This document discusses Industry 4.0 and the digital transformation of industry. It describes key technological pillars like the Internet of Things, additive manufacturing, and data analytics. It provides examples of how these technologies can be applied through predictive maintenance and customized products. The document also introduces Atos Codex, an open industrial analytics platform that uses big data, high performance computing, and machine learning to deliver business insights and solutions.

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INDUSTRIA 4.0
CONTEXTO
DESAFÍOS DE LA INDUSTRIA 4.0
TRANSFORMACIÓN DIGITAL
PALANCAS TECNOLÓGICAS
IOT, FABRICACIÓN ADITIVA, ANALÍTICA DE DATOS
BIG DATA ANALYTICS
ATOS CODEX
CASOS DE USO
APLICACIONES PRÁCTICAS EN INDUSTRIA 4.0

3. El término Industria 4.0 fue acuñado por el gobierno
alemán para describir la fábrica inteligente, una visión de
la fabricación informatizada con todos los procesos
interconectados por Internet de las Cosas ( IOT )
El nuevo modelo de industria centrada en los datos
requiere de una transformación profunda

4. Digital
Enterprise
Industrial
Data
Analytics
Industry 4.0
Internet of
Things
Digital
Trans-
formation
ERP
PLM
MES
Additive
Manufacturing
Predictive
Maintenance
Virtual
Reality
IoT
HIGH PERFORMANCE
COMPUTING
Translational Genomics
ADVANCED ANALYTICS
Analyzing in real-time
customer, products, assets
and partner data to gain insights
as source for growth
INTERNET OF THINGS
▶ Connected Vehicle
▶ Connected Machines
▶ Connected Patients
▶ Connected Infrastructure
BUSINESS REINVENTION
to transform your processes
& business models
Cyber
Security
HPC
Tomorrow’s
Enterprise
Best in class
manufacturers are able to
use the potential of each
domain as lever to unlock
the full business potential
of a Digital World

5. New Tech-companies
entering the market
Increasing complexity of
products and production
High dependency of
suppliers through
integration into the value
chain
Risks
Chances
Source: Atos
New products and
services increase
customer value through
Digitalization
Demand of customers to
get more individualized
products
Extending the own value
chain through new
services

6. While today's enterprises are linearly organized
and optimized within the boundaries
of organizational and system silos…
PAST
… companies of the future
will fulfill individual customer needs by using
collaborative and agile network of capabilities
FUTURE
Are you ready?

7. USER
CUSTOMER
SUPPLIER
PARTNER
Connected
Processes
CUSTOMER
CONSUMER
R&D
PARTNER
Connected
Customers
Connected
Products Connected
Consumers
Connected
R&D
Connected
Suppliers
Connected
Boardroom

8. Cyber
Physical
Systems
Internet of Things
& Services
Big Data
Analytics
Social/
Collaboration
Digital
Manufacturing
Cloud
Mobility/
wearables
Open
Standards
Virtualization
Decentralization
Real-Time
Capability/
Responsiveness
Service
Orientation
Modularity
CONNECTED PEOPLE CONNECTED THINGS
CONNECTED ENTERPRISE CONNECTED APPLICATIONS
Product
engineers
Process
engineers
Production
planners
Sales
Shopfloor
perators
Supervisors
Service
engineers
Quality
engineers
Maintenance
engineers
ERP
MES
PLM
SCADA
CAPP
EAI
DNC
Office PCs
Smart phones
Equipment PLC’s
Smart
Products
RFID
Shop Floor
touchscreens
CNC
Tablets
COLLABORATION
INTEGRATION
Suppliers Customers
Partners
Consumer
Manufacturer
Plants
Corporate
Intranet
Internet
Scanners
Wearablers
Label
Printers
QMSCRM
DATA
3DPrinters

9.  Rapid Prototype
 Enhanced Value Chain
 Cost Saving
 Distributed Production
Additive Manufacturing
 Remote Support
 Dangerous Environments
 Supported Training
 Enhanced Immersive Work
AR/VR
Journey towards Digital Enterprise
 Connected Machines
 Connected Worker
 Connected Products
 Connected Customer
 Cybersecurity
Industry 4.0

10.  Connected Devices
 Smart Devices
 Massive Data Information
 Interconnect Systems
 IoT Platforms
 Mindsphere
IoT
 Big Data Management
 High Performance Computing
 Plant Data Use Case:
 Predictive & Prescriptive
Maintenance
 Digitalized Field Services
Advanced Analytics
 Machine Learning
 Deep Learning
 Cognitive Computing
 Real Time Processing
 Cognitive Automation
Machine Intelligence
Journey towards Digital Enterprise

11. Los datos
aportan
eficiencia a los
procesos
Los datos ayudan
a mejorar la
experiencia
de los clientes y
ciudadanos
Mainframes Web & mobile Internet
of Things
70’s 90’s 10’s
Los datos
permiten
transformar
los negocios
y servicios
1. IoT - IoE
2. Big data
3. Exascale computing
4. Cognitive Computing
5. Artificial Intelligence
6. 5G
7. Blockchains
8. Swarm computing
9. Quantum computing
10.Quantum safe
encryption

13. Many customer organizations state today that:
▶ They are still in an early stages of Big Data projects
▶ Their current priority is to get help to drive business value from analytics
BI
Big Data
Insight Systems
Robotics & Cognitive
Track
performance
Analyze
behaviors
Predict & adapt
in real time
Provide cognitive
intelligence
Descriptive &
diagnosis analysis
First capabilities for
predictive analysis
Prescriptive analysis
& insight to action
IA & prescriptive
automation

14. Atos Codex Analytics
Atos Codex Platform
Infrastructure
Cognitive Solutions
▶ Analytics components
▶ Analytics use cases
▶ Pre-developed vertical
algorithms
▶ Visualization tools
▶ Appliances
▶ Security
▶ Middleware
▶ Abstraction layer for Cloud
▶ BDaaP
▶ Big Data Capabilities
Framework
▶ APIs
▶ Compute (bullion)
▶ HPC
▶ Public & Private Cloud
▶ Machine learning
▶ Speech recognition
▶ Image analysis
▶ Natural language processing
Atos Codex
connectors
▶ Telco connectors
▶ Smart metering
▶ Smartgrid
▶ Sensors
▶ APIs to IoT “world”
▶ IoT devices
▶ IoT connectors
▶ IoT platforms
Security ▶ Security Products▶ Security Management

15. Accelerators, templated solutions and fast
incubation services to develop at start-up
speed
Ready to deploy use cases to
build best-in-class business
solutions
Open Exascale-class supercomputing and
appliances to provide exceptional outcomes
2. Customer design labs (FabLabs)
3. Open industrial
analytic platform
1. Methodology & Data Science
- Ideator
4. High-Performance
Data Analytics
As a service, on premise and BPO plat-
forms to boost analytics agility & TCO fully
embedded with our IOT solutions-
Agile analytics consulting
and Proof-of-Value sprints
to transform data into
profitable insights
5. Industrialize best
practices and use cases

17. Leading Agricultural Machinery Manufacturer
• Delivery of a big data platform with >99% availability
• Management of data gathered every 3 minutes from 180
sensors on each machine
• Processing, storing and analyzing terabytes of live telematics
and external data
• Collection and real-time analysis of hi-fidelity and hi-
definition agricultural data at 9 Hertz of data per minute.
• 250,000 vehicles providing real-time machine data
• New platform base for new business models e.g. providing
predictions and recommendations for precision farming to
farmers
Renault
• Business and technology partner for Renaults R-Link
• Atos provides a Connected Vehicle Platform incl. On-Board
Software, Service Integration Gateway , M2M Connectivity,
Business Process Integration , Commercial Catalogue and
App Store
• R-Link establishes the connection between car, driver and
OEM (owned by Renault)
• Customer touch points are multiplied and a direct sales
channel is established via an in-car app store allowing for
driver-specific and context-related purchases.

18. Schlumberger
Predictive Maintenance
• Improving Uptime for an Oil & Gas Company + Reducing cost
of failure by Improvement of “Mean time between Failure
(MTBF) and Non-productive Time (NPT) = direct impact on
bottom-line
• Approach: Agile Team-set (Data Scientist, Industry Expert and
SI Architect) + Cloud Based Analytics Platform
• Transparency of Root causes of failures
• New maintenance policies defined
• Mean time between failure (MBTF) significantly improved
CPG Manufacturer
• Need for real-time technology solution for customer branded oil
• Customer chose Atos MPM a unique, highly effective real-time
operational intelligence system that drives significant line
performance improvement
• MPM uses theory of constraints to prioritize actions and enable
optimal line output
• MPM uses real-time data from each piece of equipment on the
line combined with product specific master data and actual
conveyor buffer quantities as the basis for its calculations
• It provided for Significant line performance improvements from
1st month on

19. It is not the strongest of
the species that
survives
but the most adaptable
to changes
(C.Darwing)

20. Atos, the Atos logo, Atos Codex, Atos Consulting, Atos Worldgrid, Worldline,
BlueKiwi, Bull, Canopy the Open Cloud Company, Unify, Yunano, Zero Email,
Zero Email Certified and The Zero Email Company are registered trademarks of
the Atos group. April 2016. © 2016 Atos. Confidential information owned by
Atos, to be used by the recipient only. This document, or any part of it, may not
be reproduced, copied, circulated and/or distributed nor quoted without prior
written approval from Atos.
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