revenue protection distribution analytics on big data ver 1.0

6 decades of value

creation

Revenue :

USD 15.6 billion

Associate base:

~1,45,000

Amongst the most

respected Indian

companies – Forbes

Automotive Farm Equipment Trade, Retail & Logistics

Infrastructure Development Information Technology After Market

Financial Services Systech Specialty Business

About Mahindra Group

Czech Republic

Hungary

Switzerland

Finland

New

Zealand

Brazil

South

Africa

Canada

United States

of America

Denmark

Sweden

Belgium

Italy Spain

France

Netherlands

Germany

UK Ireland

Qatar

India

Bahrain

Saudi Arabia

Jordan

UAE Egypt

Australia

Singapore Malaysia

China Japan Korea

Thailand

Taiwan

Americas Europe Asia Pacific

MEA & India

Hong

Kong

Mahindra Satyam - Global Presence 35,000+ Associates

>90% engineers and business graduates

60+ nationalities

28 years average age

95% Local Associates in Eastern Europe, China & Africa

African Story – Since 2004

• Strong presence in GCC, Levant and African countries since 1999

• Fastest growing region for Mahindra Satyam globally

• Over 140 full lifecycle implementations

• 450+ associates in Middle East + 300 supporting from offshore centers

• Highly matured processes across business consulting, ERP

implementations, Infrastructure management services, software

development and support

• Extensive Investments :

• Presence to be close to Customer [UAE, Qatar, Kuwait, Oman ,

Bahrain and Saudi Arabia]

• Set up of Full fledged Operations in Johannesburg to cater to

South African Operations

• Near Shore Development Center in Egypt

Mahindra Satyam Middle East & Africa

Matured practice with 10+ years of experience; focused on end-to-end solutions for Utilities

Served 55 + Customers spread across 10 Countries

Focused on end-to-end Solutions for Utilities

A decade of experience in EAM, BI and ERP

Utility Business Application – CRM/CIS, CC&B ,MDM ,

RMR , Mobility solution –Handhelds

Smart Grid components – design & integration – AMI ,

DR , Microgrid , Energy Optimisation application ,

Automation

Alliance / Joint Go-To-Market partners : SAP, Oracle,

IFS , IBM, CISCO, Schneider , Elster and GE.

Have worked with 25 of the 50 largest electric utilities

and 23 of the largest 50 gas utilities in North America

Practice Highlights

Deep knowledge of energy and utility industry,

technology, and functional “best practices”

combined with exceptional implementation skills

Use a low-risk global delivery model to accelerate

schedules with a high degree of time and cost

predictability

Deploy smaller, more experienced teams than

other top-tier firms

Provide the experience of blue-chip firms, but

more flexibly and cost-effectively.

Our Key Differentiators

Utilities Practice & Our Indicative Customers

http://www.coned.com/

http://progress-energy.com/index.asp

http://www.bchydro.com/

We are well positioned in the Smart Grid arena given our

extensive understanding of Utilities and their Smart Grid

strategies

We have worked with 27 of the 50 largest electric utilities and

24 of the largest 50 gas utilities in North America

Thorough knowledge of utility economics and business planning processes,

including regulatory treatment, that are required to develop a profitable

Smart Grid strategy and business case for investment

Deep understanding of the implications of integrating new technologies with

existing T&D infrastructure, including Smart Meter/AMI, Distribution and

Substation Automation, Outage Management, Distributed Generation and

Distribution Management

Long-term experience helping utilities deal with the broader critical issues

that they face, including asset management, regulatory policy, reliability and

safety, vendor selection, and supply chain management

A history of innovation and thought leadership in the industry through active

participation in leading organizations such as the GridWise Alliance Working

Committees, NRDC and others. We have worked with over half of the top 50

electric utilities in North America

Financial

Technical

Operational

Industry

Leadership

To have real-time access to utility databases

Able to read all data fields in all systems (but not modify them)

To check all equipments (meters, transformers, generators, etc.)

All losses and imbalances are to be tracked in real-time

Decisions are based on cost, staff availability, and pre-set performance

targets

System to automatically order equipment needed for future work and

monitor equipment inventory

In real time to monitor all network operations

System need to automatically schedule field work if equipment (or

customers) behave unexpectedly

Why do Utilities need analytics?

Smart Utilities have to manage huge amount of data

Huge

amount of

Data

Customer interactivity

Electrical

Infrastructure

Information

Infrastructure

Analytics will unleash the business value from that information. Utilities will be able to develop situational

awareness and also plan operations more effectively.

http://www.informcomm.co.uk/wpimages/wp4b38ec96.gif

‘Big Data’ sources are many, both from the new and

existing utility infrastructure

In-Memory Platform

Real-Time Analytics

Content/Applications

Accelerated BI

Benefits of Using SAP HANA for M2M Analytics

Speed and power of in-memory processing

Seamless Integration to M2M Devices using

Communication gateway

Less Database Space compared to traditional

Database

Open, agnostic architecture

Real-time insight into

Business operations without the delay

Huge volumes of detailed information

360 degree Deep-dive of M2M generated

machine data

Real Time Analytics using SAP HANA

Monitoring Devices / Evidence Collection

Managing the field inspections and Documents

Dash Boards / KPIs / Reporting

Regulatory & Statutory

Compliances Legal

framework

Case Manage

ment

Dat

a R

ep

osi

tory

, Me

ter

Dat

a, S

ite

Ph

oto

s, V

ide

os,

In

spe

ctio

n D

ocu

me

nts

Correlated Loss Analysis

Profile Administration & Balancing

Virtual Group Behavior Analysis

Distribution Operations Monitoring

Analytics for Revenue Protection and

Operational Excellence

Creating and updating the reference points

Solution Frame Work for Utilities Analytics

Data Management

(ETL: Extraction,

Transformation

and Loading) Business

Intelligence

Solutions

(Dashboards,

Portfolio

Analysis)

Pricing

strategies

Demand

Management

Assets

Management

Outage

Management

Revenue

Protection

Data visibility

and

Understanding

the right

problem

Balancing &

Settlement

Predictive Models

(Demand Response

Model, Energy Load

control Model)

Customer

Analytics

(Segmentation

and profiling)

Business

Challenges

Analytics

Challenges we can solve…

By

Pass

CT PT

Meter

Primary (MV)side Metering

Monitoring

device

Systems Details

Distribution

Feeder (MV)

Customers

Primary

Substation

Distribution Transformer

Power network parameters like

Power, Energy, Voltage, Current,

Power factor, etc.

Communication

Middleware

SAP HANA Energy Meters

Solution Architecture

GPRS

Reading & Billing

Customer

Information system

Electrical N/w

Management

System

Customer

representative

Profile

DataBase

Utility Aps & DB

Broker Client

SAP BODS SLT (Real-Time)

Reading & Billing

Customer

Information

system

Electrical N/w

Management

System

Customer

representative Profile

For N

on

SAP

Cu

stom

er

SLT (Real-Time)

SAP ECC IS -UTILITY

DT

DT

SM

SM

IDT

SAP BO Reporting

Tools (Webi, Explorer,

Dashboard)

Axeda

Platform

Integration

queue

Zero down Commercial Losses

Optimized Technical Losses

Enhanced Asset Life at least by 20 % - 30 %

Reduced expenses on maintenance – Reduction of around 20- 30 % possible

Comprehensive information system helps in network planning and ensure the reduced investment on electrical Power network

Verification tools for demand Response

Security and Safety of general public

Ensures proper billing & Settlement

Benefits for Utilities

Use Case - Demand Management

Business objectives

Identify the typical demand pattern

Determine the factors driving

energy load/influencing demand

Forecast demand for future

Demand Response Model

How

Create a detailed portfolio analysis

to understand the demand pattern

by factors such as

Seasonality

Population growth

Holidays

Logical grouping of profile

/Segmentation to address energy

load or high demand behavior

Forecast demand based on

seasonal factors or other key

factors such as climate comfort,

population growth, region etc.

Build a predictive model to

understand which segment of the

customer base is likely to respond

and thereby influence the demand

in peak time

Techniques

Pre prepared cube driven tools

such as business objects/crystal

reports

Simple excel dashboards linked to

cubes and pivot tables

Cluster Analysis/Decision Tree

techniques

Time series forecasting such as

decomposition or Linear

regression to quantify the impact

by different factors

Logistic Regression or Decision

Tree techniques

Optimizes energy consumption in homes, offices and factories

Low

Medium

High

Identify the typical demand pattern and determine

the factors driving energy load

Find the best service bundle for a consumer and

supplier from demand management point of view

Business Problems

# of customers

Dem

an

d

Attitudinal

Demographics Value Add

Services

Customer

Behavior

Combine existing demographic and attitudinal

data with behavior to form segments

Customized service bundles for different

segments

Design demand response programs for specific

segments

Approach and Implementation

Low

Demand

Medium

Demand

High Demand

Use Case - Demand Management

Accurately predict the response behavior for a

demand response program and thereby influence

the demand in peak time

Business problem

Billing

Customer information

Metering data

Demand Behavior

Others

Build profile for

response behavior

Predictive Model

Response score

Use Case - Demand Response Model

Detailed diagnosis of customer, billing and

demand behavior data

Use logistic regression to gain a complete picture

of response behavior and measure deviations

from normal behavioral patterns

Score every consumer using the equations built

in the above step

Approach

Analyze revenue generation

process and identify the revenue

leakage points

Build pricing engines so dynamic

that they produce the right offer, for

the right customer at the right time

Reduce the disruption and cost

associated with outages

Identify key components of

revenue process such as billing,

metering, collection, customer

care etc

Identify revenue leakage points

and capture factors behind it

Build price elasticity model and

measure customer response to

critical peak pricing

Understand the factors behind

outage and analyze the impact

Build a predictive model to send

alerts to consumers when outage

is predicted

Business Objectives How Techniques

Portfolio analysis to understand

the current scenario and Multiple

Regression technique to quantify

revenue leakage points

Multiple Regression and simple

excel tool to calculate percentage

change in price caused by

percentage change in demand

Decision Tree/Cluster Analysis

Use Case - Revenue and Cost Management

Efficiency Tracking and life cycle

analysis of assets involved for

optimized solutions

Identifying the financial impact of ill-

performing assets and predicting

the cost over time of under

performing assets

Trending the performance of my

network health and build predictive

models to prevent reliability issues

Life cycle cost analysis ,correlation

analysis to identify key factors

which cause potential problems in

grid and power equipments.

Historical data analysis and cross-

tabulation to understand the

correlation between cost and

performance

Building a predictive model as to

identify possible scopes of outage

and power failure

Linear regression to quantify the

impact by different factors ,Survival

analysis Enterprise, quality-centric

data model.

Reporting and key performance

indicator dashboardssimple excel

dashboards linked to cubes and

pivot tables

Time series forecasting such as

decomposition or Linear regression

to quantify the impact by different

factors

The true optimum is where the combination of costs, risks and performance shortfall is of minimum total impact

Business Objectives How Techniques

Use Case - Asset Management

Identify the typical Life cycle pattern and

determine the factors behind their failure

Identifying the financial impact of ill-performing

assets and predicting the cost over time of

under performing assets

How to best predict an outage occurrence

based on historical data analysis

Business Problems

Risk Assessment of assets

Condition Assessment of individual assets

Life Cycle Decisions: retire, refurbish, replace,

relocate

Approach and Implementation

Life Cycle cost Analysis Condition Assessment

Risk Assessment Life Cycle Cost

Analysis Condition Assessment

Optimized Solution

Achieved

Life Cycle

Decisions

Retire Relocat

e

Replac

e

Use Case - Asset Management

Application Demo

Test4.avi

Mahindrasatyam.com

Safe Harbor

This document contains forward-looking statements within the meaning of section 27A of Securities Act of 1933, as amended, and

section 21E of the Securities Exchange Act of 1934, as amended. The forward-looking statements contained herein are subject to

certain risks and uncertainties that could cause actual results to differ materially from those reflected in the forward-looking

statements. Satyam undertakes no duty to update any forward-looking statements. For a discussion of the risks associated with our

business, please see the discussions under the heading “Risk Factors” in our report on Form 6-K concerning the quarter ended

September 30, 2008, furnished to the Securities and Exchange Commission on 07 November, 2008, and the other reports filed with

the Securities and Exchange Commission from time to time. These filings are available at http://www.sec.gov

Thank you

Back Up Slides

Mahindra Satyam Customers in Africa (Indicative List)

Southern Africa

Government of Western Cape, SA

Government of Limpopo (LEDET), SA

Limpopo Treasury, SA

Road Accident Fund, SA

Capricorn Municipality, SA

Human Science Research Council (HSRC), SA

North West Provincial Legislature (NWPL), SA

Lafarge, SA

Old Mutual, SA

Standard Bank of South Africa

Nedbank, SA

First Rand Bank

Caltex, SA

Shell, SA

General Motors, SA

De Beers, SA

Harmony Gold Mines, SA

Telkom, SA

Transnet National Ports Authority(TNPA)

Assmang Ltd

Nissan SA

Rest of Africa

Lafarge Cement (WAPCO), Nigeria

Ashaka Cements, Nigeria

Far East Mercantile Co. Ltd, Nigeria

MRS Oil, Nigeria

Bank of Ghana, Ghana

Kenya Airways , Kenya

Safaricom, Kenya

Airtel Africa, Kenya

Central Bank of Kenya (CBK), Kenya

SILAFRICA Ltd., Tanzania

Government of Egypt

Misr Phone, Egypt

ITIDA, Egypt

Mittal Steel, Algeria

African Development Bank (AfDB), Tunisia

Mauritius Post and Cooperative Bank

Air Mauritius, Mauritius

Emtel, Mauritius