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1
Experience Predictability in Software Project Delivery
Pranabendu Bhattacharyya27th September 2013
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
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
Estimation
Quality
Cost
Schedule
ProfitBudget
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
• 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
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
6Experience Predictability in Software Project Delivery
Decision Parameters
Estimation Stage
Engagement Type
TechnologyEstimation Framework
Sizing Techniques
Appropriate Model
Guideline
Process
Utilization
• Apply Framework suggested models
• Define Metrics for measurement and bench mark
• Collect feedback and lessons learnt
Measurement and continuous feedback driving
Framework improvement
ScheduleTechniques
CostTechniques
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
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
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
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
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
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 ScenarioExisting 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
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
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 1Project Type
Parameter 2Technology
Parameter nParameter 4Stage
Parameter 3SDLC Type
Size EstimatorTechnique S1Technique S2
Technique Sn
.
.
.
Effort Estimator
Technique E1Technique E2
Technique En
.
.
.
Schedule Estimator
Technique T1Technique T2
Technique Tn
.
.
.
Cost EstimatorTechnique P1Technique P2
Technique Pn
.
.
.
. . .P1
T2
E4
S1
His
toric
al D
ata
S1S5
S1S2S4S5
S1S2S5
S1S5
E4E4
E1E3E4
E1E4 E4
. . .
. . .
T2T2T1T2T5
T1T2T5
T2T5
. . .
P1P5
P1P5
P1P3P5
P1P3P5
P1P5
. . .
S1S5
The
Cust
om E
stim
ation
Mod
el
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
Y-o-Y Improvement in productivity Improvement 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
Year -1 Year 0Year 1
Year 2Year 3
Year 40
100
200
300
400
500
600
700
556592
541 523
218142S
crap
val
ue
(mill
ion
US
D)
Year 0 Year 1 Year 2 Year 30
0.010.020.030.040.050.060.07
0.041 0.045
0.061 0.065
Cust
omer
Pro
ducti
vity
in
FP/P
H
Year 1 Year 2 Year 30.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
54.70%62.30%
82.90%
23.40%
36.50%
55.30%
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
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
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.