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BPaaS – Spend Analysis Competency Overview. Our Understanding. 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 .

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slide1

BPaaS – Spend Analysis

Competency Overview

trends and voc that are shaping up the market for master data rationalization mdr
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

business drivers for master data rationalization
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

data analysis

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

slide6

Scope of Discussions

Executive Summary

tech mahindra solution approach
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

slide9

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

cleansing and classification an example
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

slide13

MDR Solution Options

An Outside in View

slide14

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
slide15

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
tech mahindra spend management practice overview

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

slide18

Tech Mahindra Spend Management Practice

Maturity Snapshot

Product Focus

Vertical Focus

Services Focus

Process Focus

slide22

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
dashboard solution for cat usa
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

slide24

Rich experience

Why Tech Mahindra?

Our Differentiators

  • Thought Leader
  • Consultant profile
  • Process, Tools and Templates
  • Right Alliances, Partners
thank you
Thank you

Visit us at www.techmahindra.com