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Harnessing the Power of Big Data in Global Manufacturing
Soma Muruganandam
Director- Business Intelligence
McCain Foods Limited
3 Creating Smiles Around The World
Background, BI Strategy
Background
Russia
New Zealand
Papua-New Guinea
Australia
Philippines
Japan
Brunei
Taiwan
South Korea
North Korea
Indonesia
Malaysia
Vietnam
Cambodia
LaosMyanmar
Sri Lanka
Madagascar
India
Mongolia
China
Mozambique
South Africa
Malawi
Pakistan
Zimbabwe
Botswana
Kirgistan
Tadschikistan
Somalia
Afghanistan
Tanzania
Namibia
Sambia
Kenya
Dschibuti
Qatar
Yemen
Uganda
Ethiopia
Usbekistan
Angola
Turkmenistan
Eritrea
Iran
Zaire
Saudi-Arabia
Sudan
Iraq
Kazakhstan
Azerbaijan
Congo
Argentina
Jordan
Central Africa
Chile
Gabun
Israel
Syria
Lebanon
Georgia
Cameroon
Chad
Egypt
Cyprus
Nigeria
Turkey
Benin
Togo
Ghana
Libya
Greece
Niger
Bulgaria
Ivory Coast
Burkina Faso
Liberia
Ukraine
Serbia
Romania
Tunesia
Sierra Leone
Belarus
Mali
Guinea
Brazil
Hungaria
Algeria
Lithuania
Italy
Guinea-Bissau
Estonia
Latvia
SenegalGambia
Poland
Mauretania
Finnland
Bolivia
West Sahara
Germany
Marocco
Sweden
Denmark
Spain
France
Belgium
Netherlands
Portugal
Peru
Norway
French-Guayana
United Kingdom
Guyana
Ireland
Columbia
Ecuador
Venezuela
Trinidad
Panama
Puerto Rico
Costa Rica
Dominican RepublicHaiti
Nicaragua
Jamaica
Honduras
El Salvador
Iceland
Belize
CubaMexico
USA
Canada
NepalBhutan
Bangladesh
Thailand
Uruguay
Guatemala
Kuwait
Oman
United Arab Emirates
Lesotho
Swaziland
Burundi
Rwanda
Croatia Bosnia-Herzegovina
Austria
Switzerland
Luxemburg
Moldawia
Albania
Macedonia
Montenegro
Slovenia
Czech Republic
Slovakia
Armenia
Paraguay
Surinam
Canada and MII
USA
South America
Power Play
SDW v4.2 + SCM PDB
Perfect Order Data Mart
(Approx. 1,000+ reports)
OSA
SCM Datamart
PDB
(Approx. 2,000+ reports)
SDW v4.0
PDB
(Approx. 50+ reports)
Perfect Order Data Mart
CEU
SDW v4.2
PDB
(Approx. 1,000+ reports)
UK, PAS
PDB
(Approx. 500+ reports)
South Africa
PDB
(Approx. 200 reports)
Australia, NZ, Taiwan, Japan
SDW v4.3
(1000+ reports)
Russia
OSA
(Approx. 70 reports)
Power Play
OSA
OSA
PDB
Our Environment
Situation – Over 20,000 Crystal reports (approx 60% duplication)
– Over 3,000 Access Databases
– Unknown amount of Excel “Reporting Systems”
– Data loads – uncontrolled and unreliable (extracts, SQL, Excel loads, SSIS/DTS,etc)
– Disk space usage growing by approx 45+% per year (industry average is 23%)
– End Users cannot get to the answer they need without hours/days of work and help
Technology – Technology is unsupported – COGNOS Powerplay, ORACLE Sales Analyser
– Database is not designed for “Ad-hoc” type scalability and usability (SQL Server)
– No presentation tools – all ad hoc
– Labor intensive Data management and report management
– Data loading EVERYWHERE – Extracts, Excel, Microsoft ACCESS
Process / Method / Organization – Multiple version of the truth – Everyone builds there own version of a database
– Methodology for Requirements Gathering is effectively bottom up
– Deliverables are typically “data focused” not “business focused”
– Great number of shadow systems
– Full-time Data Downloader's and report builders – all on the fly
Inability to get a an answer quickly, easily and on demand
Goal, Mission
Goal
To create an enterprise-wide BI solution that helps the business achieve the strategic, tactical and operational goals of the McCain Group of companies
by consolidating the main areas of the business as well as providing a global view across regions.
Mission • Eliminate information gaps by creating a business intelligence system that
gives people the RIGHT information they need to do their jobs • Reduce time spent in gathering information and increase time for analysis
to reach better decisions
8
McCain BI Program
BI Vision
Planning
Architecture Roadmap / Implementation Strategy
Processes and Structure
Execution
Pilot Project BI Iterations BI Iterations
BI Program Strategy
Data Architecture
Technical Architecture
Application Architecture
9
Architecture
Evaluate and select Back-end Technology
Evaluate and Select ETL Tool
Evaluate and Select Presentation Tools
Determine readiness of Teradata’s LDM
Define Logical Data Model
Identify Goals and Constraints
Identify significant components of EDW
Architecture Application Architecture
Data Architecture
Technical Architecture
ETL Layer (IBM Infosphere)
Data Warehouse Layer (Teradata)
Front End Layer (MicroStrategy)
Industry MFG LDM
3NF Physical Data Model
Semantic Models for Report Optimization
Staging
Extract Trans form
Load
Cleanse (Standardize, Merge,
correct)
Understand (Data assessment)
Scorecards
Reports Ad-hoc
PDA, iPhone, iPad
Dashboards
MS office Integration
Web Portal Integration
PRMS
QC
AG
POS PLC
EDW Architecture
We have assembled a world-class BI tools that guarantee scalability, reliability and minimize risk
Business Warehouse
Standard (SAP-API) Extractors
3NF Physical Data Model
Semantic Models for Report Optimization
Staging
IBM InfoSphere (ETL)
Teradata (EDW)
ECC
CRM
SRM
SCM
Front End Layer (SAP Bex)
Front End Layer (MicroStrategy)
PRMS
QC
AG
POS PLC
Open Hub
SAP Portal
Sales Reporting, OEE, Retail consumer analysis,
SAP-BW & Teradata Architechture
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Assess Value
Decide on Top-Down or Bottom Up approach
Create business-driven priority list
Select and Schedule Pilot Project
Schedule BI Iterations based on prioritization
Create McCain EDW Roadmap
Implementation Strategy
Acquire Teradata’s EDWr (if Top-Down)
Roadmap / Implementation Strategy
13
13
Guiding Principles
Enterprise-wide initiative No more data marts – Global model and global database for all regions Global BI-Front End (Reporting)
Data for all companies to be loaded at the same time
No more regional implementations
A Global Reporting Governance Committee to be established by subject area Purpose: approve and prioritize reporting requirements
Ad-hoc Reporting to be available in a controlled basis
Governance to be enforced by Global Reporting Governance committee
Data Assessment will determine state of data but results will not stop iterations unless data elements are not available A Data Quality / Clean-up process should start in parallel to ensure cleanliness of data
Only MicroStrategy’s out-of-the-box functionality will be used
Business to prioritize subject areas and iterations; however, final priorities will depend on
feasibility
14
Processes and Structure
Define Org. Structure: (Steering committee, Teams, Roles and Respons.)
Identify skills required, gaps, training and staffing requirements
Define Governance Model And Standards
Define Methodologies to use
Processes and Structure
15
EDW High Level Process Global Business
Requirements Discovery
Global BI Discovery
(Identification of Dashboards, Standard
Reports and Ad-Hoc templates Needed) Master Data
Clean-up
Prototyping
Development
Subject Area Level
Scope
Master Data
Clean-up Master Data
Clean-up
Data Discovery
(Identify source data and
exam “fit for purpose”)
Scope /
Iterations
UAT
Implementation
Iteration 1
Prototyping
Development
UAT
Implementation
Iteration N
Iteration Level
16
BI Governance Team
BI Business Lead
BI Team Lead
Steering Committee
CFO, CIO, Regional VP’s,
Regional Representative(s)
Regional Business Stakeholders
Sales Marketing SCM
Regional Representative(s)
Regional Business Stakeholders
Sales Marketing SCM
Project
Manager
EDW Development
Team
Data
BI
BI Organization
Offshore Development
McCain Development
team
Microstrategy GDC
Teradata (India & Manila)
IBM
BI Organization
Our BI Apps – Where the Fun Starts
19
Overall Equipment Effectiveness
In an ideal factory, equipment would operate 100 percent of the time at 100 percent
capacity, producing 100 percent good quality. In real life, however, this rarely happens.
OEE is a comprehensive measure comparing how well a plant and its equipment is
running to this ideal
OEE is a simple and powerful tool that can help identify opportunities and measure
improvement
It reduces complex production problems into a simple, actionable presentation of
information
The OEE data provides defined and quantifiable reasons for poor equipment
performance. These can then be prioritized, used for root cause analysis, and problem
elimination progress.
OEE – Summary Global/Region/Plant view
OEE- Plant Dashboard -OEE measures and Downtime
Plant Opportunities - Last year Vs This year
OEE Primary Losses Global /Region /Plant View
Timaru Manufacturing Plant floor - Australia
24
Sales BI Solution Samples
Executive Sales Dashboard
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We are heading to….
Predictive Analytics
McCain Predictive Analytics - Field to Fork
Creating Smiles Around The World