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

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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

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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

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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

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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