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- 1. 1 Experience Predictability in Software Project Delivery Pranabendu Bhattacharyya 27th September 2013
- 2. 2Experience Predictability in Software Project Delivery Agenda • Section 1: Introduction to Estimation Predictability – The need – Challenges • Section 2: Estimation Approach – Overall approach – Estimation Framework – Model Selection – Continuous Improvement • Section 3: Case Study – Scenario, Gaps, TCS Approach, Decision Matrix, Solution, Results
- 3. 3 If collective estimation accuracy can be increased even by a minimal percentage, it will translate to savings of multi-billion dollars Experience Predictability in Software Project Delivery The Need for Predictable Estimates Quality Cost Schedule Profit Budget Productivity $3.6 Trillion 66% 50% Binding Force of Estimation along with the parameters – The overall Software Spend (Source Gartner) – IT projects fail in US geography (Source Forrester) – IT projects Rolled Back (Source Gartner)
- 4. 4 • Limited reuse of past organizational experience in estimates Experience Predictability in Software Project Delivery Common Challenges and Gaps • Unavailability of standardized rules or guidelines defined for estimation • Unavailability of varied estimation techniques for different project types • Absence of defined guidelines to estimate the impact of different project specific characteristics • Practice of non repeatable methods even for the same technology or line of business • Inability to compare performance with respect to industry standards • Limited knowledge of estimation techniques and models • Absence of governance around estimation
- 5. 5Experience Predictability in Software Project Delivery Agenda • Section 1: Introduction to Estimation Predictability – The need – Challenges • Section 2: Estimation Approach – Overall approach – Estimation Framework – Model Selection – Continuous Improvement • Section 3: Case Study – Scenario, Gaps, TCS Approach, Decision Matrix, Solution, Results
- 6. 6Experience Predictability in Software Project Delivery Estimation Framework Sizing Techniques Utilization • Apply Framework suggested models • Define Metrics for measurement and bench mark • Collect feedback and lessons learnt Measurement and continuous feedback driving Framework improvement Schedule Techniques Cost Techniques Effort Techniques AM Models (Support Model, CR Model etc.) AD Models Assurance Models Package Models (Oracle Apps, SAP etc.) Estimation Approach Standardized Model Selection
- 7. 7 An estimation framework is a collection of well defined components based on best practices ensuring consistent outputs • Experience Predictability in Software Project Delivery Estimation Framework – Driving Standardization • Size Estimator: Quantifies “work volume” of a given scope • Effort Estimator: Derives the person-hours for scope implementation • Schedule Calculator: Develops project schedule based on estimated effort • Phase-wise Distributor: Apportions overall efforts and schedule across phases based on SDLC type • FTE Calculator: Computes Full Time Equivalents based on effort & schedule • Cost Calculator: Derives the overall project cost based on staffing and logistics • Governance Umbrella: Ensures estimates are reviewed & vetted • Feedback Adaptor: Captures actuals and lessons learnt to refine framework
- 8. 8Experience Predictability in Software Project Delivery • The TCS estimation framework is accessorized by a “Multi Dimensional Decision Matrix” which enables “FIRST TIME RIGHT” model selection. Model Selection - Driving Accuracy • “Decision Matrix” enabler consists of the following four dimensions: - Estimation Stage - Technology area and platform - Project Type - Software Life Cycle Used • Based on the model, framework selects organizational baseline productivity • Based on the decision matrix, the framework performs the following: - Determines the applicable components of the framework - Determines the specific methodology/ technique that would be applicable to each chosen framework component - Suggests the best fit model based on the organizational history
- 9. 9Experience Predictability in Software Project Delivery • Benchmark Productivity with Industry standards • Scale effectiveness of estimation models • Perform Causal Analysis for outliers • Identify levers for productivity improvement • Cross-pollination of best practices • Refine Estimation models • Implement Causal analysis findings Compute • Productivity for various tech-stack/platforms • Estimation Variance of different estimation models • Other related delivery metrics Plan process for • Collection of Actual Data from closed projects at regular cycles • Feedback from Users on estimation challenges faced, best practices involved Plan Do CheckAct Continuous Feedback - Driving Improvement
- 10. 10Experience Predictability in Software Project Delivery Agenda • Section 1: Introduction to Estimation Predictability – The need – Challenges • Section 2: Estimation Approach – Overall approach – Estimation Framework – Model Selection – Continuous Improvement • Section 3: Case Study – Scenario, Gaps, TCS Approach, Decision Matrix, Solution, Results
- 11. 11Experience Predictability in Software Project Delivery • Most of the projects incurred regular cost and effort overrun (~150%-200%) • Increased project management efforts (>40%) due to poor estimates/re- estimates • Lack of delivery predictability resulting in scrapping of projects amounting to millions of dollars of recurring losses • Huge expenditure due to induction of resources at higher rates at later stages of the projects to complete them on time The Scenario Existing Challenges at a Large US Financial Corporation • Poor Return On Investments (ROI) • Dissatisfied clients • No vendor performance comparison to augment outsourcing • Difficult decision-making for the right investment opportunities • No scope of validation of the estimates prepared by project teams The Consequences…
- 12. 12 Applied the proven four phased approach for process improvement Experience Predictability in Software Project Delivery TCS Solution Approach 1. Determine 2. Design & Develop 3. Deploy 4. Deliver Identify the gaps and plan accordingly Tailor, pilot and setup an Estimation Framework to establish processes and estimation techniques aligned to the needs Integrate solution with existing organizational processes Demonstrate estimation effectiveness through KPIs
- 13. 13 All the framework components like “Size” etc. were adopted to instantiate best fit estimation models for relevant project types Experience Predictability in Software Project Delivery Design and Develop: TCS Solution Implementation approach Parameter 1 Project Type Parameter 2 Technology Parameter nParameter 4 Stage Parameter 3 SDLC Type Size Estimator Technique S1 Technique S2 Technique Sn . . . Effort Estimator Technique E1 Technique E2 Technique En . . . Schedule Estimator Technique T1 Technique T2 Technique Tn . . . Cost Estimator Technique P1 Technique P2 Technique Pn . . . . . . P1 T2 E4 S1 HistoricalData S1 S5 S1 S2 S4 S5 S1 S2 S5 S1 S5 E4E4 E1 E3 E4 E1 E4 E4 . . . . . . T2T2 T1 T2 T5 T1 T2 T5 T2 T5 . . . P1 P5 P1 P5 P1 P3 P5 P1 P3 P5 P1 P5 . . . S1 S5 TheCustomEstimationModel
- 14. 14 • Improved predictability of project costs and schedules • Measured and base-lined productivity levels • Reduced cost of estimation/re-estimation, idle time, unplanned induction of staff, project scraps and so on • Created repository of historical estimation data • Established estimation traceability to business requirements • Improved quantitative risk analysis resulting in higher estimation confidence • Provisioned for fact based inputs aiding vendor bid negotiations • Measured scope creep at different stages of projects Experience Predictability in Software Project Delivery Deploy & Deliver • Built solution awareness within the practitioner community • Handheld projects for effective change management Solution Deployment Results Delivery
- 15. 15 Y-o-Y Improvement in productivityImprovement in Scrap Value Reduction • Reduced cost/function point (by 41%) for web based projects • Reduced cost/function point (by 15%) for mainframe projects Experience Predictability in Software Project Delivery 556 592 541 523 218 142 0 100 200 300 400 500 600 700 Scrapvalue (millionUSD) 0.041 0.045 0.061 0.065 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Year 0 Year 1 Year 2 Year 3 CustomerProductivityin FP/PH 54.70% 62.30% 82.90% 23.40% 36.50% 55.30% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% Year 1 Year 2 Year 3 20% band 10% band Model Effectiveness Analysis 2 variance bands (+20% & +10%) were defined for Model Effectiveness Analysis • Year 1: 3 Models were used, 26% Coverage • Year 2: 2 New Models were introduced along with 3 existing, 55% Coverage • Year 3: Coverage 80% Stats Tangible Benefits Realized
- 16. 16 The Key takeaways Presentation Title One of the critical parameters of bringing about certainty in uncertain times is estimation predictability. This is possible by leveraging the robust, standard yet flexible estimation framework which enables Project Managers to : • Harness the estimation experience of executed projects to bring in the desired predictability. • Provide feedback for the improvements with further refinements • Generate key metrics like variance, productivity, schedule & effort slippage • Get the “best fit” estimation prescription applicable for different types of projects based on parameter analysis
- 17. 17 Author profiles Presentation Title Pranabendu Bhattacharyya (CFPS,PMP) has more than 20 years of IT experience and heading the TCS estimation Center of Excellence for last 8 years. He is an M-Tech (IIT KGP) and has been the chief consultant for many estimation consulting engagements. He is one of the core members of ITPC (IFPUG) guiding committee and presented paper in various international colloquiums. Sanghamitra GhoshBasu has 13 years of experience in software delivery and project management. She has around 9 years of experience in software estimation and has been instrumental in defining, developing and deploying estimation models for multiple engagement types Parag Saha has over 15 years of industry experience spanning multiple domains including Transportation, Government, Insurance and Telecom- RAFM. He is currently part of the Estimation Center of Excellence in TCS and has been involved in defining and refining estimation models and in deployment of these standardized models across multiple domains in TCS.
- 18. 18 Thank You Contact: pranabendu.bhattacharyya@tcs.com Experience Predictability in Software Project Delivery