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如何透過 AWS IoT 服務建構
物聯網應用
T r a c k 4 | S e s s i o n 6
Ed Tsai
Cloud Support Engineer
Amazon Web Services

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thing
problems

3. Smart
product
validation
Smart
factories
Smart
products
Smart field
service
Smart
product
design
IoT data is the new oil
Lubricating every process across the entire product value chain

4. … to solve challenging enterprise-scale problems
Remotely monitor
patient health &
wellness applications
Manage energy resources
more efficiently
Enhance safety in
the home, the office, and
on the factory floor
Transform transportation
with connected and
autonomous vehicles
Track inventory
levels and manage
warehouse operations
Improve the performance
and productivity of
industrial processes
Build smarter products &
a better user experience in
homes, buildings, and cities
Grow healthier crops
with greater efficiencies

5. New services &
business models
Products that get
better with time
Better relationship
with customers
Increased
efficiency
Intelligent
decision-making
Data-driven
discipline
… and enable major business outcomes …
Revenue growth
IoT data drives business growth
Operational efficiency
IoT data decreases operational expenditure (opex)

6. … which drive new industrial market trends
Convergence of business, process, and government
standards, like Industry 4.0
Mass production
↓
Mass customization
Pay upfront
↓
Pay as you go
Manual
↓
Automated
Buy (a product)
↓
Subscribe (to a service)

7. Is there any doubt that …
… IoT data from billions of connected devices
will be the fuel
to transform industries?

8. AWS IoT customers in many sectors are doing this today

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10. Your digital transformation journey
Changing what you
build
• Connected products
• Evolving experiences
• Products that improve over time
• Value-added services
Changing how you
operate
• Operational efficiency dashboards
• Predictive response
• Supply chain enhancements
• Smart factory & manufacturing
Changing your
market economics
• New business models
• Service / lease vs. purchase
• Data-based services

11. Example: Digital/IoT
transformation in agriculture
Changing what you build
• Connected equipment (combines, tractors)
• Field sensors, imaging, drones
Changing your operations
• Granular and consolidated insight into soil
conditions, moisture, nutrients
• Automated operations
Changing your economics
• Predictive harvesting
• Livestock tracking
• More efficient equipment maintenance
• Smarter supply chain

12. Exploring
POCs & pilots Full production
Limited production
Reality: Most organizations struggle

13. What makes this hard?
Instrumenting the physical world takes time—especially for “brownfield”
equipment or legacy devices
Security and privacy concerns
Organizations are not ready to manage data: quantity, quality, and models
Also, it requires a culture change
Returns from insights and efficiency take time
New business models challenge traditional approaches

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15. Evolving customer
experiences
Connected products improve over time
• iDevices: Voice experiences
• iRobot: Room mapping & routing
• Updating with new features
& capabilities
• Reducing costs via diagnostics &
customer support

16. IoT & edge
ML / AIAnalytics

17. AWS IoT Core
Data transport & routing
Amazon SageMaker
Process state
Product quality
Equipment health
Output forecast
Equipment
efficiency
Leak detection
Asset planning
Ops optimization
Tool productivity
AWS IoT SiteWise
AWS IoT Analytics
Data aggregation, enrichment,
cleansing, time-series processing,
model configuration
Model creation, optimization,
retrainingData collection, model inference
AWS IoT
GreengrassFreeRTOS
Connected product  ML/AI cycle

18. Collect AnalyzeGenerate React
AI + IoT lifecycle
Predict
Most customers
are here
Customers want
to be here

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20. IoT data transforms traditional industrial processes
Manufacturing Mining Oil and Gas Agriculture
But most data collected on premises is never analyzed and is thrown away

21. Problem
• Untapped data
• Locked: Historians/SCADA
• Basics first, then AI/ML
AWS solution approach
1. Extract, expose, visualize
2. Build open data stores for
specific business problems
3. AI/ML journey:
Descriptive > preventive >
predictive analytics
Liberating locked
industrial data

22. Data lake on AWS
Machine learning
On-premises
SCADA/historian
Analytics
Real-time IoT data
Our foundational high-level architecture …

23. Customer (on premises)
PLCs
Customer
field assets
Predictive
maintenance
Worker
safety
advantages
Operational
enhancement
Process
optimization
Product
quality
improvement
Product
design
enrichment
Scrap &
leakage
reduction
Raising
purchasing,
supply chain &
logistics
efficiency
SCADA / historian
Customer DC
assets
AWS Cloud
Reporting /
visualization
& monitoring
AWS IoT
Events
AWS IoT
Core
AWS IoT
SiteWise
Protocol
convertor
AWS IoT Greengrass
/ AWS IoT SiteWise
Collector
Industrial gateway
Customer gateway assets
Data lake on AWS
Amazon
QuickSight
Amazon
Athena
Amazon
SageMaker
Analytics, AI/ML
… to build high impact field-to-cloud industrial solutions

24. How it transformed:
Its edge to cloud data journey
Bayer Crop Science
AWS IoT
SiteWise
Collection
gateway
Protocol
conversion
OPC-UA
MQTT
HTTP
Modbus
OPC-UA
server
On-premises
historian
IoT
historian
(edge)
Edge
Extract data
Perform actions @edge
AWS IoT Events
Event
detection
Take
action
Use
predictions
Industrial
data lake
Information models
Enrichment
pipelines
AWS IoT Analytics
& AWS IoT SiteWise
Batch processed
datasets
Machine
learning
integration
Cross-site views,
remote diagnostics
IoT
historian
(cloud)
State management
and analytical actions
Asset
modeler GUI
Cloud
Visualize data
Operational dashboards
Store for analytics & compliance
Self-serve analytics Machine-learned analytics

25. Example: Bayer Crop Science
“One-third of globally produced food is lost or wasted before
people consume it, and of that, 39 percent of the loss occurs
in food manufacturing. This equals a loss of 750 billion US
dollars annually.”
- Bayer
Now Bayer can continuously monitor food processing
efficiency, quality, and resource utilization through
their entire production process
From:
• Fields to
• Receiving to
• Shelling to
• Storage to
• Drying to
• Color sorting to
• Cleaning & sizing to
• Treating to
• Warehousing & shipping
They can now adjust processes and equipment to
reduce losses even as the quality or quantity of input
crop streams change

26. Oil & Gas: Remote drill-site monitoring at North Slope
AWS IoT
Amazon
SageMaker
AWS IoT Greengrass
w/ ML inferenceML model
Video feeds
Video feeds
Inference
output
Standard camera/Smart gateway
AWS IoT Greengrass
w/ ML inference
Video feeds
AWS IoT
Amazon
SageMaker
/ Amazon
SageMaker Neo
ML
model
Inference
output
Smart camera/No gateway

27. Example: Computer vision-
based IoT solutions for
hazardous oil field operations
Problem
• 1,200 oil wells
• Brownfield assets: Analog gauges and valves
• Manual inspection, 2x day, any weather (-62 C)
• Major health, safety, and environment (HSE) issue
Solution
• ML models for value and gauge readings
• C1D2 certified cameras, not the gateways
• Local alerts with verification, cloud storage for
compliance
• Pilot underway on North Slope
• Regulatory certification next
• If successful, major resource utilization and HSE
win

28. Problem
Valmet delivers technology and automation with multiple
dependent processes running in parallel.
Data analytics is needed to optimize Valmet’s customers’
processes.
Solution
Valmet is building a new digital twin capability to allow paper
mill operators to view equipment and process data during
production runs. AWS IoT Analytics is at the core of this
solution, training ML models for paper quality forecasting and
scheduling metrics generation for digital twin view generation.
Impact
AWS IoT Analytics enables Valmet to combine historical models
of equipment performance with live data from current
operations to glean insights that help it to further provide
solutions that enable its customers to produce paper with
lower costs and optimum quality.

29. Oliver Blume
Chairman of the Executive Board of Porsche AG
Member of the Board of Management of Volkswagen Group
“We will continue to strengthen production as
a key competitive factor for the Volkswagen
Group. Our strategic collaboration with AWS
will lay the foundation.”

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31. Moving to as-a-service
business models
Outcome sold as-a-service, not a sale of a
collection of individual assets
Example: Construction
• Volume of earth moved vs. renting equipment
• Connected equipment tracks usage &
efficiency
• Models allow for performance-based pricing
• Requires accurate analytic forecasting models
Example: Tire manufacturing
• Miles of usage; change in ownership paradigm
Example: Jet engine manufacturing
• Miles of uptime between maintenance
Examples in numerous industries

32. About SKF
Founded in 1907, SKF is the
world’s largest bearing
manufacturer. The company also
manufacturers seals, lubrication
and smart lubrication systems,
maintenance products,
mechatronics products, power
transmission products, and
condition monitoring systems.
SKF has a large distributor
network, with 17K distributor
locations spanning 130 countries.
Industry: Manufacturing
Headquarters: Sweden
Challenge Solution Benefits
Move beyond selling only
products to a “rotating
equipment performance”
model
Ensuring automatic
lubrication of bearings to
maximize performance
Gather data from
customers to improve
product design
Add new placement part
revenue
Connected System 24
single point lubricator
feeding a data lake in
Amazon S3 to ingest and
analyze data
Amazon ML to analyze
products in the field; AWS
databases to manage large
amounts of complex
vibration and equipment
data
AWS IoT and AWS Lambda
to speed time-to-market
and lower costs
Revenue expansion
beyond ship-and-forget
to a services enhanced
model
Grow sales even if raw
product shipment
numbers do not
increase
Innovate faster with
lower costs
Focus on value for
customers instead of
managing IT resources
Case study: Smart bearings manufacturing,
product-as-a-service

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34. Achieve business outcomes faster using solutions
built by AWS and our APN Partners
AWS IoT Solutions
Help you quickly solve problems across
common industry use cases
APN Solutions
Accelerate your time-to-value by
leveraging the expertise of APN Partners
and their prebuilt solutions

35. AWS IoT Solutions
Build faster with AWS CloudFormation templates, deployment guides, GitHub repositories,
reference architectures, and more

36. AWS IoT Partner Solutions
Accelerate your time-to-value with prebuilt, end-to-end solutions built by APN Partners

37. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

38. AWS IoT Partner Network
Delivering use case-specific applications and solutions
Solutions
and
outcomes
Connectivity
Edge
aws.amazon.com/iot/partner-solutions/

39. • AWS IoT Analytics
• AWS IoT Device Management
• AWS IoT Foundations
• AWS IoT Greengrass
• AWS IoT Security
Learn ​IoT with ​AWS Training and Certification
Resources created by the experts at AWS to help you build IoT skills
Visit the learning library at https://aws.training
25+ free digital courses cover topics related to IoT, including:
Take the free digital curriculum, Internet of Things Foundation
Series, to build IoT skills and work through common scenarios

40. Thank you!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.