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Patrick Poiret – Solution Engineer

Oracle Adaptive Intelligent Apps for Manufacturing

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Agenda

2

1. Définitions

2. AI for Manufacturing

3. Démonstration

4. Conclusion

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Data Science, Artificial Intelligence, Machine Learning, Deep Learning…

Data Science: Un domaine interdisciplinaire qui combine statistiques, apprentissage automatique et l'intelligence artificielle pour extraire des informations à partir de données Big Data.

Machine Learning: Une branche de l'intelligence artificielle axée sur la création de systèmes qui «apprennent» - ou améliorent les performances - à partir de données qu'ils consomment.

Deep Learning: Une technique de ML basée sur la structure d'un système nerveux. Les couches interconnectées effectuent des opérations avant de transmettre le résultat à la suivante.

Artificial Intelligence: Systèmes ou machines imitant l'intelligence humaine, en effectuant des tâches complexes qui nécessitaient auparavant une intervention humaine.

https://www.datascience.com/what-is-data-science

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GAFA

6

• Google, Linked In, Twitter• Problème: comment exploiter les données • Capacités de calcul,$$..• Recherches

People you may know

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Adaptive Intelligent Apps for Manufacturing

Business User

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Every Manufacturer has the same problem“Data Rich…Information Poor”

“The inability for manufacturers to get actionable insights from data across Information Technology (IT) and Operational Technology (OT) systems”

Source: Processengineering.co.uk

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Besoin d’une meilleure approcheLes systèmes de Business Intelligence classiques ne traitent pas ces sujets.

• Y a t il des modèles identifiables dans les données relatives aux pertesrendement ou aux défauts ?

• Y a t il une corrélation entre les retours de produits par les clients et les process industriels employés?

• Quels sont les facteurs-clés influençant la qualité, le rendement et le temps de cycle ?

• Peut on prédire les déviations et défauts produits durant la fabrication afinde minimizer les rebuts et les reprises ?

• Peut on tracer les données MO, machine, process, matiére des produitsdéfectueux et identifier les cas clients similaires (smart recall) ?

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The Root Cause could be a combination of 5M factors

Man MachineMethod

MaterialsManagement

CAUSE

“Given the sheer number and complexity of production activities that influence yield & quality, manufacturers need a more granular approach to diagnosing and correcting process flaws.”

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La même analogie s’applique au manufacturingTrouver les facteurs influençant la qualité des produits, l’efficience de la fabrication

Key Performance Indicators

Production Yield

ProductQuality

Cycle Time

Scrap

Manufacturing Cost

Rework

Operator Efficiency

Returns

Manpower

Machine

Management

Method

Material

• Operator• Operator Skills / Certifications• Clock In/Out Time

• Machine Name• Machine Age, Service History, Usage• Sensor Data (Temp, Pressure etc.)

• Work Order Delay• Operation Delay• Work Order Hold

• Quantity Used• Serial and Lot• Supplier

• Operation Sequence• Exceptions• Standard Operating Procedures

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Operational Report/Standard BI vs. Actionable IntelligenceAn Example…

Operational/Standard BI Report

‘What’ Analysis

68% of Batches of Chocolate Cakes made between Jan 1 and Jan 28 had Excessive Cracks after Baking Operation.

What everyone has today

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Operational Report/Standard BI vs. Actionable IntelligenceAn Example…

Operational/Standard BI Report

‘What’ Analysis

Deep Insights

‘Why’ Analysis

68% of Batches of Chocolate Cakes made between Jan 1 and Jan 28 had Excessive Cracks after Baking Operation.

What everyone has today What everyone wants

68% of Batches of Chocolate Cakes made between Jan 1 and Jan 28 had Excessive Cracks after Baking Operation and:

• Eggs were added after sugar @ Mixing Step• Batch was made in the afternoons• Sugar Quantity usage between .5 and .7 lbs• Sugar Lot Number = LT-PG-0001• Blender Max. Speed = 590 RPM @ Mixing Step• Oven Average Temperature > 350 F @ Baking Step

IT Data(ERP, MFG, SCM)

OT Data(machine/sensor)

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Pourquoi c’est compliqué?On doit découvrir des corrélations potentielles dans de gros volumes de données

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Machine Learning vs. Traditional ProgrammingThe Difference

Source: Talend.com Blog: What everyone should know about Machine Learning

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Types of Artificial IntelligenceAI

Source: https://becominghuman.ai/preparing-for-artificial-intelligence-d0b644087537

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Algorithms – Learning methods (II)ML

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DémonstrationAdaptive Intelligent Apps for Manufacturing

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The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

MESQuality

InventorySensor / IoT Procurement / Supplier

HCM

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

Lot LT-PG-0001 (Pinion Gear)

Work Orders where Lot LT-PG-0001 (Pinion

Gear) was used

Material Supplier(for Pinion Gear)

Finished Products that contain Lot LT-PG-0001 (Pinion Gear)

Customers and Warehouses that have Products that contain

Lot LT-PG-0001 (Pinion Gear)

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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Safe Harbor Statement

The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

Review all critical events that happened during the manufacturing process for a

given work order

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Etudes de cas d’application de techniques de ML aux données du Manufacturing

Retours d’experiences ML/AI dans le secteur manufacturier

Components that were assembled by tightening the bolts 12 times had high rate of failure in the field compared to the ones that were tightened 13 times during production.

- Missile Systems Manufacturer

There were 200+ parameters that could influence the vaccine yield. Applying data mining techniques narrowed the list to 9 parameters that mattered the most.

- BioPharma Manufacturer

Every Chip undergoes 19000 tests. Starting at the wafer level, analysis of data from the manufacturing process helped cut down test time and focus on specific tests. The result was a savings of $3 million in manufacturing costs.– Semiconductor Chip Manufacturer

A unique combination of fan speed, temp, and humidity during painting operation were the most common factors in vehicles that had paint crack problems reported by customers.

- Automobile Manufacturer

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Industrialisation

Business & Domain

expertise

Applied AI

DonnéesData

Science

Le dévelopement et le déploiement des modèles doit être organisé. Il y a unecollaboration necessaire entre différentsprofils.

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Oracle Adaptive Intelligent Apps for Manufacturing

Data Lake

Structured Data

Unstructured Data

Semi-StructuredData

PredictionsPatterns & Correlations Genealogy & Trace

ERP

CRM

HCM

SCM

MES

QualityLIMS

Data Preparation & Contextualization

T&A

Machine Learning Model Management

Manpower Machine ManagementMaterial Method

Insight Models Predictive Models Feature Significance Models

Model Deployment

Sensor Time Series (SAX Features)Pre-Seeded & Custom Attributes

Enterprise ApplicationsModel Definition

Shopfloor Devices

PLC

SCADA

Gateway

Data Historian

Environment Data

Model Training Model Performance Evaluation

Root Cause Analysis Impact AnalysisProactive Management

Audio Video Log Files Notes Image

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Conclusion