Revenue Protection & Distribution System Analytics - Using in memory computing & big data analytics

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1 Revenue Protection & Distribution System Analytics - Using in memory computing & big data analytics Sanjeev Kumar Singh Sr. Consultant Utilities Solutions Mahindra Satyam Contact (M) (O) Fax Global Center of Excellence Utilities

2 About Mahindra Group Automotive Farm Equipment Trade, Retail & Logistics 6 decades of value creation Revenue : USD 15.6 billion Infrastructure Development Information Technology After Market Associate base: ~1,45,000 Amongst the most respected Indian companies Forbes Financial Services Systech Specialty Business

3 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 Americas Europe Asia Pacific Denmark Sweden Canada United States of America France Netherlands UK Ireland Germany Belgium Finland Czech Republic Hungary Switzerland Italy Spain Brazil Jordan Egypt Bahrain Saudi Arabia UAE Qatar India China Hong Kong Thailand Singapore Korea Taiwan Japan Malaysia South Africa Australia MEA & India New Zealand

4 Mahindra Satyam Middle East & Africa 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 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

5 African Story Since 2004

6 Utilities Practice Highlights Matured practice with 10+ years of experience; focused on end-to-end solutions for Utilities Practice Highlights Key Value Preposition Served 55 + Customers spread across globe 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, AMI, Mobility solution Handhelds Smart Grid components design & integration AMI, DR, Microgrid, Energy Optimization application, Automation Have worked with 25 of the 50 largest electric utilities and 23 of the largest 50 gas utilities in North America Our group owns a water utility by name Mahindra Water Utilities - Do/Consulting- Services/Companies/Mahindra-Water- Utilities Financial Technical Operational Industry Leadership Thorough knowledge of Utility Economics and business planning process, including regulatory treatment, that are required to develop a profitable 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 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 Grid -Wise Alliance, Working Committee, NRDC & others. Our Consultants have worked with over half of the top 50 electric utilities in North America.

7 Utilities have to manage huge amount of data Electrical Infrastructure Information Infrastructure Customer interactivity Huge amount of Data Analytics will unleash the business value from that information. Utilities will be able to develop situational awareness and also plan operations more effectively.

8 Big Data sources are many, both from the new and existing utility infrastructure

9 Why do Utilities need analytics? 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

10 Real Time Analytics using SAP HANA 2 million Records in seconds 1000 times of transformation from 10 Min Benefits of Using SAP HANA for M2M Analytics Speed and power of in-memory processing Seamless Integration to M2M Devices using Communication gateway In-Memory Platform Real-Time Analytics Content/Applications Accelerated BI 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

11 Challenges we can solve Assets Management Outage Management Demand Management Revenue Protection Data visibility and Understanding the right problem Business Challenges Analytics Balancing & Settlement Data Management (ETL: Extraction, Transformation and Loading) Business Intelligence Solutions (Dashboards, Portfolio Analysis) Customer Analytics (Segmentation and profiling) Predictive Models (Demand Response Model, Energy Load control Model) Pricing strategies

12 Data Repository, Meter Data, Site Photos, Videos, Inspection Documents Solution Frame Work for Utilities Analytics Monitoring Devices / Evidence Collection Managing the field inspections and Documents Creating and updating the reference points Analytics for Revenue Protection and Operational Excellence Correlated Loss Analysis Profile Administration & Balancing Virtual Group Behavior Analysis Distribution Operations Monitoring Regulatory & Statutory Compliances Legal framework Case Manage ment Dash Boards / KPIs / Reporting

13 Systems Details Distribution Transformer Primary Substation CT PT Primary (MV)side Metering Distribution Feeder (MV) Meter By Pass Monitoring device Power network parameters like Power, Energy, Voltage, Current, Power factor, etc. Communication Middleware Customers

14 Solution Architecture DT SAP BO Reporting Tools (Webi, Explorer, Dashboard) DT IDT GPRS Energy Meters Communica tion Middle ware SAP HANA SLT (Real-Time) Integration queue SLT (Real-Time) SAP BODS SM SM Broker Client Customer Information system Reading & Billing Electrical N/w Management System Customer representative Profile SAP ECC IS -UTILITY Customer Information system Reading & Billing Electrical N/w Management System Customer representative Profile Utility Aps & DB For Non SAP Customer DataBase

15 Important use Cases

16 Loss Diagnostic Studies Profiling & Estimation

17 Axis Title Axis Title Loss Diagnostic Studies Statistical Analysis Chart Title Axis Title Z= 95% confidence level = 1.96 S= Standard Deviation = 1099 E= Error tolerance = 0.5 Delivered Units Typical Theft Profile Typical Theft Profile y = x R² = Axis Title Typical Theft Profile Linear (Typical Theft Profile)

18 Asset Management - Load Monitoring Peak load Load duration curves can tell us about the peak loading & duration of overloading Suppose, this is 100% rating This is the duration of overloading

19 Asset Management - Phase Unbalance Studies

20 Asset Management - Reactive Demand Profiling & Studies Base & switched capacitor requirement Should be taken from the profile Curve This can be linked to upstream voltage conditions

21 Customer Analytics

22 Customer Analytics

23 Application Video

24 Benefits for Utilities Zero down Commercial Losses Optimized Technical Losses Enhanced Asset Life at least by 20 % - 30 % Reduced expenses on maintenance Reduction of around % 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

25 Thank you 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

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