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La gestion de projet dans l'industrie 4.0

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La gestion de projet dans l’Industrie 4.0

La conférence portera sur la gestion de projet dans l’aire technologique de l’industrie 4.0. La révolution de la collecte de données, de l’analyse de ces données et partage de ces données apporte de nouveaux défis pour les gestionnaires de projets. Que ce soit avant, pendant ou après le projet : les innovations technologiques sont une considération importante pour la livraison des projets du futur.

Published in: Data & Analytics
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La gestion de projet dans l'industrie 4.0

  1. 1. 1 Olivier Allard Ing. Directeur Simulation – MAYA HTT La gestion de projet dans l’aire Industrie 4.0
  2. 2. Agenda Olivier Allard Ing. MAYA HTT Industrie 4.0 • La donnée • La technologie • La gestion de projet 2
  3. 3. Olivier Allard Ing. L’innovation technologique est une passion 3
  4. 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 4
  5. 5. mayahtt.com
  6. 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.
  7. 7. 7 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. 8. Numérisation Ça change TOUT!!!
  9. 9. Industrie 4.0 L’ère de changement technologique … 9
  10. 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. 11. Digital Product Digital Production Digital Performance Holistic Digital Twin
  12. 12. La donnée L’or de d’aujourd’hui … 12
  13. 13. 13 La valeur des données
  14. 14. 14 Les risques des données
  15. 15. Le parcours de la donnée La collecte et visualisation L’interprétation et intégration Apprentissage et prédiction Optimisation et automatisation 15
  16. 16. Valorisation de la donnée Wisdom Knowledge Information Data Insight (why). What is best. Meaning (how) Context (who, what, when, where Raw Data Collection
  17. 17. La collecte Différentes sources Différents formats Différents besoins « Garbage in, Garbage out » 17
  18. 18. La visualisation 0D, 1D, 2D, 3D, 4D … 5D Rapport vs Dashboard Publique vs. privée Page Web, App, email, … 18
  19. 19. L’intégration Relation des données Le danger du « timestamp » Snap shot vs. Historique Les unités de mesure 19
  20. 20. L’interprétation Expertise du domaine Expertise « science de la donnée » Snap shot vs. Historique Le Danger des moyennes 20
  21. 21. La technologie L’internet des objets (IOT), Info-Nuagique (Cloud) , Intelligence Artificielle (AI) … 21
  22. 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. 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. 24. IA vs Humain : Compréhension Langue Naturelle (NLP) 100+ langues… Vraiment?
  25. 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
  26. 26. 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. 27. Retour sur l’investissement pour 4.0
  28. 28. Maturité de votre évolution 4.0
  29. 29. 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. 30. La gestion de projet 4.0 Seul on va vite, Ensemble on va LOIN … 30
  31. 31. Les 3 “i” de votre succès en évolution 4.0
  32. 32. Les 6 dimensions à considérer
  33. 33. 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. 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. 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. 36. Agenda Olivier Allard Ing. MAYA HTT Industrie 4.0 • La donnée • La technologie • La gestion de projet 36
  37. 37. 37 Données design Données ingénierie Données production Données performance Donnée vente • Le cycle d’innovation
  38. 38. Un allier complet Expertise intégration multi-formats et multi-sources Expertise simulation multi-physiques Expertise analyse de données Expertise organisationelle 38
  39. 39. mayahtt.com

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