MM:S
Jun-13 P0 - - - - - Jun-13 P1 11 1 12 0 29,75 2,48 2 83,33 0 100,00 - 1:30:22 4:54:00Jun-13 P2 13 4 17 0 40,5 2,38 3 82,35 0 100,00 - 0:33:00 11:33:00Jun-13 P3 807 132 845 94 2552 3,02 5 99,41 45 94,67 - 0:02:00 30:10:00Jun-13 P4 44 35 47 32 216 4,60 1 97,87 0 100,00 - 0:02:00 149:38:00Jun-13 P5 - - - - - Jun-13 S0 - - - - - Jun-13 S1 2 1 3 0 0,00 0 100,00 0 100,00 - 0:05:00 10:56:00Jun-13 S2 10 2 10 2 4 0,40 6 40,00 2 80,00 - 43:34:00 52:14:00Jun-13 S3 437 82 475 44 1134 2,39 0 100,00 55 88,42 - 0:02:00 24:25:00Jun-13 S4 53 46 52 47 259 4,98 0 100,00 0 100,00 - 0:16:00 169:45:00Jun-13 S5 - - - - -
9 Capgemini Leading and Lagging Indicators – NESMA Presentation Niteen Kumar
MEASUREMENT OBJECTIVE
SCOPE INDICATORSMAINTENANCE
PROJECT
REGRESSION EQUATION
Example: Delivered Defect Density
RAE = Requirement Analysis EffortTPE = Test Preperation EffortCRE = Code Review Effort
* Example
*DDD = 3.0-0.05*RAE-0.06*TPE – 0.025CRE
The information contained in this presentation is proprietary.© 2012 Capgemini. All rights reserved.
www.capgemini.com
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
