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CMMI High Maturity Best Practices HMBP 2010: Different Flavors Of PPMs by S.Sugavaneswaran
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CMMI High Maturity Best Practices HMBP 2010: Different Flavors Of PPMs by S.Sugavaneswaran

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Different Flavors Of PPMs …

Different Flavors Of PPMs
-S.Sugavaneswaran
Sonata Software Ltd.
presented at
1st International Colloquium on CMMI High Maturity Best Practices held on May 21, 2010, organized by QA

Published in Technology , Business
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  • 1. Different Flavors Of PPMs -S.Sugavaneswaran Sonata Software Ltd.
  • 2. Different Flavors of PPMs Presented at HMBP 2010 S.Sugavaneswaran Sonata Software Limited 21-May-10 www.sonata-software.com
  • 3. Agenda • About Process Performance Models • High maturity enablers • Challenges faced in implementation • Flavors of PPM • How good they are 3
  • 4. Need for PPM Adapted from the SEI paper “An Executive Tutorial of CMMI Process Performance Models” • An Earned Value Management dashboard • How effective is such a report in terms of triggering process improvement actions? • Will it help to know which controllable process factors influence the above outcomes? 4
  • 5. Process Performance Models “Delighting customers is what it’s all about, and that comes from consistent, end-to-end process performance.” – Kevin Weiss • Relate controllable factors to an outcome o Y=f(x1,x2,x3…) • Developed from historical data • Predict results achieved by following a process • With a known confidence level • Help perform “What-if” analysis • Compose processes for a project 5
  • 6. Our Context • IT Consulting and Services company • Customers across US, Europe, Middle East and APAC • Services offered • Product Engineering Services • Application Development/ Management • Managed Testing • Infrastructure Management • Quality standards adaptation • ISO 9001 • CMM Level 5 • CMMI v1.2 Level 3 • ISO 20000-1 6
  • 7. High Maturity Enablers • Standardizing size measures for projects • To normalize process performance • Enabling sub-process level control • Effort to create, review and rework • Options for each sub-process • Data at the sub-process option level • Capturing defect injection and detection 7
  • 8. Implementation Challenges “The truth is that you always know the right thing to do. The hard part is really doing it.” – H. Norman Schwarzkopf • Stakeholder buy-in • Issues with data availability / stability • Tool enablement constraints • Continued involvement of practitioners 8
  • 9. PPM – Healthy Ingredients 1. Statistical or probabilistic in nature 2. Predict interim and/or final project outcomes 3. Use controllable factors tied to sub-processes 4. Model the variation of predictive factors to forecast outcome variations 5. “What-if” analysis for project planning/re-planning 6. Connect upstream with downstream activities 7. Enable mid-course corrections 9
  • 10. PPM Flavors “All models are wrong, some are useful!” – George Box • Development project – Continuous simulation • Sub-process wise process performance • Prediction with confidence levels • “What-if” analysis • Production support – Discrete event simulation • Process flow depiction and simulation • Analysis of • SLA adherence • Resource utilization 10
  • 11. Flavor 1 • About the project • New development (Agile) • Sprints & stories • Sprint content decided based on experience • Developers categorized by skill level • Model applied • Monte Carlo Simulation 11
  • 12. Simulation Highlights • Objective: To optimize number of stories forming part of a sprint • Predictive factors • Working hours per day • Number of stories • Sub-process wise productivity • Skill levels • Size of each story • Team size 12
  • 13. The Model • Inputs: Estimated story size and sub-process productivity distributions • In each simulation run, • The model chooses values from sub-process productivity distributions, arrives at effort • Predicted effort = Sum of all sub-process efforts • Effort computed is divided by the available man-hours per day, giving the elapsed days • Over time, a profile is built showing the distribution of likely outcomes (number of days) • Confidence level indicated for the output 13
  • 14. Scenarios Story Story Story Story Story Story Story Story Story Story 1 2 3 4 5 6 7 8 9 10 Size 30 12 80 2 6 Skill High High High Low High Understanding & 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Analysis Design 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Design Review 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Coding 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Code Review 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Code Fix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unit Test 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Units Test Fix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 FIT Testing 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 IT Fix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Table 1: Model before running the simulation 14
  • 15. Sample Predictions Scenario 1: Stories 1,2,3,4,5 and 6 Tool Output 1: Release Prediction – 6 Stories Scenario 2: Stories 1,2,3,5 and 6 Tool Output 2: Release Prediction – 5 Stories 15
  • 16. Process Control Sub-processes to be closely monitored: IT and Coding- High skill Tool Output 3: Sensitivity-Release Prediction 16
  • 17. Flavor 2 • About the project • Production Support • High volume, short turnaround work • SLA-driven • Different ticket priorities • Three different skill sets • Model applied • Discrete Event Simulation 17
  • 18. Simulation Highlights • Objectives: To forecast and manage SLA adherence and Resource utilization • Predictive factors • Team size • Response, analysis and development time • Arrival pattern of tickets (by priority) • Wait times 18
  • 19. The Process Model 19
  • 20. SLA Adherence Tool Output 4: SLA Miss before the model Tool Output 5: SLA Miss after the model Probability of SLA breach brought down
  • 21. Resource Utilization Tool Output 6: Before the model Tool Output 7: After the model Resource utilization improved as well 21
  • 22. Model Flavors vs Healthy Ingredients Ingredient Flavor 1 Flavor 2 Statistical, probabilistic… Yes Yes Predict interim/ final… Yes Yes Sub-process level factors Yes Yes Model uncertainty… Yes Yes Support “What-if” Yes Yes analysis Connect to downstream.. Yes Yes Enable course correction Yes Yes Table 2: Models vs Healthy Ingredients 22
  • 23. Conclusion “Action may not always bring happiness, but there is no happiness without action.” - Benjamin Disraeli 23
  • 24. Thank you Q&A Email: sesh@sonata-software.com www.sonata-software.com 24
  • 25. Click here for: High Maturity best practices HMBP 2010 Presentations organized by QAI Click here