Day 1 1620 - 1705 - maple - pranabendu bhattacharyya

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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

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If collective estimation accuracy can be increased even by a minimal percentage, it

will translate to savings of multi-billion dollars


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)

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• Limited reuse of past organizational experience in estimates


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


Agenda





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

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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


• 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


• 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


Agenda





• 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…

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Applied the proven four phased approach

for process improvement


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

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All the framework components like “Size” etc. were adopted to instantiate best fit estimation models for relevant project types


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

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• 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


Deploy & Deliver

• Built solution awareness within the practitioner community

• Handheld projects for effective change management

Solution Deployment

Results Delivery

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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


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

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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

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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.

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Thank You

Contact: [email protected]
