Implementing Level 5 Metrics Programme @ Capgemini Netherlands
Download as PPTX, PDF3 likes1,367 views
The document discusses the implementation of a Level 5 metrics program at Capgemini Netherlands, focusing on leading and lagging indicators for project measurement. It outlines various factors affecting application development and maintenance performances, including cost, quality, schedule, and specific measurement criteria for key performance indicators (KPIs). Additionally, it emphasizes the importance of statistical validation for identified factors influencing project outcomes.
Implementing Level 5 Metrics Programme @ Capgemini Netherlands
- 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 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
- 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.