AWS를 활용한 Digital Manufacturing 실현 방법 및 사례 소개 - Douglas Bellin, 월드와이드 제조 솔루션 담당 디렉터, AWS / 장대기 대리, GS Caltex :: AWS Summit Seoul 2019

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Digital Manufacturing and
Relevant Use Cases
Douglas Bellin, AWS
장대기, GS칼텍스

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REVENUE GROWTH
OPERATIONAL OVERHEAD
Empowered
Sales Teams
Increased
Efficiency
Intelligent Decision
Making
Products that Get
Better with Time
Better Relationship
with Customers
Data Driven
Discipline

Business Architecture – Manufacturing Operations
PLC/
CONTROLLER
IMAGE
MES
QUALITY
MANAGEMENT
SYSTEM
HMI
SCADA
GW
(OPC SRV) HISTORIAN
ASSET
MANAGEMENT
SYSTEM
Plant 1
Plant 2
Plant 3
Ingestion
Batch Process Machine Learning
Rule Engine
Optimization
Real Time Process Global
Monitoring
Dashboard
Predictive
Maintenance
Quality Control
Maintenance
Management/
Operators
Modeling
Applications

AWS Manufacturing Reference Architecture
Greengrass
Edge/GW
S3
Data Lake
Kinesis
MES
Factory Machines
ML
Inference
IoT Core
Sage Maker
ML
QuickSight
Business
Intelligence
Athena
Historian
Storage Gateway
EMR
EBS EC2 Batch AppStreamEBS EC2
E&D Workloads
(PLM/HPC/CAE)
Enterprise Workloads
(SAP ERP/CRM)DMS RDS
Local Servers
RedShift
Data Warehouse
DataIngestion
API
SiteWise
Snowball Edge
Smart Products
DynamoDB Lambda
IoT Core
Amazon Forecast
Plant Maint. Planning
Business Functions
Greengrass
Connectors
IoT Analytics
Timestream
Outpost
IoT Events
EC2
Lambda
Business Logic

Use Cases for Industrial Data Platform
Visibility Production Quality
Access Critical plant information
at any time
Automate data capture Determine root cause
Measure and improve OEE Drive continuous process
improvement
Reduce scrap and defects
Compare KPIs and metrics across
the Enterprise
Predict out of control processes
and failures
Increase yield
Benchmark machines, lines and
plants
Monitor regulatory info – track
and trace, genealogy
Control processes with stricter
tolerances
Measure and trend quality
performance
Increase capacity utilization Setup alerts to maintain process
control
Improve worker safety Reduce maintenance costs Reduce rework
New services opportunities Increase in NPI time Smarter products

Topics
• Product Shape/Size/Color Recognition
• Production Line Utilization (OEE)
• Predictive Maintenance/Quality & Alarming

Use-Case: Object Shape/Size/Color Detection
Goal: Detect product shape/size/color, joints (weld/seam) problems as
the product moves down a conveyor and/or (vehicle) assembly line, paint
shop, etc. Industrial
Camera
NOTE: Pattern/Shape recognition is Machine Learning in it’s simplest form in seeing we have a defect but not really
predicting why it occurred.

Use-Case: Slow Machine Cycle Time Prediction
Goal: Monitor a packaging machine for slow post cycle times, determine
root causes/contributing factors (e.g., outgoing conveyor belt issues, etc.)
then predict future slowdowns.
PLC
Packaging
Machine
PLC

Paper & Building Product Manufacturing
• Georgia Pacific is one of the world's largest manufacturers of tissue, paper, packaging, building products, and related chemicals.
• The company employs over 35,000 people in more than 150 locations.
• Products include brand names like Angel Soft, Quilted Northern, Brawny, Dixie, Mardi Gras, PerfecTouch, Sparkle, Blue Ribbon, Clutter
Cutter, DensArmor Plus, DensDeck, DensGlass, DensShield, DryPly, FireGuard, GP Lam, Nautilus, Ply-Bead, Plytanium, Southern Gold,
Sta-Strait, Thermostat, ToughRock, Wood I Beam, and XJ 85
Client Profile
• Multiple facilities with different manufacturing
equipment
• Improving plant productivity & quality
• A retiring workforce with valuable site-specific
knowledge gained over years of experience
• Downtime costs of $250k/day, and a need to
maximize machine uptime
• Amazon Kinesis to stream data from complex
machines
• Data Lake with Amazon Simple Storage Service
(Amazon S3) to efficiently ingest and analyze
structured and unstructured data at scale.
• Amazon Elastic MapReduce (Amazon EMR) to
transform data before delivering it in a structured
fashion to data analysts through Amazon Redshift.
• Amazon SageMaker, an AWS machine-learning
solution to build, train, and deploy ML models at
scale
• 30 percent paper-product tear reduction.
• Increased profits by millions of dollars for one
production line, with hundreds of other
production ines to benefit
• Less dependence on disparate factory experts to
keep lines running efficiently.
• Predict equipment failure 60–90 days in advance,
and reduce unplanned downtime.
• Ensure the highest-quality product running at the
fastest-possible rate
Challenges Our Joint Solution Results
“Overall, using AWS, we can ensure the highest-quality product running at the fastest-possible
rate, so we can best serve our customers.” Steve Bakalar, VP of IT/digital transformation

Bearing and Smart Product Manufacturing
• Founded in Sweden in 1907
• The world's largest bearing manufacturer (they also manufacture seals, lubrication and smart lubrication systems, maintenance products,
mechatronics products, power transmission products, and condition monitoring systems.
• SKF employs 44,000 people in 108 manufacturing units.
• Large industrial distributor network, with 17,000 distributor locations encompassing 130 countries.
Client Profile
• Move beyond selling only products, to a
“Rotating Equipment Performance” service
model
• Ensure bearings are lubricated correctly and
automatically to maximize performance and life
• Gather data from customers all over the world to
improve product design and performance
• Add new replacement part revenue streams
• Connected System 24 single point lubricator
feeding a Data Lake with Amazon Simple Storage
Service (Amazon S3) to efficiently ingest and
analyze structured and unstructured data at scale.
• AWS machine learning to help analyze products in
the field
• AWS Database services to manage large amounts
of complex vibration and equipment condition
data
• AWS IoT services, and Lambda to speed time to
market and lower costs on new product designs
• Expand their revenue beyond a ship-and-forget
model, to a services enhanced revenue stream
• Grow sales, even if their raw product shipment
numbers do not increase.
• Innovate faster, with lower costs.
• Concentrate on the value they can provide for
their customers, instead of setting up the
hardware and compute resources required to
make it happen
Challenges Our Joint Solution Results
“I see a lot of speed of innovation coming from AWS, and we are confident that this is the
platform we are going forward with.” Johan Tommervik, CIO

Problem
Wärtsilä needed to accurately predict, when the
marine engines they manufactured needed to
get serviced. Understanding and predicting the
service schedule is vital for Wärtsilä to increase
their service and parts revenue.
Solution
Accenture worked with AWS account SAs, AoD
SAs, and Salesforce SAs to architect an IoT
solution using Salesforce and AWS IoT Core to
collect data and build predictive models. The
solution developed is scalable and extensible
beyond just this use case, as Wärtsilä has 14,000
ships with 35,000 engines installed. There are
great possibilities for sensor driven IoT use
cases.
Impact
The entire solution should result in an increase
in parts/service sales for Wärtsilä and higher
customer retention.

Problem
As Konecranes specializes in the
manufacturing and service of cranes
globally, they discovered that when they
needed to make updates to their
machinery it meant downtime and local
presence onsite.
Solution
Using Greengrass has enabled them to
deploy updates using cloud models that
continually get smarter over time as they
sync with the local environments.
Impact
This allows them to simplify their current
crane architecture and make it possible
to update calculations to the cranes in a
secure way even after the installation
has taken place.

Problem
Stanley Black and Decker finds it unsustainable to ingest,
transmit, store, query and analyze all data generated at
the edge and more specifically on construction sites or
rural areas with constrained network resources.
Solution
AWS Greengrass enables Stanley Black and Decker to
monitor and filter data at the edge of the network
enabling applications to send asset health and predict
any mechanical failures before they occur. Edge-based
applications built on Greengrass will help detect and
compare vibrations emitted by high value tools to
historical signatures that indicate everything from
normal operations to imminent failure.
Impact
Instead of trying to use all the data Stanley Black and
Decker will utilize Greengrass to focus on the right data.
Applications include remote troubleshooting of hydraulic
assets by technicians, maintenance interval tracking, fuel
savings, and alerts.

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
장대기, 경영정보화팀

Oil & Gas Industry,
Refinery에 대한 이미지?

정유업에서의 Data Analytics
Energy
Health care
Logistics
Automotive
Consumer Packed
goods
Telecom, Insurance,
Banking
Retail
Media
Extremely limited Use of digital,
primarily in internal operation
수 십 년간의 사업 운영을 통해 축적된
(Big) Data
오랜 시간 검증된 분석 솔루션
이미 Data Analytics를 적용
추가 개선이 가능한지에 대한 의문

Data Analytics 적용의 시작 그리고 Cloud
[생산 영역] 정형화된 Data 풍부
[생산 외 영역]
Planning
Scheduling
Optimization
Operation
Reliability/SHE
Sales/Marketing
Logistics/Procurement
Finance/Corporate Strategy
시범 과제
[알고리즘 활용]
Anomaly Detection
Cloud 도입의 필요성
• Pilot이었기 때문에 거대한
리소스를 필요 시에만 사용
• 공장 시스템에 영향을
주지 않아야 하는 제약
AWS Cloud 도입
• 다양한 가격 정책
• Cloud 대부분의 서비스가
Korea Region에서 운영
• 다양한 Data Analytics
Tool 보유
• 높은 보안 수준 수 천 개의 제어 장치와 수 만개의
유관 장치 테스트 시, 머신러닝을 위한
Computing 자원 필요
Optimization
4.52
4.53
4.53
4.54
4.54
4.55
현재 값 목표 값
Anomaly Point

Data Analytics 적용 영역의 확대 – 생산 영역
과제 발굴 영역
공정 수율/성상 예측
공정 운영 최적화
Trouble 원인 분석
이상 감지
우선 수행
과제 도출
업무 포털을 통한 예측 결과 조회
- 배치 분석 수행 및 결과 저장(DB)
업무포털
Cloud에 접속 후 분석 환경을 직접 활용
Use Case 예시 3
Use Case 예시 1
End-Point를 이용한 실시간 결과 조회
Use Case 예시 2
Inference
(Response)
Input Data
(Request)
1) 분석 환경 구축
2) 모델 개발
“정형화된 분석”
“계획적 반복 분석”
“On-Demand 분석”
“다수 Point에서 활용”
“신규 모델 개발”
“데이터 탐색 및 시각화”

Data Analytics 적용 영역의 확대 – 고객 영역 ①
주유소 CCTV 영상을 통한 차량 인식 및 분석 예시
주유소 영상
차량 인식
모델
통계 Data 추출 및 분석
(방문 차량 모델 비율 등)
Data Point 추출
(차종, 연식만)
Privacy Data 원천 제외
고객 마케팅/주유소
운영전략

주유 /세차 차량 인식 영상

주유소 인근 교통량 인식 영상

Data Analytics 적용 영역의 확대 – 고객 영역 ②
경쟁 주유소 파악 (예시)
GS 칼텍스 주유소
주유소 Coverage (예시)

Data Analytics 적용 영역의 확대 – Risk Management 영역
100
110
120
130
140
150
160
170
180
p10
p50
p90
Actual
Amazon Forecast
<결과 예시>
RM의 고민
• 급변하는 시장 상황
(변수의 증가)
• 담당자 별 숙련도에
따른 예측 정확도 차이
 다양한 변수를
고려한 방향성 예측
Logic이 필요
활용범위 확산검토
• 데이터만 잘 준비되면
코딩 없이 분석 가능
• Case 별 분석을 통해
영향력 있는 변수
산출 가능
 비전문가도 활용 가능
 확산 검토 중

Data Analytics를 위한 AWS 활용
Industrial equipment
Factory Data
Interface
Server
S3
AURORA
DB
EBSEC2
Existing Analytics Tools (SAS)
sas
SageMaker EC2
기타 Data
CRM
CRM
CRM

Digitalization에 대응한 진화의 시작

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AWS를 활용한 Digital Manufacturing 실현 방법 및 사례 소개 - Douglas Bellin, 월드와이드 제조 솔루션 담당 디렉터, AWS / 장대기 대리, GS Caltex :: AWS Summit Seoul 2019

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AWS를 활용한 Digital Manufacturing 실현 방법 및 사례 소개 - Douglas Bellin, 월드와이드 제조 솔루션 담당 디렉터, AWS / 장대기 대리, GS Caltex :: AWS Summit Seoul 2019