디지털기술을활용한제조산업의 Digital …2019/11/21 · 정광식 Cloud Solution Industry Architect Oracle Digital Prime Tech. November 21st, 2019 디지털기술을활용한제조산업의

정광식

Cloud Solution Industry Architect

Oracle Digital Prime Tech.

November 21st, 2019

디지털기술을활용한제조산업의Digital Transformation 동향Digital Manufacturing for Industry 4.0

1 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted

The following 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, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.

Statements in this presentation relating to Oracle’s future plans, expectations, beliefs, intentions and prospects are “forward-looking statements” and are subject to material risks and uncertainties. A detailed discussion of these factors and other risks that affect our business is contained in Oracle’s Securities and Exchange Commission (SEC) filings, including our most recent reports on Form 10-K and Form 10-Q under the heading “Risk Factors.” These filings are available on the SEC’s website or on Oracle’s website at http://www.oracle.com/investor. All information in this presentation is current as of September 2019 and Oracle undertakes no duty to update any statement in light of new information or future events.

Safe Harbor

Copyright © 2019 Oracle and/or its affiliates.2

http://www.oracle.com/investor

제조 산업 TREND


제조업의변화와도전과제들

4

▪ Inability to react faster to events increasing direct and in-direct costs

▪ IT-OT separation with no data transparency

▪ Slower or wrong decision making withincreasing system complexity

▪ Inability to plan production schedules keeping uncertainties in view

▪ Lack of experienced, skilled laborrequired to run and maintain next-gen manufacturing

전통적인 ‘자동화 ‘ 만으로는 부족

비즈니스모델의변화 주요이슈

제조업에서의혁신의가속화

왜혁신인가?Industry 4.0 technologies such as IOT, Big Data, Machine Learning, etc. are related to Brain Labor, including a comprehensive linkage of collaboration, collective intelligence and value chain. The intelligent process by industry 4.0 will solve the current issue, problem of complexity, as repetitive process by PLC solved the problem of scale and adaptive process by ERP solved the problem of inflexibility.

Repetitive Process

Ad-hoc Process

Adaptive Process

Scale issue

Inflexibility issue

Complexity issue

Intelligent Process

PLC/SCADA

ERP/SCM

Digital Twin(CPS)

산업內주요변화

67%of industrial manufacturers have

an ongoing Smart Factory initiative

>90% of new supply chain execution application

spend will be cloud-basedapplications by 2020

20.4 Billionconnected products will exist

by 2020.

38%increase in manufacturers adoption of

machine learning and analytics to improve predictive

maintenance is predicted over the next five years.

63% of manufacturers believe that the

Internet of Things (IoT) will

increase profitability over the next five years and are set to invest

$267B in IOT by 2020.

85% of spare parts suppliers will

incorporate 3D printing into

their business within 5 years.

Industry 1.0에서 4.0까지

(Connected humans, parts, machines,

partners)

1 42 3

End18th century

Early70’

TodayEarly20th century

Mechanical

Electrical

Electronics & IT

Cyber Systems “ 다음 세대의 운영 기술은Digital Native”

• 3D printers, Cobots, connected tooling, AGVs… must be connected.

• Artificial Intelligence will run complex operations.

• Innovations will come from data and digital improvement.

• Digital OT must be secured and enterprise grade by design.

디지털혁신기술

Data Science

Machine Learning Artificial Intelligence

Big Data Analysis In-Memory Computing

Smart Devices

Internet of Things

Augmented RealityChatbots Blockchain

디지털화 / Industry 4.0

Digitalization

IoT

IT/OT Convergence

SocialIncreasing Social based

collaboration at the enterprise level

CloudNew Service delivery

model through Cloud platform

AnalyticsExpansion of application

and scope of Big Data analytics

New Machine

Drone, 3D printer with IT technology

MobilityIncreasing Mobility

Solution to the industry domain

Technical perspective Business perspective

※ Source: Industry 4.0, Smart Manufacturing for the future, Germany Trade & Invest

Operational efficiency

UnconventionalGrowth

Typical Digital transition ofmanufacturer

Digitalization is a foundation of industry 4.0. To realize vision of industry 4.0, most enterprise processes must

become more digitalized. From a technical perspective, digitalization means a combination of various new

technologies that can be newly applied. And from a business perspective, it means evolving customer

experience, business process and business itself.

Industry 4.0 –디지털엔터프라이즈D

igital Thre

adDig

ital

Th

read

RemoteDiagnostics

Predictive Models

Real TimeAnalytics

Track And Detect

Digital FieldService

Production Monitoring

Fleet Monitoring

AssetMonitoring

Product Usage Data

Connected digital service

Digital Logistics

Connected smart factory

Digital supply chain

Lead to cash for the

digital age

Connected digital

innovation

Voice of the Factory Voice of the Product Voice of the CustomerVoice of the Digital Twin

Industry 4.0 - 주요테마

Data, Connectivity & Computational Power e.g. Cloud, IoT, Big Data

Analytics & Intelligence e.g. AI, Knowledge, etc.

Conversion to Physical World e.g. 3D Printing, Robotics

Human-Machine Interfaces e.g. Virtual & Augmented Reality

Industry 4.0 –주요비즈니스가치

Labor

Time-to-Market

Inventories

Quality

Resource / Process

Supply / Demand Design and Engineering Costs

Costs for Qualityreduced by 10-20%

Forecasting accuracy increased by 85%

10-30% reduction in design and engineering costs

Productivity increase by 3-5% Costs for inventory holding decreased by 20-50%

20-50% reduction in time-to-market

45-55% increase of productivity through

automation of knowledge work

Connectivity의증가

IoT

Smart Manufacturing

Facility Management

Field Asset Management

Preventive Maintenance

API driven Digital Manufacturing

Automated ( 자동화)Smart Factory

Autonomous ( 자율화)Smart Factory

From To

Data Explosion

실행가능한데이터분석필요

Standard Operational Report

➔ ‘What’ Analysis

Actionable Insights

➔ ‘Why’ Analysis

21% of Work Orders of Strawberry Jam made between Jan 1 and May 31 had Consistency Above Upper Limit at Operation 50.

What everyone has today What everyone wants

21% of Work Orders of Strawberry Jam made between Jan 1 and May 31 had Consistency Above Upper Limit at Operation 50 when:

• Operator was David Cooper @ Op 10• Batch was made during 2nd Shift• Sugar Quantity usage between 10.2 and 10.7 lbs.• Sugar Lot Supplier was “White Crystals Inc.”• Blender Max. Speed was 590 RPM @ Op 20• Oven Average Temperature was above 350 F @ Op 30

IT Data

OT Data

Things Needed for Data To Actionable Insights

실행가능한데이터분석체계Paradigms are Required to Change

Traditional Empirical Approach

What data are available?

Data

What information can we learn from

these data?

Information

What insights can we generate

with this information?

Insights

How do we use these insights

to make decisions?

Decisions

Advanced Empirical Approach

What information do we need to

get these insights?

Information

What insights do we need to

make these decisions?

Insights

What are the critical decisions?

What do we need to solve & improve?

Decisions

What data must we collect to

get information we want?

Data

왜 이러한 분석이 어려운가?

Copyright © 2019 Oracle and/or its affiliates.

Data

Analysis

Work Instruction

s Non-Conformanc

es

Genealogy

Parts & Assembly

ProductQuality

Serials and Lots

Bill of Materials

Routing

Operators

Equipment

Need to contextualize data from ERP and shopfloor devices together

인공지능, 머신러닝, 딥러닝 ???

인간의 뉴런과 비슷한 인공 신경망 방식으로 학습(ML 기법 중 하나)

딥 러닝(Deep Learning)

컴퓨터가 스스로 학습하여 인공지능의 성능을 향상시키는 방법(AI의 한 분야)

머신 러닝(Machine Learning)

사고나 학습 등 인간의 지능을 컴퓨터를 통해서시뮬레이션하여 구현

인공 지능(Artificial Intelligence)

머신러닝

Customer Months

Cell Phone Churners vs. Loyal Customers

Pattern #1: IF CUST_MO > 14 AND INCOME < $90K, THEN Prediction = Cell Phone Churner, Confidence = 100% (8/8), Support = 21% 8/39

Pattern #2: IF CUST_MO > 7 AND INCOME > $175K, THEN Prediction = Cell Phone Churner, Confidence = 83% (5/6), Support = 13% (5/39)

데이터로 부터 알고리즘 찾기

머신러닝 적용 Use Cases

Product Requirements/Specifications Mgmt

OpportunityStatus

DeliveryOrder

6

LogisticsMgmnt

Product Quality,Customer Claims &Warranty Mgmt

Planning

Product LifecycleMgmt

Marketing &Sales Mgmt

Pre-Sales and Sales Activities

Supply Chain &Production Planning

Production Mgmnt& Manufacturing

Transportation Plan and Execution

Delivery

After-sales Services

Finance & Accounting

Human Resources

Management

Yield Rate Improvement

Early detection ofProduct Defects

Product Usage Monitoring & Advices

Procurement Cost Reduction

VoC Analysis

Marketing & SalesActivities Optimization

Work-Style Reform

Demand/Sales Forecast

Quality Mgmt & Assurance

Maintenance Optimization &Equipment Failure Prevention

Product Failure Predicts withProactive Service Actions

Productivity Improvement

Employee SatisfactionImprovement

Global Cost Mgmt

Best offer analysis

Social ListeningDetailed Mgmt

Simulation

Decision Making Support

머신러닝 적용 Use Cases

Product Requirements/Specifications Mgmt

OpportunityStatus

DeliveryOrder

6

LogisticsMgmnt

Product Quality,Customer Claims &Warranty Mgmt

Planning

Product LifecycleMgmt

Marketing &Sales Mgmt

Pre-Sales and Sales Activities

Supply Chain &Production Planning

Production Mgmnt& Manufacturing

Transportation Plan and Execution

Delivery

After-sales Services

Finance & Accounting

Human Resources

Management

Yield Rate Improvement

Early detection ofProduct Defects

Product Usage Monitoring & Advices

Procurement Cost Reduction

VoC Analysis

Marketing & SalesActivities Optimization

Work-Style Reform

Demand/Sales Forecast

Quality Mgmt & Assurance

Maintenance Optimization &Equipment Failure Prevention

Product Failure Predicts withProactive Service Actions

Productivity Improvement

Employee SatisfactionImprovement

Global Cost Mgmt

Best offer analysis

Social ListeningDetailed Mgmt

Simulation

Decision Making Support

Voice of Customer Voice of Product

Voice of Factory

EnterpriseManage-

ment

AI/ML 산업내적용 Cases

Intelligence

연결 & 분석을통한 Predictive Insights 확보

Time-to-Value

Connected Assets• Remote monitoring• Business validation

0-3 Months

Real-Time Decisions

• Proactive decisions• Store into Big Data

3-6 Months 6-9 Months

Enterprise Integration

• IoT blended into SaaS/ On-Prem apps

• Mobile apps and interaction

• Differentiation through customer experience

Fast Adoption, Low Risk, Minimal Investment

9-12 Months

Contextual Predictive Insights

• Improved products

• Increased control end to end

• New usage models and customer services

Bu

sin

ess

Va

lue

디지털기술기반제조혁신

Edge DevicesCreating new data sources: individual, product, machine, facility and environment

Edge-to-CloudConnectivity

Creating connectivity for 2-way transfer of data and instruction

CloudStore and Analyse data in Cloud and integrate with source of truths

Horizontal and Vertical Integration

Integrating data and applications across the extended value chain

Connected and Integrated

Real-Time Insight Analyzing real-time data to generate a deeper understanding of assets

VirtualizationSimulation

Simulating the impact of assets and networks to support decision making and prediction

Big Data Analytics

Applying machine learning to more, bigger datasets to generate new insight and algorithms

Automation

Applying algorithms to predict outcomes and adjust parameters in real-time

Intelligent and Optimized

Leveraging new technologies to provide value

오라클 Strategy & Solution for Industry 4.0


오라클 엔터프라이즈 클라우드 전략

Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted27

엔터프라이즈를 위한 클라우드 기술 인프라의 혁신

✓ Gen2 Architecture Innovation

✓ Core-to-Edge Comprehensive Security

안정성 기반 위에 Autonomous를 통한 편의성 제공✓ 안정성 + 자율 운영(Self-Driving/Securing/Repairing)

✓ MAA(Maximum Availability Architecture): - RAC + ADG 기반의 99.995% 가용성 제공

Digital Transformation을 미래 기반 IT 기술의 활용

✓ Cloud Native AppDev

✓ AI/ML 기반 Emerging Technology Adoption

✓ Autonomous, Data Science, Blockchain, Chat-Bot, Digital Assistant …

AutonomousDatabase

Predictable, Best

Price/Performance

Digital Innovation

AutonomousLinux

오라클 Intelligent Supply Chain Cloud


Analytics &Deep Learning

DecideDetect

Real-Time Monitoring & Predictions

Global Visibility

Unified Data Lake

Supply ChainPlanning

Supply Chain & ManufacturingDigitalSignals

BusinessEcosystem

Execute

Real-Time Monitoring & Predictions[IOT Apps]

Trends & Anomaly Detection

Asset, Fleet, ProductionMonitoring

Digital Twins

Equipment Diagnostics &

Recommendations

Industrial Gateways

SCADA,OPC-UA

MQTT,HTTP, etc.

Historians

Digital Signals

Causal Analysis

Analytics & Deep Learning[BI & AI Apps ]

Supply Chain Analytics

Insights & Predictions

External Systems

Business Networks

Data Pools

Market Intelligence

Business Ecosystem

Global Visibility

Dashboards Mobile AppsAugmented

RealityDigital

AssistantsSocial Collaboration Blockchain

Unified Data Lake

Context ContextUnstructured Data

Social, Audio, Video, Image FilesStructured/Relational Data

Business IT Systems

Semi-Structured/Time Series Data

Machine / Sensor Data

Supply Chain Planning[Planning Apps]

Supply Planning

Integrated Business Planning

Demand Management

`

Supply Chain & Manufacturing[Business Apps]

Product Innovation

Sourcing & Procurement

Manufacturing & Costing

Maintenance& Service

Order Management Inventory & WMS

Quality Management

Transportation& Global Trade

Supply Chain Collaboration

오라클 Industry 4.0


The vision for tomorrow delivered today

오라클 Industry 4.0


Digitally connected enterprise

Dig

ital

Th

read

Master DataManagement

Enterprise Performance Management

Finance andAccounting

Human Capital Management

MobilityAdaptiveIntelligence (AI / ML)

IoTMonitoring

CybersecurityBlockchain

Lead to Cash for the Digital Age

Digital Logistics

Connected Digital Innovation

DigitalService

Smart Manufacturing

DigitalSupply Chain

Voice of the Digital Twin Voice of the Factory Voice of the Product Voice of the Customer

Digital Th

read

오라클 Smart Manufacturing


Enabling Factories of the future

Make intelligent judgements

DecideExecute proactive and predictive actions

ExecuteGlean unknown insights

DiscoverConnect, Collect, and Communicate

Integrate

Workorders

Work Instructions

Automated Production IoT Monitoring

Enterprise/SCM

Deep Learning

Genealogy & Trace

Manufacturing Actions

Business Actions

Analysis/Research

Closed Loop Feedback

Closed Loop Execution

IT/OTConvergence

BusinessData

Operational Data

Execution/MES Customer Service

Copyright © 2019, Oracle and/or its affiliates. All rights reserved. 32

Smart Manufacturing: Integrate

Integrate assets with business processes to gain real-time visibility and transparency

Decide the next best action based on deeper insights from AI

Create feedback loops and resolve issues faster

Discover unknown information from collected data

Connect, Collect, and Communicate

Make intelligent judgements Execute proactive and predictive actions

Glean unknown insights

Decide ExecuteDiscoverIntegrate

A path to getting actionable results faster

33Copyright © 2019, Oracle and/or its affiliates. All rights reserved.

Reduce Operational Risks with Digitized and Intelligent Assets

• Monitor sensor data and asset health in real-time

• Get notifications and predictive insights on asset failures

• Implement quickly with real business value

• Seeded KPI’s and integrations

• Configurable and extendable

Reactive maintenanceSilos of informationDisconnected ops

Proactive maintenanceGlobal visibilityDrive biz outcomes

Before After


Improved Performance with Automated Work Order and Quality Check Transactions

• Connect production automation with enterprise software systems

• Transact work order progression and quality exceptions directly in SCM Cloud

• Real-time data collection for quicker analysis and remediation

• Integrated visibility into operations

Manual operationsLoss of dataDisconnected silos

Fully automatedReal-Time dataEnd-to-end visibility

Before After

오라클 IoT 클라우드서비스Faster adoption, lower risk, less investment, better outcomes

Internet of Things Cloud Enterprise (Platform)

Enhance worker safety through monitoring of workers and environment

Monitor shipments, fleet vehicles, driver behavior and costs

Manufacturing equipment and production line monitoring and prognostics

Monitor assets, their health, utilization and availability

Automate asset monitoring and Customer Service to enhance customer experience

Connected WorkerProduction MonitoringAsset Monitoring Service Monitoring for Connected Assets

Fleet Monitoring

Connect Analyze Integrate Learn


Mitsubishi Electric Is Triggering the Industry 4.0

Automationsolutions for manufacturer requirements to boost productivity

Mitsubishiis connected to oracle IOT to run the e-factory solution

Mitsubishi Electricoperate 237 factories and laboratories worldwide over 121 countries

CUSTOMER PERSPECTIVE

Mitsubishi Electric’s solutions for Edge Computing and support for Cloud Computing help businesses who are looking to reap the benefits of the Internet of Things (IoT). The Edge Computing solutions are built on local control platforms to provide filtering andpre-processing of production data from intelligent devices.


Smart Manufacturing: DiscoverPath to getting actionable results faster










Efficient Production Management with a Connected Factory

• Provides a hierarchical global and local transparency of your factories KPIs.

• One 360°view of yield / efficiency, and equipment behaviors.

• Consolidate planning and cost management.

• Leverage IOT, predictive AIand supply chain cloud. Manual reporting

Silos of informationStatic Analytics

Realtime data collectionCentralized dataData driven analytics

Before After


Agile Production Planning, Monitoring, and Execution

• Predictive alerts and next best action based on production line OEE.

• Anticipate potential yield drop.

• Optimize your maintenance scheduling, downtime costs and spare parts procurement.

• Bigdata root cause analysis for maintenance optimization. Unplanned downtime

Uncontrollable costsPoor quality

Less interruptionsLower costsBetter quality

Before After

오라클데이터분석클라우드서비스DataViz Analytics for Root Cause Analysis Across Maintenance, Suppliers, Machine


Precision Group Enhances Operational Efficiencies on the Factory Floor


By having this system right now, we were able to achieve increased efficiency and end-to-end visibility into the supply chain. We have real time visibility of our orders and thereby the entire cycles of order-to-cash and procure-to-pay have been completely streamlined. We have complete visibility of our business with advanced analytics in simple dashboards, enabling better collaboration and faster decision-making.

Real-time visibilityinto supply chain and inventory organization

Oracle Cloud applications were

fully integrated with manufacturing devices

Improvement in transaction posting,

resulting in higher accuracy in inventory control and management


Smart Manufacturing: Decide









Path to getting actionable results faster


Operational Excellence withAI Driven Insights

• Get deeper root-cause analysis for quality and yield issues in minutes.

• Address all root-causes to fix issues.

• Avoid scrap, rework, and recalls.

• Use Genealogy and Trace to find affected customer orders and provide proactive service.

What happenedShallow insightsPoor quality, yieldTakes weeks &months

Why it happenedDeeper insightsHigher yield, qualityWithin minutes

Before After

Copyright © 2019 Oracle and/or its affiliates.

Oracle Adaptive Intelligent for Supply Chain & 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 ImageUnstructured data ingestion future roadmap

오라클 Machine Learning 클라우드서비스Advanced Prognostics Architecture for High Value Assets In Manufacturing Industry


Noble Plastics Is Creating a Light-out Factory


What we've done is we've built a connection between our robots and the cloud that allows us to monitor those and any of the data that's coming off, and then we can also send information back to the robot and it can take an action based on those decisions.

Improvemanufacturing process and enhance product quality

Highly automated manufacturing facility with lights out third shift

Noble Plasticshas achieved a higher transparency through asset monitoring


Smart Manufacturing: Execute









Path to getting actionable results faster


Process Deviation Monitoring and Operator Training

• Corrective action automation based on root cause triggers identified by AI.

• Adjust machine parameters to meet the quality and performance standards.

• Implement and automate best practices based on past response history.

• On-the-fly operator training based on VR/AR technologies.

Lost opportunitySlow training processPoor quality

Immediate detectionQuicker re-trainingImproved quality

Before After


Empower Field Technicians for Remote Asset Diagnosis and Maintenance• Create maintenance orders based on root

cause triggers identified by AI.

• IoT and open-standards adoption enable technicians to troubleshoot and resolve issues remotely.

• Cloud-based and mobile technologies to empower technician on the go.

• Enable field technicians with best practices and service knowledge. Slower response

Local best practiceDisgruntled customers

Rapid actionsSmart best practicesHigher CX

Before After

오라클 Mobile 클라우드서비스Create Field Service Actions Based on Insights for Faster Response

!


World-Wide Leading Manufacturer of Valves, Automation Components for High-Tech Process Industry


IoT provides the opportunity to add value to customers and users. GEMÜ is already service oriented, but IoT is the road to increase and enhance this with new service delivery mechanisms. Device driven performance and service data are means to increase uptime, reduce maintenance cycles, reduce risk –improve product safety.

Real-timefiltering and processing of events from equipment deployed worldwide

Increaselifetime and reliability of components used in safety and health-critical processes

Monitoring400,000 product versions

제조산업에서의 오라클 Cloud 고객사


Oracle Cloud

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Smart Manufacturing을 통한 경쟁 우위 달성


Improve Customer Satisfaction

ReduceCosts

Maximize Productivity and Efficiency

Reduce Time-to-Value

Accelerated Decision Making

Q & A


Thank you

정광식 ([email protected])

Cloud Solution Industry ArchitectOracle Digital Prime Tech.

55 © 2019 Oracle Internal/Restricted/Highly Restricted

Documents

디지털기술을활용한제조산업의 Digital …2019/11/21 · 정광식 Cloud Solution Industry Architect Oracle Digital Prime Tech. November 21st, 2019 디지털기술을활용한제조산업의