SlideShare a Scribd company logo
1 of 18
1
Experience Predictability in Software Project Delivery
Pranabendu Bhattacharyya
27th 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
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
• 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
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
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 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
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 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
• 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 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
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.
18
Thank You
Contact: pranabendu.bhattacharyya@tcs.com
Experience Predictability in Software Project Delivery

More Related Content

What's hot

Creating QA Dashboard
Creating QA DashboardCreating QA Dashboard
Creating QA Dashboard
Petro Porchuk
 
Project Quality Management
 Project Quality Management Project Quality Management
Project Quality Management
Serdar Temiz
 
Quality metrics in project management
Quality metrics in project managementQuality metrics in project management
Quality metrics in project management
selinasimpson1601
 
Analyze phase lean six sigma tollgate template
Analyze phase   lean six sigma tollgate templateAnalyze phase   lean six sigma tollgate template
Analyze phase lean six sigma tollgate template
Steven Bonacorsi
 

What's hot (19)

Creating QA Dashboard
Creating QA DashboardCreating QA Dashboard
Creating QA Dashboard
 
Using the test process improvement models. Case study based on TPI Next model...
Using the test process improvement models. Case study based on TPI Next model...Using the test process improvement models. Case study based on TPI Next model...
Using the test process improvement models. Case study based on TPI Next model...
 
Estimation guidelines and templates
Estimation guidelines and templatesEstimation guidelines and templates
Estimation guidelines and templates
 
Project Quality Management
 Project Quality Management Project Quality Management
Project Quality Management
 
Six sigma project
Six sigma projectSix sigma project
Six sigma project
 
Risk-Based Testing for Agile Projects
Risk-Based Testing for Agile ProjectsRisk-Based Testing for Agile Projects
Risk-Based Testing for Agile Projects
 
Define Phase
Define PhaseDefine Phase
Define Phase
 
Project quality management
Project quality managementProject quality management
Project quality management
 
Quality metrics in project management
Quality metrics in project managementQuality metrics in project management
Quality metrics in project management
 
PMP Preparation - 08 Quality Management
PMP Preparation - 08 Quality ManagementPMP Preparation - 08 Quality Management
PMP Preparation - 08 Quality Management
 
Analyze phase lean six sigma tollgate template
Analyze phase   lean six sigma tollgate templateAnalyze phase   lean six sigma tollgate template
Analyze phase lean six sigma tollgate template
 
Webinar: Metrics Management - Be Careful What You Wish For!
Webinar: Metrics Management - Be Careful What You Wish For!Webinar: Metrics Management - Be Careful What You Wish For!
Webinar: Metrics Management - Be Careful What You Wish For!
 
Software Project Estimation
Software Project EstimationSoftware Project Estimation
Software Project Estimation
 
Drupalcon la estimation john_nollin
Drupalcon la estimation john_nollinDrupalcon la estimation john_nollin
Drupalcon la estimation john_nollin
 
Slides chapters 21-23
Slides chapters 21-23Slides chapters 21-23
Slides chapters 21-23
 
PMO and Tollgate Process
PMO and Tollgate Process PMO and Tollgate Process
PMO and Tollgate Process
 
Chap08 project quality management
Chap08 project quality  managementChap08 project quality  management
Chap08 project quality management
 
Risk based testing, Katerina Meshkova
Risk based testing, Katerina MeshkovaRisk based testing, Katerina Meshkova
Risk based testing, Katerina Meshkova
 
James Brodie - Outsourcing Partnership - Shared Perspectives
James Brodie - Outsourcing Partnership - Shared Perspectives James Brodie - Outsourcing Partnership - Shared Perspectives
James Brodie - Outsourcing Partnership - Shared Perspectives
 

Viewers also liked

Bhavesh pmi final
Bhavesh  pmi finalBhavesh  pmi final
Bhavesh pmi final
PMI2011
 
Day 1 1410 - 1455 - pearl 2 - vijay sane
Day 1   1410 - 1455 - pearl 2 - vijay saneDay 1   1410 - 1455 - pearl 2 - vijay sane
Day 1 1410 - 1455 - pearl 2 - vijay sane
PMI2011
 

Viewers also liked (8)

Bhavesh pmi final
Bhavesh  pmi finalBhavesh  pmi final
Bhavesh pmi final
 
LPM2
LPM2LPM2
LPM2
 
LPM6
LPM6LPM6
LPM6
 
DPPM4
DPPM4DPPM4
DPPM4
 
MCP3
MCP3MCP3
MCP3
 
LPM1
LPM1LPM1
LPM1
 
ETPM1
ETPM1ETPM1
ETPM1
 
Day 1 1410 - 1455 - pearl 2 - vijay sane
Day 1   1410 - 1455 - pearl 2 - vijay saneDay 1   1410 - 1455 - pearl 2 - vijay sane
Day 1 1410 - 1455 - pearl 2 - vijay sane
 

Similar to Day 1 1620 - 1705 - maple - pranabendu bhattacharyya

Downloads abc 2006 presentation downloads-ramesh_babu
Downloads abc 2006   presentation downloads-ramesh_babuDownloads abc 2006   presentation downloads-ramesh_babu
Downloads abc 2006 presentation downloads-ramesh_babu
Hem Rana
 
Software engineering lecture notes
Software engineering lecture notesSoftware engineering lecture notes
Software engineering lecture notes
Siva Ayyakutti
 

Similar to Day 1 1620 - 1705 - maple - pranabendu bhattacharyya (20)

Downloads abc 2006 presentation downloads-ramesh_babu
Downloads abc 2006   presentation downloads-ramesh_babuDownloads abc 2006   presentation downloads-ramesh_babu
Downloads abc 2006 presentation downloads-ramesh_babu
 
Using Benchmarking to Quantify the Benefits of Software Process Improvement
Using Benchmarking to Quantify the Benefits of Software Process ImprovementUsing Benchmarking to Quantify the Benefits of Software Process Improvement
Using Benchmarking to Quantify the Benefits of Software Process Improvement
 
Pressman ch-22-process-and-project-metrics
Pressman ch-22-process-and-project-metricsPressman ch-22-process-and-project-metrics
Pressman ch-22-process-and-project-metrics
 
Mg6088 spm unit-2
Mg6088 spm unit-2Mg6088 spm unit-2
Mg6088 spm unit-2
 
project planning components.pdf
project planning components.pdfproject planning components.pdf
project planning components.pdf
 
Risk Driven Testing
Risk Driven TestingRisk Driven Testing
Risk Driven Testing
 
significance_of_test_estimating_in_the_software_development.pdf
significance_of_test_estimating_in_the_software_development.pdfsignificance_of_test_estimating_in_the_software_development.pdf
significance_of_test_estimating_in_the_software_development.pdf
 
Chapter 15 software product metrics
Chapter 15 software product metricsChapter 15 software product metrics
Chapter 15 software product metrics
 
Critical steps in Determining Your Value Stream Management Solution
Critical steps in Determining Your Value Stream Management SolutionCritical steps in Determining Your Value Stream Management Solution
Critical steps in Determining Your Value Stream Management Solution
 
significance_of_test_estimating_in_the_software_development.pptx
significance_of_test_estimating_in_the_software_development.pptxsignificance_of_test_estimating_in_the_software_development.pptx
significance_of_test_estimating_in_the_software_development.pptx
 
Software metrics by Dr. B. J. Mohite
Software metrics by Dr. B. J. MohiteSoftware metrics by Dr. B. J. Mohite
Software metrics by Dr. B. J. Mohite
 
Module 4 - IDP.pptx
Module 4 - IDP.pptxModule 4 - IDP.pptx
Module 4 - IDP.pptx
 
Software engineering
Software engineeringSoftware engineering
Software engineering
 
Effective Test Estimation
Effective Test EstimationEffective Test Estimation
Effective Test Estimation
 
Software engineering jwfiles 3
Software engineering jwfiles 3Software engineering jwfiles 3
Software engineering jwfiles 3
 
A New Model for Building Business Process Quality
A New Model for Building Business Process QualityA New Model for Building Business Process Quality
A New Model for Building Business Process Quality
 
Six Sigma & Lean Production
Six Sigma & Lean ProductionSix Sigma & Lean Production
Six Sigma & Lean Production
 
Software engineering lecture notes
Software engineering lecture notesSoftware engineering lecture notes
Software engineering lecture notes
 
Test Planning and Test Estimation Techniques
Test Planning and Test Estimation TechniquesTest Planning and Test Estimation Techniques
Test Planning and Test Estimation Techniques
 
significance_of_test_estimating_in_the_software_development.pdf
significance_of_test_estimating_in_the_software_development.pdfsignificance_of_test_estimating_in_the_software_development.pdf
significance_of_test_estimating_in_the_software_development.pdf
 

More from PMI2011

Final chakradhar purohith proposal milieu analysis (without account data un...
Final chakradhar purohith proposal milieu analysis (without account data   un...Final chakradhar purohith proposal milieu analysis (without account data   un...
Final chakradhar purohith proposal milieu analysis (without account data un...
PMI2011
 
Wilso anandaraj balasubramaniankrishnamurthy
Wilso anandaraj balasubramaniankrishnamurthyWilso anandaraj balasubramaniankrishnamurthy
Wilso anandaraj balasubramaniankrishnamurthy
PMI2011
 
Vs velan dchakravarty_ppchakraborti
Vs velan dchakravarty_ppchakrabortiVs velan dchakravarty_ppchakraborti
Vs velan dchakravarty_ppchakraborti
PMI2011
 
Vineet jain
Vineet jainVineet jain
Vineet jain
PMI2011
 
Yamuna padmanaban
Yamuna padmanabanYamuna padmanaban
Yamuna padmanaban
PMI2011
 
Vimal kumarkhanna
Vimal kumarkhannaVimal kumarkhanna
Vimal kumarkhanna
PMI2011
 
Venkatraman l
Venkatraman lVenkatraman l
Venkatraman l
PMI2011
 
Vardarajan sethuraman
Vardarajan sethuramanVardarajan sethuraman
Vardarajan sethuraman
PMI2011
 
Soumen de
Soumen deSoumen de
Soumen de
PMI2011
 
Sujit sopan barhate
Sujit sopan barhateSujit sopan barhate
Sujit sopan barhate
PMI2011
 
Srinivasa desikanraghavan
Srinivasa desikanraghavanSrinivasa desikanraghavan
Srinivasa desikanraghavan
PMI2011
 
Sharad pandey abhisek goswami
Sharad pandey abhisek goswamiSharad pandey abhisek goswami
Sharad pandey abhisek goswami
PMI2011
 
Soma roy sarkar
Soma roy sarkarSoma roy sarkar
Soma roy sarkar
PMI2011
 
Shallu soni mymoonshabana_lavanya raghuraman
Shallu soni mymoonshabana_lavanya raghuramanShallu soni mymoonshabana_lavanya raghuraman
Shallu soni mymoonshabana_lavanya raghuraman
PMI2011
 
Regeena pererira sujithn_rai_suchitrajoyceb
Regeena pererira sujithn_rai_suchitrajoycebRegeena pererira sujithn_rai_suchitrajoyceb
Regeena pererira sujithn_rai_suchitrajoyceb
PMI2011
 
Ramesh ganiga
Ramesh ganigaRamesh ganiga
Ramesh ganiga
PMI2011
 
Pranabendu
PranabenduPranabendu
Pranabendu
PMI2011
 
Rt sundari ashutosh_pandey
Rt sundari ashutosh_pandeyRt sundari ashutosh_pandey
Rt sundari ashutosh_pandey
PMI2011
 
Pradeep n singh_praveenkyadav
Pradeep n singh_praveenkyadavPradeep n singh_praveenkyadav
Pradeep n singh_praveenkyadav
PMI2011
 
Parvathi karthik
Parvathi karthikParvathi karthik
Parvathi karthik
PMI2011
 

More from PMI2011 (20)

Final chakradhar purohith proposal milieu analysis (without account data un...
Final chakradhar purohith proposal milieu analysis (without account data   un...Final chakradhar purohith proposal milieu analysis (without account data   un...
Final chakradhar purohith proposal milieu analysis (without account data un...
 
Wilso anandaraj balasubramaniankrishnamurthy
Wilso anandaraj balasubramaniankrishnamurthyWilso anandaraj balasubramaniankrishnamurthy
Wilso anandaraj balasubramaniankrishnamurthy
 
Vs velan dchakravarty_ppchakraborti
Vs velan dchakravarty_ppchakrabortiVs velan dchakravarty_ppchakraborti
Vs velan dchakravarty_ppchakraborti
 
Vineet jain
Vineet jainVineet jain
Vineet jain
 
Yamuna padmanaban
Yamuna padmanabanYamuna padmanaban
Yamuna padmanaban
 
Vimal kumarkhanna
Vimal kumarkhannaVimal kumarkhanna
Vimal kumarkhanna
 
Venkatraman l
Venkatraman lVenkatraman l
Venkatraman l
 
Vardarajan sethuraman
Vardarajan sethuramanVardarajan sethuraman
Vardarajan sethuraman
 
Soumen de
Soumen deSoumen de
Soumen de
 
Sujit sopan barhate
Sujit sopan barhateSujit sopan barhate
Sujit sopan barhate
 
Srinivasa desikanraghavan
Srinivasa desikanraghavanSrinivasa desikanraghavan
Srinivasa desikanraghavan
 
Sharad pandey abhisek goswami
Sharad pandey abhisek goswamiSharad pandey abhisek goswami
Sharad pandey abhisek goswami
 
Soma roy sarkar
Soma roy sarkarSoma roy sarkar
Soma roy sarkar
 
Shallu soni mymoonshabana_lavanya raghuraman
Shallu soni mymoonshabana_lavanya raghuramanShallu soni mymoonshabana_lavanya raghuraman
Shallu soni mymoonshabana_lavanya raghuraman
 
Regeena pererira sujithn_rai_suchitrajoyceb
Regeena pererira sujithn_rai_suchitrajoycebRegeena pererira sujithn_rai_suchitrajoyceb
Regeena pererira sujithn_rai_suchitrajoyceb
 
Ramesh ganiga
Ramesh ganigaRamesh ganiga
Ramesh ganiga
 
Pranabendu
PranabenduPranabendu
Pranabendu
 
Rt sundari ashutosh_pandey
Rt sundari ashutosh_pandeyRt sundari ashutosh_pandey
Rt sundari ashutosh_pandey
 
Pradeep n singh_praveenkyadav
Pradeep n singh_praveenkyadavPradeep n singh_praveenkyadav
Pradeep n singh_praveenkyadav
 
Parvathi karthik
Parvathi karthikParvathi karthik
Parvathi karthik
 

Recently uploaded

Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
vu2urc
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
Earley Information Science
 

Recently uploaded (20)

Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 

Day 1 1620 - 1705 - maple - pranabendu bhattacharyya

  • 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