Transcript Data Analysis

BPaaS – Spend Analysis
Competency Overview
Copyright © 2013 Tech Mahindra. All rights reserved.
1
Our Understanding
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2
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
80%
75%
73%
66%
Companies
Companies
were not
feel difficult or
confident in impractical to
their material standardise
data
data
Manually
Manually clean
cleanse the
data are not
data
happy with the
result
Motivation for implementing Data Categorization
Success factors to implement Data Categorization
*http://www.ventanaresearch.com/ and E&Y reports
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3
Business Drivers for Master Data Rationalization
Organization scenario
M&A entity
Migrating to ERP
from Legacy
Consolidating to a
single ERP application
attempt to
move towards
Achieve merger
synergies by asset and
inventory
consolidation
Expedite transition
and ensure
compatibility of
consolidated data
CONSOLIDATING
MASTER DATA
Driving down CoGS
at design stage
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cause of concern
Duplicate item
data instances
Drive part reuse and
develop synch
between production
and sourcing by aiding
‘critical information’
discovery
4
MDR – A Business Case
 An in-depth review of an organization’s expenditure for a
specific period of time using information on S2P activities
& contributors
Data Analysis
AP Data
 Strategic tool for decisions across the organization
i.e...Sourcing, purchasing, Payment, Supplier
performance, Compliance, Return & Recovery
PO Data
pCard
Data
Business Benefits
Improve
Processes
Manage
Maverick
Spend
Manage
Supplier
Relationship
Manage Risk
Recover
Overpayments
Procure Cooperatively
Reduce
Disparity
Source More
Locally
Ensure Legal
Compliance
Benchmark
Relative
Position
Quantify
Savings
Deliver
Savings
Cleanse
Classify
Enhance
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Spend
Data
Mgmt
5
Scope of Discussions
Executive Summary
Data Management –Materials (OEM and OPM Parts)
Scope of Work
Service details
Current issues
• Duplicate
Materials
• Non Standard
descriptions
• Materials with
minimum
description
information
Key Statistics
Objectives
• To cleanse the
material master to
eliminate duplicates,
obtain a single
cleansed,
standardized, enriched
material master per
item
• Business benefit will
be to facilitate better
searches for items,
ordering correct
material, reducing
maverick spend and
ensure complete
information of an item
in a PO
Taxonomy
• Providing Industry
Standard
Taxonomy
•Prepare Mapping
Sheets
Cleansing
Data
Enrichment
Multi Language
Capability
• Cleansing
• Enhance
•
and
information
standardizing from parts
•
the material
repository,
master
supplier
• Assigning
catalogs and
attributes
web crawl
• Cross
• Providing
referencing
Interchangeabl
with internal
e Item
classification
Identification
• Duplicates
report
reporting
• OEM and OPM
Data
Enrichment
Translation to
other languages
Creation of
ERP MDM
“Load Ready”
file
• Catalog and Stock items categorized-44,000+ item
• Key commodities include: Tools & Maintenance (MRO), Process Chemicals, Oils &
Lubricants, Site Services, Machinery Maintenance, Industrial Supplies, Office Supplies,
Packaging
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6
Our Proposition
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7
Tech Mahindra Solution Approach
TechM Solution Stages
• Leverage TechM’s strong Manufacturing
Stage 1
Prepare the taxonomy and Mapping
rules
domain expertise to build the taxonomy
and
Stage 2
Profiling of the data input files
Stage 3
attributes
for
the
key
categories.
• Use
Cleansing of Data
modifier
TechM
knowledge
base
and
competency to develop business rules for
data cleansing in a timely and optimal way.
Stage 4
Stage 5
Enrichment of Data using Additional
attributes
Mapping into ERP MDM “Load
Ready” files
• 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
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8
Solution Architecture
Solution leverages existing infrastructure and best in class features
Satyam content dictionary
Web Referencing
Supplier repository
ERP/Legacy
Standards Knowledge Base
Code Validation
Spend Classification Tool -Data Work bench
Extraction Tool
Layer (ETL)
Can be automated
classification tool or
manual classification
QA Workbench
Data Profiling
Cleanse & Classify
Specification
Template
Classification
Model
Enrichment
Repository
Output
CSV File
Data Warehouse
Spend Visibility Solution
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Analysis of the Sample Data
Socket Data from the sample received
MMS #
58949680
71246510
19071750
41802880
41856410
41858510
41810990
41811010
41804770
41805050
41802570
41802820
41802550
41805770
41815380
41803500
Description
502195001 8 PIN RELAY SOCKET FAIRMONT
8332375 SOCKET EMD, SOCKET FOR 096-7058 STEP LIGHT, ASSY
HOSE SOCKET, 3/8 FEMALE PIPE THREAD, 3/8 BODY SIZE, SAFETY LOCK, TUTHILL#23203-300
SET, SOCKET 3/8 DR IMPACT HEX HEAD, 2 LENGTH. 7 PC SET INCLUDES, 3/16, 7/32, 1/4, 5/16, 3/8, 7/16, 1/2, POWER TOOL RATED ALLEN HEX, BIT, GREY PNEUMATIC# GP1297H,
DXP# GP1297H
SOCKET, 1 7/8 SOCKET 8 LONG, GUYNAN MACHINE AND STEEL, # 052410, CUSTOM MADE SOCKET FOR THE, QUICK AND SAFE REMOVAL OF, GUAGE RODS ESPECIALLY IN,
DERAILMENT SITUATIONS.
SOCKET, 1 SQ DRIVE 1 1/4 DOUBLE SQ, POWER DRIVE IMPACT, BLACK, ANSI B107.2, SNAP-ON IM-740
SOCKET, 1/2 DRIVE. IMPACT GREY, 82012D, 3/8 DEEP WELL SOCKET. THIN, WALL, DUO-SOCKET. 6 PT. NO, SUBSTITUTIONS, DXP# 82012D
SOCKET, 1/2 DRIVE. IMPACT GREY, 82016D, 1/2 DEEP WELL SOCKET. THIN, WALL, DUO-SOCKET. 6 PT. NO, SUBSTITUTIONS, DXP# 82016D
SOCKET, 1/2IN DRIVE 10 5/16IN LENGTH, TBGRE2910F, IMPACT HEX SOCKET
SOCKET, 3/8 DR IMPACT HEX HEAD, 3/16 X 2 LENGTH. POWER TOOL, RATED ALLEN HEX BIT., GREY PNEUMATIC# GP 1906F, DXP# GP1906F
SOCKET, 3/8 DR, 22 MM, IMPACT GREY PNEUMATIC, 1122M, THIN WALL, DUO SOCKET,, 12 PT METRIC, DXP# GP1122M
SOCKET, 3/8 DR, 8 MM, IMPACT GREY PNEUMATIC, 1108M, THIN WALL, DUO SOCKET,, 12 PT METRIC, DXP# GP1108M
SOCKET, 3/8 DRIVE 5/16 IN HEX PROTO, J49905-16XL, 6-3/32 IN LENGTH XTRA LONG,, ALLEN. 16XL HAND USE ONLY., DXP# J49905-16XL
SOCKET, 3/8 IMPACT HEX HEAD, 3/8 IN 2-23/32 IN LENGTH. POWER, TOOL RATED ALLEN HEX BIT, PROTO# J7290 3/8, DXP# J7290 3/8
SOCKET, 3/8 SQ DRIVE 3/4 HEX, POWER DRIVE IMPACT, BLACK, SWIVEL, ANSI B107.2, SNAP-ON IPF24C
SOCKET, 3/8 SQ DRIVE, HAND ONLY. CROWFOOT 3/4IN WRENCH, PROTO# J3924CF, DXP# J4924CF, SNAP ON# FCO24A
Key Findings
Nomenclature mentioned is not similar across the family e.g. Drive / Dr is mentioned
Attributes range from two to nine
Inconsistent placement of Attributes
In some item discriptions Modifier is not mentioned
Make of the socket is not mentioned in a consistent manner ( Grey Pnumatic / Grey)
Enriched Data
Noun
Modifier
Attribute 1
Attribute 2
Attribute 3
Attribute 4
Attribute 5
Socket
Impact
Drive
Head Size
Standard /Deep / Universal / Deep Universal
Manufacturer Name
Manufacturer Part No
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10
Cleansing and Classification – An Example
8323955 PLUG, 3 POLE-MALE EMD, FOR COOLING FAN, GENERATOR/DUST BIN BLOWER
Key noun: Plug;
Raw Data, # of
items - 6000
Modifier: 3 Pole;
Completion and Data
Accuracy  50%
Initial QA for
Column integrity /
Row integrity and
data types
Existing data
Completion and Data
Accuracy  75%
# of Items  443
Cleansed and classified
ShortDesc: 8323955
PLUG, 3 POLE-MALE
EMD, FOR COOLING FAN,
GENERATOR/DUST BIN
BLOWER
# of attributes = 2
Completion and Data
Accuracy  90%
Classification Tool
for finding out key
nouns classification
PLUG, 3 POLE
Rating: __ Amps
IP: ____________
Straight or Angled
Cleansing
•
•
•
•
Completion and
Data Accuracy  95 %
Enriched Data
More attributes
# of attributes = 7
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Duplicity removal
Data Parsing
Additional duplicates removal
Population of available
attributes
PLUG, 3 POLE
Rating: __ Amps
IP: ____________
Straight or Angled
Manufacturer Name
MFG Part #
More attributes
Data Enrichment
•
Additional attributes
identification
• Population of the
attributes (Satyam
knowledgebase, web
referencing
Language
translation
DW Ready
Load Files
11
Deployment Options
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12
MDR Solution Options
An Outside in View
Capabilities
Automated Approach
Manual Approach
Ability to handle poor
quality data
High
Medium
Ability to handle
multilingual item data
High
Medium
Real Time
Classification
Yes
No
Scalability to large
volumes
High
Medium
Ability to assist in
standards adoption
High
Medium
Rework Required to
change or upgrade
schema
Low
Very High
Combination of Domain
and Language expertise
Yes
No
Scalability to large
volumes
High
Low
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13
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
Phases/ Steps of Project
1
Data Assessment and Taxonomy
Definition
2
Automated Classification
Solution
Total
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Duration
Weeks
4-6
4- 6
Remarks
Feasibility Study and Development
of Taxonomy, (Key Noun, Modifier and
attributes, Classification Standards
adoption)
One Year Subscription For
Classification Software + Config +
Training
8-12 weeks
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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
Phases/ Steps of Project
1
2
Data Assessment and Taxonomy
Definition
Automated Classification
Solution
Total Cost of Ownership
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Duration
Weeks
4-6
12-16
Remarks
Feasibility Study and Development
of Taxonomy, (Key Noun, Modifier
and attributes, Classification
Standards adoption)
Manual Classification of Master Data
16-22
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Competency Details
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16
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.
Service Offerings
Spend Analysis
Strategic Sourcing
Low cost Country
Sourcing
Sourcing Support &
Auctions
SRM Package
Evaluation
eTendering
Invoice Processing
Procurement
Operations
Contract
Management
SRM Product
Implementation
Maintenance &
Support
Supplier
Collaboration
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
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Tech Mahindra Spend Management Practice
Maturity Snapshot
Product Focus
Vertical Focus
7%
5%
10%
Ariba
CPG
10%
7%
i2
10%
Financial Services
10%
Zycus
SAP SRM
Pharmaceuticals
5%
Oil & Energy
53%
Oracle iProcure
17%
10%
30%
Manufacturing
Strategic Sourcing
Retail
3%
PMs/Functional
Consultants
3%
Services Focus
11%
20%
Automotive/Aeronautic
al
Process Focus
7%
Procurement
Consulting
7%
5%
5%
5%
5%
5%
5%
3%
Spend Analysis
20%
Sourcing
Strategic Sourcing
Contract Management
20%
Spend Data
Management/ Spend
Analysis
10%
Procure-2-Pay
Invoice
Product
Implementation
Supplier Management
40%
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Maintenance &
Support
52%
18
Marquee Spend Management Customers
Keppel Tatlee Bank
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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
 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
 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
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22
Dashboard Solution for CAT, USA
Business Scenario
Solution Architecture
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
Objective
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.
Benefits
Helps in performing health check of business
activity through monitoring of critical KPIs.
Single version of truth available across the
organization.
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.
Technology Stack
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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 )
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Why Tech Mahindra?
Our Differentiators
Proven ability in building new competencies
Rich experience
Successful transition of complex business applications from incumbent vendor
Successfully delivered similar contract management solutions for larger enterprises
Long list of customers in both Process evaluation, design and Technology consulting services
Thought Leader
Global player, trusted advisor with proven track record, who imparts in value creation for your
Leadership buy in for our methodology and commitment to make it succeed for mutual benefit.
Professionally qualified consultants, possess huge experience in Supply chain process and technology
Consultant profile
Certifications in Six sigma, Project Management, relevant products with ability to deal all levels of users
Conscious of business dynamics, cost drivers; added value to both organizations and it’s key user community
Innovated a set of tools (Process assessment, analysis, diagnosis tools) that results in faster execution
Process, Tools and
Templates
Wealth of templates, Six Sigma, Lean management, Score cards
Project and Change management templates that enable consistent and flawless results
Right Alliances,
Partners
Leveraging MSat ‘s SRM Consulting practice for Product +Implementation experience, Satyam BPO for Business
process Outsourcing expertise, MSat Bridge Consulting, and our leading product alliances
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Thank you
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