The document discusses how various manufacturing companies are using AWS to drive digital transformation across their value chains. It provides examples of Airbus using AWS to build a global data platform to gather aircraft performance data and SKF using AWS IoT services to connect smart products with smart factories. It also shares how Georgia-Pacific is establishing an "enterprise data lake" on AWS to power advanced analytics across its manufacturing operations.

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AWS for Manufacturing
Digital Transformation throughout the Value Chain
M F G 2 0 2
Host: Michel Putnik
AWS Head WW Business Development - Manufacturing

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AWS in manufacturing
Common use cases
• Smart factory
• Smart products and services
• Smart supply chains
• Digital twin
• ML based forecasting, planning, and QC
• Predictive maintenance
• Self organizing production
• Disaster recovery
• Knowledge transfer
Autonomous
robots
Simulation & HPC
Industrial Internet
of Things
Cybersecurity
Cloud
Additive
manufacturing
Augmented
reality
Big Data
ML, analytics
Vertical & horizontal
integration

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AWS in manufacturing

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Customer Speakers

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Journey to the cloud
Chris Taylor
VP, Digital Accelerator

6. 6
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7. Airbus
A commercial aircraft manufacturer with the two Divisions
Defence and Space and Helicopters.
We make it fly.
October 2018
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8. 8
A single global enterprise platform for all aviation
data sources
Supply Chain
People
& user experience
Information Systems
Support
Engineering
Manufacturing
In-service fleet
Network
Context & Environment
Operations
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9. PLATFORM
BENEFITS
Step change in speed to
market
Consistent performance at
global scale
Self service at scale
Enable decentralised
development
Incremental use cases at
lower cost
Platform approach critical
PLATFORM
CHALLENGES
Many new skills required
and mindset
Technically challenging
Balance of business
feature and technical
debt
Data governance
Security combined with
scale and agility
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10. Collecting data is key for us
Improve
Airbus Aircraft
Enhance
In-Service Support
• Shorten the lead-time from
techrequest to root cause
identification and fix
• Monitor the efficiency of new
system (new Modifications and
Service Bulletins)
• Identify and fixing spurious
faults
Monitoring fix efficiency
• Monitor aircraft performance
Improve
Reliability & Safety
• Manage safety enhancements
• Better Monitor and analyse the
worldwide fleet reliability
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11. Connect
And enables extensive benefits and opportunities
Watch
Collaborate
Optimize
Digitalize
Predict
Performance
Planning
ReliabilityInventory
Operations
Fleet Management
Decide Actionable Items
Zero AOG
Material Forecast
Processes
Operations
Risk & Asset
Management
Data sharing
Benchmarking
Aircraft
Passengers
Pilots
Crews
Aircraft environment
Flight & Fleet
Data Sources
Operations
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12. Overall Architecture – platforms of platforms
12
Data Platform
Airbus
Public Cloud
Airbus API
Platform
AirbusE2E
MonitoringPlatformandSecurity
SelfService
Analysis
SelfService
Dashboard
Apps Apps
Application
SkywiseAuthenticationand
AuthorizationServices
…Application…MicroService
…Sandbox
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13. 2017-2018 signed contracts
Skywise Predictive Maintenance
Skywise Core
2000 A/C
5000 A/C
10000 A/C
+12 undisclosed
+ 2 undisclosed
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SKF IoT – Smart Factory and Smart Product
Jens Greiner
Global Manager IoT Development

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SKF – A truly global company
Established 1907
Sales 2017 SEK 77,938 million
Employees 45,678
Manufacturing units 103
SKF presence 130 countries
Distributors/dealers 17,000 locations
Global certificates ISO 9001 – Quality
ISO 14001 – Environment
ISO 50001 – Energy
OHSAS 18001 – Health and safety

16. Rotating equipment
performance
Product Focus
Two value propositions

17. Rotating equipment
performance
Product Focus
Voice of the customer
“We need to increase uptime – continuous
breakdowns are costing a fortune.”
“We need solutions that helps us to manage
our total cost of ownership.”
“I want your products and my assets to reach
technical end of life with trouble-free
operation.”
“We need products that meet our
requirements, at the right price.”
“We need a fast and reliable development
partner for our new product generation.”
“I want on-time delivery, quality and field
performance, flawless launches of new
products, technology and price.”

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Digital Transformation throughout the Value Chain
User Value
• Smart Services
• Analytical insights
Service Layer
• Scalable, Reliable
• Secure
• Cost Efficient
Data Layer
• Smart Products
• Manufacturing
Powered by
Digital Platform

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Smart manufacturing

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Smart manufacturing for SKF
5G
Skills development
Digital twin
Mobility
Robotics
Optimized flow (AGVs)
Shop floor to top floor
connectivity
Industrial internet
Data security
Supply chain 4.0
Traceability
Predictive maintenance
Continuous
improvement
Real time action taking
AGV = AutomatedGuided Vehicle

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Traceability & Mobility
Machine 1
Machine 2
*Pilot Phase
Factory
Machine 3
AWS Cloud
Data Lake
Consumption Layer
Smart Gateway
FANUC 31i CNC
Sinumerik
Siemens S7 PLC
MQTT
Digital Platform

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High Level Architecture
AWS Cloud
Client
Dashboard
Realtime alert
Data Lake & Analytics
Raw data
Email, SMS
FANUC 31i CNC
Sinumerik
Siemens S7 PLC
On-Premise
Lambda
function
MQTT
protocol
Node-RED
Smart Gateway
Factory
Trained ML Model
Rule
ML Inference
Digital Platform

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Connected System 24

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Connected System 24 - Smart Lubricator*
Customer challenge:
• Lubrication is critical for bearing life
• Bearings distributed in a wide area >10 km
• Human interaction is rare
Customer value:
• Active information about grease level
• Active information about alarms
• Remote adjustment of lubrication
• Dashboard with additional insights

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Connected System 24 Dashboard
*Pilot Phase

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High Level Architecture
LoRa
Gateway
AWS Cloud
Customer
Notification service
Business Logic
Data Lake & Analytics
Raw data
Alarm
Lubricator
Manufacturing
Gateway
Manufacturing
Analytics
Digital Platform

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Advanced Analytics Journey
Enabled by an “Enterprise
Data Lake”
Steven M. Bakalar
IT VP – Digital Transformation, Georgia-Pacific

28. Consumer
products
Building
products
Packaging
and cellulose

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Manufacturing facilities

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Current landscape

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Transformation backdrop: Collab. and Support Center
Opportunity to transform from site specific subject matter expertise,
point solutions, and disparate systems to accelerated value capture
through greater leverage of expertise and scalable problem solving
Collaboration
and Support
Center
Andritz
E80 Black
&
Veatch
Rockwell
GP
SME’s
Asset health
Vibration, thermal, lube, leak detection
Exception driven—streaming diagnostic + SME’s
Objective—> 90 day lead time to repair
Process safety
Second Level of monitor and diagnostic
Vendor/SME support
Objective—100% within control limits
Process optimization
Cluster common unit operations (paper, power, drying)
Predictive performance—“digital twin”
Objective—availability, yield, throughput
Valmet
SKF
KCF
Molex
“Pulp mill”
“Vibration”
“Paper”
“Automation”
“Power/
water
treatment”
“Drives/
controls”

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Tissue manufacturing process
Parent Roll Tissue
Converting
Final
Product
• Delicate nature of tissue paper leads to breaks during converting
• Lack of insight around rolls likely to break causes fear and loss of productivity
• Complex manufacturing process pulling on five V’s of Big Data:
• Velocity, Volume, Value, Variety, Veracity

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Value of cloud based analytics solution
Paper Machine
Converting
Quality,
storage,
etc.
Live data
feed
Live
predictions
Human Machine
Interface (HMI)
Feedback

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HMIs – Realtime Recommendations
Paper Machine Operators
Key Drivers % contributor Current Value Suggested Range
a 63% 12.45 14.6
b 8% 82 78
c 6% 0.65 0.59
. . . .
. . . .
Converting Operations
Running Score Staged Score
Roll # 1 5 Roll # 5 5
Roll # 2 5 Roll # 6 3
Roll # 3 3 Roll # 7 5
Roll # 4 2 Roll # 8 4
• Thousands of data points
processed every minute to
identify key drivers
• Drivers are ranked to help
identify “if you could only do
3 things”
• Specific recommended set
points identified based on
best run settings
• Combination of data from
Paper Machine, Quality
(camera data), etc. analyzed to
suggest “convertibility” of the
parent rolls
• Currently working to identify
best run speeds. Once this is
done, we can automate the
process (via APCs)

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• Realtime insights has helped reduce variability and increase output
Outcome
*results shown
are not actual.

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Representative use case results
• Asset reliability: $,$$$,$$$
• Lube systems/vibration analyses/prognostics for rotating equipment
• Remote monitoring and diagnosis…comparing like equipment across facilities and processes
• Process Optimization: $$,$$$,$$$
• Utilities and power
• Pulping and bleaching
• Paper machine sheet break
• Parent roll converting
• Material consumption and waste elimination
• Lumber pricing

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“Operations data lake” architecture

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Learnings
• What we did right:
• Established a dedicated core capability of “data citizens”
• “use case” over “capability” approach for setting priorities and deploying resources
• Technology “augmentation” over a full “automation” approach
• Quickly leveraged data that was “good enough” but “not perfect”
• Loaded granular, non-processed operations related data
• Quickly moved to a “data lake-based hub and spoke” from a traditional “point-to-point” architecture
• Challenges on the horizon:
• Data governance, quality, context and enrichment
• Advanced analytics platform
• Edge computing

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Interested in more?
Visit these sessions for a deeper dive
MFG303: Enable Your Smart Factory w the AWS Industrial IoT Reference Soln
MFG315: Airbus Goes Serverless with AWS to Improve Fleet Operations
MFG316: SKF Connects Smart Products with Smart Factories
IOT214: Machine Learning at the IoT Edge
IOT303: Industrial IoT: Connecting Existing Machines to Tomorrow's Internet of Things
IOT342: Industrial IoT Applications: Making the Connection and Extracting Value
IOT402: Serverless Industry 4.0 & AI: Drive Business Insights from Machine Data

40. Thank you!
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