1.
Maintenance Predictive ou comment le
Big Data révolutionne les usines du futur
AIE Suresnes, 26 Septembre 2016
Capgemini, Capgemini Consulting, Sogeti HT

2.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 2
Table of Contents
 Enjeux, contexte et bénéfices
 Solutions techniques Big Data
 Applications IBM PMQ et Braincube

3.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 3
 Manufacturing Intelligence => Braincube
 Predictive Maintenance => PMQ
Faurecia Digital Enterprise Project
3- Prepare
Rapid
Scale-Up
2- Experiment
and Learn
1- Explore &
Design
FEB. 2015 SEPT. 2015
200 digital
use cases
40 Proofs
of concept
9 solutions
Deploy
40 sites
Deploy
40 sites
END. 2018
2016 2017 2018
Pilot
6 sites
Industrialize
Deploy
14 sites
A systemic approach, at the speed of light

4.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 4
Digital Enterprise
Manufacturing Intelligence & Predictive Maintenance
 Big data benefits
Why do we implement Big Data initiative?
Improve
productivity
 OEE*, Improve production flows, stock, …
 Optimization cost of energy, utilities, indirect cost
 Accelerate run at rate (loss of raw material, FMC)
Run Plant
respecting
standards
Reduce
product
quality issues
 Reduce scrap
 Anticipation of non-quality with alerts and recommendations
Reduce key
equipment
issues
 Minimize unscheduled downtime and breakdowns
 Manage business opportunities such as insourcing capacity
 Increased equipment life cycle
(*) OEE stand for Overall Equipment Effectiveness (« Taux de Rendement Synthétique » in French)
Manufacturing
Intelligence
Monitor production process in real time
And make decisions based on data
Predictive
Maintenance
Predict potential breakdowns of a machine
through data analysis and historian
2 families of Big Data
tools in Operations
 Monitor & alert in real time production parameters
 Display tuning information to the operator on the shop floor
 Keep production line stability for all shifts
 Benchmark plants

5.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 5
Table of Contents
 Enjeux, contexte et bénéfices
 Solutions techniques Big Data
 Applications IBM PMQ et Braincube

6.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 6
Commencer par démontrer l’intérêt d’une architecture Big Data
au centre de la solution globale via un pilote
 A pilot…
 In time boxing (3 months on Big Insights environment with plants data)
 Thru simulated flow in a first step and then connected to plants
 Real-time data flows implementation, reusable for industrialization
 Analytics : demo of some possibilities
Manufacturing
Intelligence
(Braincube)
Predictive
Maintenance
(IBM PMQ)
Plants Plants …
sensors sensors
1
2
3
3
4 4
1
2
3
4
IBM Cloud/Hadoop infrastructures
One shot data initialization
Real time simulation alimentation
Direct real time alimentation
3
2
5
5 Analytics & discovery
Open Data, External
Data, etc.

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Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 7
Définir l’architecture Big Data cible en fonction des besoins
Architecture Framework for Predictive Maintenance
Simplified Architecture Functions and Technologies
❶ Data ingestion of
Ticketing Data and
Traceability Data
❷ Data storage of Process Data,
Traceability Data and Ticketing
Data
Ticketing
Data
Traceability
Data
SAP logs Other Data
❸ Processing to calculate KPI’s,
traceability and graphs
preparation
❹Visualization of
KPI’s
Predictive Maintenance
(IBM PMQ)
Usage
Analytics Visualization API /
Drivers
Structuration
Processing SQL
NoSQL
Storage
Hadoop
HDFS
Warehouse In memory
Ingestion
Batch
Micro Batch Real time
1
2
3
4
1
2
3
4
❶ Real time ingestion of
Process Data from
Plants
❷ In memory storage of Process Data
❸ Trans-coding for PMQ
and Braincube
❹ Publishing to PMQ with Kafka and
Braincube with HTTPs
Manufacturing Intelligence
(Braincube)
Process
Data
Kafka
Kafka
Kafka
BigInsights
3
5
❺Data Discovery
❶ Batch layer ❶ Stream layer

8.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 8
Retour concrets et intérêts du Big Data
❶ Single point of entry
- reduce the load on PCo side
- distribute the process data to all analytical
components
❷ Storage capacities
- centralization of data in one place
- available for any type of request from MI/PM
❸ Analytics & discovery
- computing power for custom analytics
- direct analytical functions
❹ Data Publishing
- compatible with current & new partners
- custom data visualization
Manufacturing
Intelligence
(Braincube)
Predictive
Maintenance
(IBM PMQ)
PCOOther Data
Big Data
TraçaStratos

9.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 9
Quelques visualisations possibles des données dans HDFS
Ingestion
Plants
Monitoring
Storage
Processing
Visualization
Plants
Processing
Parts
Traceability
IT Ticketing
Flat filesExternal
Databases
Real Time
Process Data

10.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 10
Table of Contents
 Enjeux, contexte et bénéfices
 Solutions techniques Big Data
 Applications IBM PMQ et Braincube

11.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 11
Predictive Maintenance – Principe et mise en œuvre avec PMQ
Visualisation
& Usage
Data AnalysisData Storage & StructurationData Collect
7.5
5 min.
DATA COLLECTION DATA STRUCTURATION MODEL & ANALYSE DEPLOY & IMPROVE
OBJECTIVES & DATA
IDENTIFICATION
 Define clear objectives
 Identify if relevant data are
available
 Prepare Change
MIPM DEPLOYMENT
 Industrial IS
 Machines connected
 Data collection
 Secure & scalable
 Data structuration
 Data Lake
 Analytics platform
 Monitoring
 Modeling
 Dashboarding
 Deployment
 Adapt, optimize
 Change management
1. Récupération des données du data lake en temps réel
2. Traitement sur intervalles puis mise à disposition d’un
modèle prédictif (algorithme)
3. Le modèle établit un score d’anomalies
4. Interprétation et décision
 Machine learning : Détection d’anomalies corrélée à une
base d’apprentissage et de connaissances.
 Performance: Disposer de modèles pertinents avec des
données significatives , d’un contexte métier et des process.

12.
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Predictive Maintenance – Illustration avec machine de Fine blanking

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Presentation Title | Date
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Into data: a concrete example on Big data for an automotive supplier
Data Driven Production
• Manufacturing
Intelligence
What we wanted to achieve with BIG DATA
 Reduce scraps
 Quickly investigate a production problem
19
Equipment
on the line
A measure
every 1s
60 000 s
in a production
day
220 days of
production
> 20 parameters
by equipment
followed in real time
X X
5 Billions
data available for analyse in 1
year of production
XX =
BRAINCUBE
Solution

18.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 18
How we can do that: Reduce scraps on dashboard
« It is not knowing what to do, it’s
doing what you know »
Anthony Robbins
2015 06 Scrap at the FRIMO
Manufacturing intelligence is
about undestanding what
makes your production green
and repeat it
Guides &
Rules

19.
Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 19
Braincube found a way to adjust production settings that reduce
scraps
Rule – RHD2
Lookint at only 2
parameters
combined
(temperature
galvano &
thickness) ...
1
: Good parts
went from 96 to
98,2%
4
...we were 3,8%
time with a setting
that generate few
scraps...
The analytics say
that we could be
up to 40% time in
this favourable
situation
4
And
save
M€ ! 5
...During the
past 27
days...
32

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Presentation Title | Date
Copyright © 2016 Capgemini and Sogeti. All rights reserved. 20
A collaborative plateform to share the production status in real time
FROM DATA TO
FACTS BASED
ACTIONS ON THE
PRODUCTION LINE
Manufacturing
Intelligence
Site manager, COO, BU manager
•Production line manager
•Quality manager
•Methods
•Process engineering
•Operator on the shop floor

21.
www.capgemini.com
The information contained in this presentation is proprietary.
Copyright © 2016 Capgemini and Sogeti. All rights reserved.
Rightshore® is a trademark belonging to Capgemini.
www.sogeti.com
About Capgemini and Sogeti
With more than 180,000 people in over 40 countries, Capgemini is a
global leader in consulting, technology and outsourcing services. The
Group reported 2015 global revenues of EUR 11.9 billion. Together
with its clients, Capgemini creates and delivers business, technology
and digital solutions that fit their needs, enabling them to achieve
innovation and competitiveness. A deeply multicultural organization,
Capgemini has developed its own way of working, the Collaborative
Business Experience™, and draws on Rightshore®, its worldwide
delivery model.
Sogeti is a leading provider of technology and software testing,
specializing in Application, Infrastructure and Engineering
Services. Sogeti offers cutting-edge solutions around Testing,
Business Intelligence & Analytics, Mobile, Cloud and Cyber
Security. Sogeti brings together more than 23,000 professionals in
15 countries and has a strong local presence in over 100 locations
in Europe, USA and India. Sogeti is a wholly-owned subsidiary of
Cap Gemini S.A., listed on the Paris Stock Exchange.