BPaaS – Spend Analysis Competency Overview

1. BPaaS – Spend Analysis Competency Overview

2. Our Understanding

3. Trends and VOC that are shaping up the market for Master Data Rationalization (MDR) Statistics about data in manufacturing Industry Important challenges in Data management Motivation for implementing Data Categorization Success factors to implement Data Categorization *http://www.ventanaresearch.com/ and E&Y reports

4. Business Drivers for Master Data Rationalization attempt to move towards cause of concern Organization scenario Achieve merger synergies by asset and inventory consolidation M&A entity Migrating to ERP from Legacy Expedite transition and ensure compatibility of consolidated data Consolidating to a single ERP application Duplicate item data instances CONSOLIDATING MASTER DATA Drive part reuse and develop synch between production and sourcing by aiding ‘critical information’ discovery Driving down CoGS at design stage

5. MDR – A Business Case Data Analysis • An in-depth review of an organization’s expenditure for a specific period of time using information on S2P activities & contributors • Strategic tool for decisions across the organization i.e...Sourcing, purchasing, Payment, Supplier performance, Compliance, Return & Recovery Business Benefits Spend Data Mgmt

6. Scope of Discussions Executive Summary

7. Our Proposition

8. Tech Mahindra Solution Approach TechMSolution Stages • Leverage TechM’sstrong Manufacturing domain expertise to build the taxonomy and modifier attributes for the key categories. • Use TechMknowledge base and competency to develop business rules for data cleansing in a timely and optimal way. • Leverage Tech Mahindra’s strong expertise in Extraction and Automated Cleansing tools • Scalable model to include further key nouns in future from other legacy systems Stage 1 Prepare the taxonomy and Mapping rules Profiling of the data input files Stage 2 Stage 3 Cleansing of Data Stage 4 Enrichment of Data using Additional attributes Stage 5 Mapping into ERP MDM “Load Ready” files

9. Solution Architecture Solution leverages existing infrastructure and best in class features ERP/Legacy Satyam content dictionary Web Referencing Supplier repository Standards Knowledge Base Code Validation Can be automated classification tool or manual classification Spend Classification Tool -Data Work bench QA Workbench Enrichment Extraction Tool Layer (ETL) Data Profiling Repository Cleanse & Classify Classification Model Specification Template Output CSV File Data Warehouse Spend Visibility Solution

10. Socket Data from the sample received Analysis of the Sample Data Key Findings Enriched Data

11. Cleansing and Classification – An Example 8323955 PLUG, 3 POLE-MALE EMD, FOR COOLING FAN, GENERATOR/DUST BIN BLOWER Key noun: Plug;Modifier: 3 Pole; Completion and Data Accuracy  50% Raw Data, # of items - 6000 Completion and Data Accuracy  75% # of Items  443 Initial QA for Column integrity / Row integrity and data types Completion and Data Accuracy  90% Classification Tool for finding out key nouns classification Existing data • Cleansing • Duplicity removal • Data Parsing • Additional duplicates removal • Population of available attributes ShortDesc: 8323955 PLUG, 3 POLE-MALE EMD, FOR COOLING FAN, GENERATOR/DUST BIN BLOWER Cleansed and classified PLUG, 3 POLE Rating: __ Amps IP: ____________ Straight or Angled More attributes • Data Enrichment • Additional attributes identification • Population of the attributes (Satyam knowledgebase, web referencing Completion and Data Accuracy  95 % Enriched Data # of attributes = 2 PLUG, 3 POLE Rating: __ Amps IP: ____________ Straight or Angled Manufacturer Name MFG Part # More attributes Language translation DW Ready Load Files # of attributes = 7

12. Deployment Options

13. MDR Solution Options An Outside in View

14. MDR Solution Option- I Fully Automated Solution • The solution considers UPRR will provide Master Data from multiple source systems and either generates the flat file/ data in required format and pushes to a staging area. • Cloud Based Automated Classification tool would pick up the flat file of data using scheduler and loads into the Classification engine for cleansing/ classifying (UNSPSC) and enrichment. • Output accuracy – 80-90 % • Speed of Processing – 50000- 100000 records per hr • Needs SME intervention for taxonomy definition, cleansing rules, training the classification engine • The project is expected to go live (operational) in 8-12 weeks from Start of Assessment phase

15. MDR Solution Option- II Manual Classification Solution • The solution considers UPRR will provide Master Data from multiple source systems and either generates the flat file/ data in required format and pushes to a staging area. • Manual Classification – Recommendation of UNSPSC taxonomy adoption. • Output accuracy – 70- 85 % • Needs SME intervention for taxonomy definition, cleansing rules, training the classification engine • The project is expected to go live (operational) in 20-24 weeks from Start of Assessment phase

16. Competency Details

17. Vision Providing end to end purchasing solutions that helps clients achieve cost reductions, streamline Sourcing & Procurement process and reduce sourcing cycle time. Tech Mahindra Spend Management Practice Overview Service Offerings Spend Analysis Strategic Sourcing Low cost Country Sourcing SRM Package Evaluation eTendering Sourcing Support & Auctions Invoice Processing Procurement Operations Contract Management SRM Product Implementation Supplier Collaboration Maintenance & Support Practice Highlights • Largest practice with over 250 consultants providing Procurement & Sourcing solutions worldwide. • Over 5 Million person hours of experience in delivering spend management solutions. • Sourced over US$ 100+ Bn (direct & Indirect material) , 200+ sourcing events & achieved significant savings to clients. • Executed over 100 projects ranging from Sourcing, Procurement Consulting, Product Implementation and Maintenance. • Alliance with Product vendors such as Ariba ,SAP, Oracle, Aravo, Basware, Nextenders, Symfact, Endeca, Zycus etc. Key Alliances

18. Tech Mahindra Spend Management Practice Maturity Snapshot Product Focus Vertical Focus Services Focus Process Focus

19. Marquee Spend Management Customers Keppel Tatlee Bank

22. CASE STUDY – Spend Analytics Design & Implement • A standardized Global Data Warehouse considering all transactional (P.O. and Invoice) & Master data requirements within the entire Client community • A Dashboard and drill-down toolset utilizing corporate standards Business Imperatives • Global data warehouse and integrated with sales data • Reduce time spent on gathering data • Homogenize the data as per Client’s DSAP standards Commodity code using USNPSC, Vendor using DUNS, Geographic master data • Increased control and maintenance of data • Increase quality of global spend data Benefits Satyam Challenges • 42 source systems ( 60% legacy ) • Integration with data homogenizing tools like Zycus, D&B • ETL and Trillium for data cleansing and transformation • Aggressive target build times

23. Dashboard Solution for CAT, USA Considering the pain areas the objective of the solution is to provide • High Scalability • Easy Availability • High TCO • Easy Maintainability Building Dashboard based solution is planned to address the business needs. Objective Business Scenario Infra Major is keen on examining a Business Activity Monitoring (BAM) and scorecard solutions which can help in a) Overcoming the problems associated with aligning operational activities & corporate strtgy b) Conquer the difficulties involved in identifying, monitoring and acting on urgent problems quickly and effectively Solution Details • Initial phase required detailed analysis of the KPIs and building a dimensional model completing the fully life cycle involved in the data warehouse design. •  Data was loaded from the text files to staging area on SQL server using SSIS 2005. The only transformation involved was of converting DB2 date time fields to SQL Server date time date type. • Fact tables were populated with help of stored procedures. Measured dimension and summary fact were created to display data in business scorecard view. • For detailed analysis of data; report views including pivot chart and pivot cubes were designed and published from OLAP Server data source to SharePoint server. Benefits • Helps in performing health check of business activity through monitoring of critical KPIs. • Single version of truth available across the organization. Solution Architecture Database-SQL Server 2005 ETL- SQL Server Integration Services(SSAS) Dashboard: Business Scorecard Manager Portal : Share Point Portal Server 2003 Development Studio.- Business Intelligence development studio (BIDS) Technology Stack

24. Rich experience Why Tech Mahindra? Our Differentiators • Thought Leader • Consultant profile • Process, Tools and Templates • Right Alliances, Partners

25. Thank you Visit us at www.techmahindra.com