1.
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Olivier Allard Ing.
Directeur Simulation – MAYA HTT
La gestion de projet
dans l’aire Industrie 4.0

2.
Agenda
Olivier Allard Ing.
MAYA HTT
Industrie 4.0
• La donnée
• La technologie
• La gestion de projet
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3.
Olivier Allard Ing.
L’innovation technologique est une passion
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4.
Un parcours particulier …
Polytechnique de Montréal -Innovation technologique
Formateur logiciel de maintenance
Représentant logiciel
CEO – co-fondateur
Représentant logiciel
Directeur Simulation logiciel … Innovation technologique
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5.
mayahtt.com

6.
Investissement
11 Milliards
sur 11 ans pour
les acquisitions
6
MAYA / Siemens
#1
Compagnie de
simulation digitale au
Canada et partenaire
en simulation pour
Siemens PLM
140,000 clients
Les 100 meilleurs clients de Siemens PLM
Software utilisent cette technologie depuis plus de
19 ans.
L’innovation avec
45
Brevets,
en moins de 2 ans.
Intelligence artificielle,
Développement de logiciel,
Suivi en temps reel
L’expertise MAYA HTT est vaste
Plus de
7 millions
d’utilisateurs à
travers le monde
210 190+ employées
75 % Ingénieurs et Scientifique
22 % Docteurs dans leur domaine
30 % Maitres dans leur domaine
35+ Solutions
supportées par MAYA
HTT pour Siemens PLM
Expérience
Plus de 10 000 000 Voitures
Plus de 10 000 Moteur d’avion
Plus de 1 000 Projets d’ingénierie
Plus de 50 Satellites en orbite
1 Excellent baton d’hockey
1 millionsD’étudiants supportez dans plus de 3000
institutions à travers le monde, incluant toutes les
grandes universités du Québec.

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Nos créneaux et clients
Optimization & Simulation
Getting the best out of your product, processes
and technology
Industry 4.0
Data - anywhere, anytime, everyone
Make the right Biz / Eng/ Ops decisions
Software development
Define, build, deploy and maintain
robust commercial applications
Applied Artificial Intelligence
Harvest the value of your data
Uncover what the human eye cant see

8.
Numérisation
Ça change
TOUT!!!

9.
Industrie 4.0
L’ère de changement technologique …
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10.
Phase 1
Production Mécanique
Phase 2
Electrification
Phase 3
Automatisaton
Phase 4
Numérisation
Quel est votre niveau de maturité?
L’évolution industrielle
Siemens a été au cœur de chacune des phases

11.
Digital Product Digital Production Digital Performance
Holistic Digital Twin

12.
La donnée
L’or de d’aujourd’hui …
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La valeur des données

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Les risques des données

15.
Le parcours de la donnée
La collecte et visualisation
L’interprétation et intégration
Apprentissage et prédiction
Optimisation et automatisation
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Valorisation de la donnée
Wisdom
Knowledge
Information
Data
Insight (why). What is best.
Meaning (how)
Context (who, what, when, where
Raw Data Collection

17.
La collecte
Différentes sources
Différents formats
Différents besoins
« Garbage in, Garbage out »
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La visualisation
0D, 1D, 2D, 3D, 4D … 5D
Rapport vs Dashboard
Publique vs. privée
Page Web, App, email, …
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L’intégration
Relation des données
Le danger du « timestamp »
Snap shot vs. Historique
Les unités de mesure
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L’interprétation
Expertise du domaine
Expertise « science de la donnée »
Snap shot vs. Historique
Le Danger des moyennes
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21.
La technologie
L’internet des objets (IOT),
Info-Nuagique (Cloud) ,
Intelligence Artificielle (AI) …
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22.
Introduction
What year was this referring to?
“In … , while studying mathematics at Princeton, he built
the first learning machine, an artificial neural network…”
Source: MIT Technology Review, October 2015
What year was this referring to?
“In 1951, while studying mathematics at Princeton, he
(Marvin Minsky) built the first learning machine, an artificial
neural network built from vacuum tubes called the Stochastic
Neural Analog Reinforcement Calculator, or SNARC. Shortly
after that, he turned his attention toward the manipulation of
logic and symbols using computers, which guided his later
work on artificial intelligence.
In 1959, together with the computer scientist John McCarthy,
Minsky founded the Artificial Intelligence Laboratory at MIT.”
Source: MIT Technology Review, October 2015

23.
IA vs Humain : Perception …
Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein,
Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.
14M images…
Vraiment?

24.
IA vs Humain : Compréhension Langue Naturelle (NLP)
100+ langues…
Vraiment?

25.
Discrete Manufacturing
10 manufacturing stages
20,000 data points
4+ years of data
Product performance
variance and rejection rate
Fleet Optimization
~100 data points per asset
Large fleet of assets
Large unplanned fuel OPEX
Increasing ops anomalies
Engineering Simulations
Computation fluid dynamics
15 minute solve time
Long iterative process
Non-optimized design
Savings of $400k USD/year
 AI with 99.52% accuracy in early manufacturing
stage QA failure prediction
 Large savings per year by removing bad
material early in production & less re-work
Reduced OPEX
 Daily anomaly detection tool for efficient fleet
management & operations
 Business risk identified: OPEX fuel
consumption outlier deviations (cost
avoidance)
900x efficiency gain
 Less than 1 second solve time
 Less than 1% error rate compared to results
generated by full simulation
Applied AI
&
Analytics
Des cas réels appliqués

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Correlation Mapping
• Analysis of top
correlations within the
identified ~50 features
• 9 correlation sets (18
features) identified to
study for causation
Causation Analysis
• In collaboration with the
customer SME, identify
high likelihood of
causation
• Individual or multi-
feature pattern identified
to reduce rejection rate
Example of a single
feature pass/fail graph
Example of 2 features
correlation pattern
AI Training
1) Trained ~130 DNNs
2) Optimized 2 best deep
neural network
candidates
AI training validation
results
• Pass : 99.87% accurate
• Fail/rejection prediction:
77.82% accurate
AI feature sensitivity
analysis
• ~50 out of 1000+
features with higher
influence on outcome
prediction
Conclusions
• Early manufacturing
stage failure
prediction possible
with applied AI
• Removal of bad
materials and
unnecessary
production time and
re-work time =
• Gained insight to
guide production
regarding sensitive
settings and
operations
• Established tighter
production thresholds
at key manufacturing
stages and machine
parameters to reduce
rejections rates
• Cleaned up
systematic erroneous
data entry
Prediction accuracy: 99.52%
~$400k / year in
savings
Correlation Array
Correlation set
from AI-based
feature
sensitivity results
2 features
failure pattern
Business
 Identify AI target project:
 Need to reduce
manufactured products
QA rejection rate
 Need higher consistency
in battery performance
variability
Data Sources Access
1) PI System (4+ years)
2) SQL (6+ years)
3) Proprietary ERP DB (6+ years)
Data Manipulations
1) PI Asset Framework structure
validation and data reference
augmentation with SQL flags
2) PI Event Frame creation by
combining SQL-based
manufacturing start/end flags
and PI System tags
3) Data mapping, SQL queries,
cleanup, normalization, stats…
S1 S2 S3 S4 S5 … S10
10 Manufacturing stages
~20,000 PI tags over 4+ years
• Rejection rate
• High cost of re-
work & disposal
S1-1 S1-2
AI feature
sensitivity
analysis
Single data
point failure
thresholds
STEP 1
BUSINESS NEEDS &
USE CASES
STEP 2
DATA PRE-
PROCESSING
STEP 5
AI BUSINESS
RESULTS
STEP 3
AI CREATION
& OPTIMIZATION
STEP 4
AI PROBATION
& SME
VALIDATION
Total Applied AI Project Duration : ~3-6 months

27.
Retour sur l’investissement pour 4.0

28.
Maturité de votre évolution 4.0

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MAXIMIZE
MINIMIZE
INCUBATION GROWTH MATURITY DECLINE END OF LIFE
$+
$-
First product
delivery
Peak
RetireBreak Even
Boost
Productivity
Reduce
Cost
Speed to
Market
Extend
Returns
Increase
Revenue
Time
Pourquoi … Industrie 4.0 ?

30.
La gestion de projet 4.0
Seul on va vite,
Ensemble on va LOIN …
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Les 3 “i” de votre succès en évolution 4.0

32.
Les 6 dimensions à considérer

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Audit
•High-level objectives
•Gap-Analysis
•Data gathering
•Network architecture
Concept
Definition
•Workflow &
storyboard
presentation
•Exploration map
Alignment
Meeting
•Requirements
•Formal notes
Functional
Specification
•How it will work
Statement of
Work
•Work breakdown,
cost and schedule
Project
Kick-off
La phase 0
Deliverable Deliverable

34.
Phase 1, 2, 3 …Phase 0
Alignment
•Business needs
•Sensors & Data gaps
•System Architecture
Definition
•Roles, Security
Access, workflow
Process &
Configuration
Definition
• Project Schedule
Solution
Definition
• Plan to implement
and deploy
architecture
• Plan to configure
environment
• Data migration tool
definition
• Instrumentation
and sensors
selection
Solution
Installation
• Installation in your
environment
• Data model testing
and deployment
• AI model
investigations
Configuration
• System user
creation (roles, etc)
• System
Configuration
•Real-time
dashboarding
• Workflow
• User Interfaces
• AI training and
operational
deployment
Training
• User Training
• Admin. Training
Integration of
other systems
• ERP Integration
• Data migration
Gestion de projet

35.
Qui est affecté par la révolution 4.0 ?
Engineering
•Quality
•Change mgt
•Systems
level model
•Root cause
analysis
•Surrogate
models
•Optimization
Manufacturing
•Product
Quality
issues
•Performance
specification
issues
•Rejections
rates
•Env. factors
•Supplier
issues
Operations
•Downtime?
•Incidents?
•Telemetry
data quality
•Performance
analysis
•Root cause
of failures
•Energy
efficiency
Marketing
•Sales
prediction
data
•Product
features
analysis
•Consummer
purchasing
experience
optimization
Sales
•Performance
correlations
•Price history
•Recommend
ation tools
Customer
Experience
•Warranty
issues
•Returns
•Performance
issues
•Operational
feedback

36.
Agenda
Olivier Allard Ing.
MAYA HTT
Industrie 4.0
• La donnée
• La technologie
• La gestion de projet
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Données
design
Données
ingénierie
Données
production
Données
performance
Donnée
vente
• Le cycle
d’innovation

38.
Un allier complet
Expertise intégration multi-formats et multi-sources
Expertise simulation multi-physiques
Expertise analyse de données
Expertise organisationelle
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mayahtt.com