AWS for Manufacturing Monterrey

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AWS in Manufacturing
Douglas Bellin, WW BD Lead Manufacturing
bellin@amazon.com

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Improving
manufacturing operations
is everything!

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Manufacturing
Industry Drivers
Emerging Markets
Complex, Dynamic Value Chains
Security, Cyber & Physical
Truth in Data
Demanding Customers
Converging Technologies
Ubiquitous Connectivity
Traceability & Transparency,
Brand & Reputation
Workforce
Shortage of expertise, Knowledge
Transfer (Attrition), Tech Savvy

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Product-as-a-
Service
Digitally
“Executed”
Manufacturing
Data is the more
then the
New Oil
Connected
Products
Manufacturing Industry Trends
Sustainability

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Responding to
Business Demands
24 × 7 × 365
Operations
Asset
Lifecycle
Enabling the
Workforce
Protecting and
Security IP
Unleashing Data and
Bringing Insights
Global & Regional
Collaboration
Cost
Reduction
Manufacturing Industry Challenges

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If you knew the state of every thing in the world,
and could reason on top of the data:
What problems would you solve?

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DIGITAL MANUFACTURING IS A IMPORTANT
ELEMENTOF THE DIGITAL SUPPLYCHAIN
A connected supply chain generates demand signals that are used to manage the supply (manufacturing process) creating a tightly-knit
‘sense and respond’ supply chain
SUPPLIER MANUFACTURING DISTRIBUTION
The signal is used to manage
downstream activities.
In the factory, this is used to plan the
production run.
Customer and consumer
provides demand signal
Supplier
Network
Raw
Materials
Inventory
Production Phases Shipped Out Finished
Good Stocks
Transit
Stocks
Last Mile
Fulfilment
SALES

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Supply Chain Trends
• Smaller Brands With
Digital Channels Are
Carving Out Their
Share
• M&A for CP
companies are
increasing at a faster
past than other
industries
• Personalization in
marketing, with
brands, online
experiences for their
users, delivering
dynamic content and
more.
• New smaller brands
moving faster
• Develop a
relationship with the
consumer
• Selling direct to the
consumer
• Block Chain
5. Market
Share
• Keep the consumer
engaged
• Product
Transparency
• Changing buying
habits
• Influence of Social &
Mobile
• Experimentation
with direct to
consumer
• Trust / Security
4. Cost
Removal
• Digitization across
the value chain
• Managed Services
• Automation and
Robotics
• Core Modernization
• Cloud
Transformation
• Speed of
Change
• Providing alternate
ways to use /
receive product
• Interactive
product
• Consumer
Personalization
• Low costs of mass
production &
flexibility to
personalize products
3. Product
Innovation
2. Knowing the
Consumer
1. Protect the
Brand
• Growth in mass
customization
• Supply Chain

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Source - Deloitte’s 2018 Global Chief Procurement Officers survey
Many companies have no visibility beyond tier 1
65% have limited
to no visibility
beyond tier 1
Supplier 11%
55%
1%
6%
27%
Virtually no visbility below
tier 1
Limited visibility below tier
1
NA
Full Transparency
Good Transparency below
tier 1

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What if You Could Enhance the Entire Supply Chain?
Enable adaptable, innovative, and scalable supply chain
services that optimize business outcomes
Foundational Advanced Capabilities
Quality
Excellence
Delivery
Excellence
Cost
Excellence
new
New Product
Introduction
Risk
Management Segmentation
Acquisition
integration Security
Supplier
Collaboration
Workforce of
the Future
Sales & Ops
Planning
Sustainability
Factory of
the Future
Connected
Supply Chain
Development

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The Value of a Digitized Supply Chain
Increased
Visibility
Proactive Issue
Resolution
Enhanced Customer
Experience
Data anytime.
Anywhere. Any
device.
End-to-end
Collaboration

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Connect the clouds on a platform…
…create a community (shippers, suppliers, and logistics providers)…
…and the “Game” changes.
Financial Services
In-market
Packaging
$
Import/Export
Fulfillment Service
Logistics Demand
Transportation Providers and Brokers
Packaging
Buyer
Planning
DC
Retail
Wholesale
Planning
Production
API
production
Third party
suppliers
Re-packers
Seller

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Key factors for
visibility technology
Implementation
 Connectivity with (future) partners
 Alignment of processes, people, IT
 Cooperation of partners
Functionality
 Understanding of functionality by
all supply chain partners
 Integration in current way of working
Vision
 Determination of scope in line
with business objectives
 Organizational capabilities
 Translation of vision
Cost and service
 Quantification of (indirect) benefits
 Cost and benefit sharing
 Selection of services offered
Technical architecture
 Integration in current architecture
 Flexible and cost-effective setup
Many Consumer oriented companies have visibility
technology in place

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Supply chain network
A S I N G L E N E T W O R K P L AT F O R M T O C O N N E C T, C O L L A B O R AT E & E X E C U T E I N T E L L I G E N T LY
NETWORK INTELLIGENCE | CONTROL CENTER
Customers Distributors Manufacturers Transportation
Providers
3PLs Banks Ocean
Carriers
Suppliers
END-TO-END VISIBILITY & CONTROL
NETWORK PLATFORM
Workflow Automation | Single InformationData Model | Data Quality | Multi-Party Integration | Security | Extensibility | Mobility
Single Connection | Global Onboarding and Support
Plan Source Make Move Deliver Pay

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What is preventing the industry from moving ahead?
AI/ML & BIG
DATA
expertise is
rare
Building and
scaling AI/ML &
BIG DATA
technology is hard
Deploying and operating
models/solutions in
production is time-
consuming and
expensive
A lack of cost-effective,
easy-to-use, and
scalable AI/ML & Big
Data services

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…realizing value from Big Data is challenging
What’s
holding
you back
from
using Big
Data?1:
Unable to link data
together
96%
of Manufacturers
state data is not used
Data collected too
infrequently
Data difficult
to access
39%
of Manufacturers do not
regularly collect data
66%
of Manufacturers find
data is difficult to access
Without the right platform, data insights remain elusive

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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

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AWS in Manufacturing
© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

19. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
About Georgia-Pacific
Georgia-Pacific, owned by Koch
Industries, is an American wood
products, pulp, and paper company
based in Atlanta, Georgia. The
organization is one of the world’s
largest manufacturers and distributors
of pulp, towel and tissue paper and
dispensers, packaging, and wood and
gypsum building products.
Industry: Manufacturing
Headquarters: Atlanta GA
Employees: 35K
Website: www.gp.com
We are using AWS data analysis technologies to predict ... precisely
how fast converting lines should run to avoid tearing. By reducing
paper tears, we have increased profits by millions of dollars for one
production line.
Steve Bakalar, VP of IT & Digital Transformation
“
”
Challenges Solution Benefits
Georgia-Pacific wanted to gain new
insights from manufacturing data
collected at paper production plants,
but it relied on disparate sources to
analyze data on material quality,
moisture, temperature, and other
features.
Georgia-Pacific uses an AWS
advanced analytics solution,
featuring Amazon Kinesis and
Amazon SageMaker, to
collect and analyze data from
equipment at manufacturing
facilities across North America.
• Boosts profits by millions of dollars
• Predicts equipment failure 60-90 days
in advance
• Runs more production lines in a
predictable manner
• Ensures highest quality products
Case Study: Paper & Building
Product Manufacturing

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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
Employees: 46K
Website: www.skf.com
I see a lot of speed of innovation coming from AWS, and we are
confident that this the platform we are going forward with.
Johan Tommervik, CIO
“
”
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 Data Lake with
Amazon S3 to ingest and analyze
data; AWS ML to analyze products
in the field; AWS Database to
manage large amounts of complex
vibration and equipment data;
AWS IoT and 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 Bearing and
Smart Manufacturing

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New Format
Manufacturing Functional Areas
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Marketing and Sales
Market Analysis
Portfolio Planning
Demand Creation
Selling Products /
Services / Outcomes
Marketing & Sales
Analytics
Brand Management
Product &
Production Design
Refine Plan and Define
Product/Services
Design and Release
Product/Services
Validate Product/Service
Design to Requirements
Prepare and Validate
Production Environment
Prepare and Validate
In-Service Environment
Manufacturing
Operations
Plan and Schedule
Production
Manage Supply and
Inbound Logistics
Make or Assemble
Product
Fulfill Production Order
Supply Chain
Develop Supply Base
Manage Production
Support and Materials
Manage Warehousing
and Distribution
Conduct Supplier
Aftermarket Support
Service Chain
Manage Technical
Maintenance and
Engineering Information
Plan Maintenance
Perform
Maintenance/Repair
Manage Services Supply
Manage Service
Contracts and Warranty
Analyze and Report
Service and Quality Data
Mitigate Risk in SLAs
Business
Operations
Provide Administrative
Services
Manage Business
Provide Human
Resource Support
Provide Financial
Support
Manage Public Affairs
Provide Legal Counsel
Provide Information
Technology and
Communications

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Marketing and
Sales
SMART PRODUCT
SMART FACTORY
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
Amazon Forecast
Amazon Sagemaker
• ML for Demand Forecasting
• ML for Campaign Generation and
Execution
• Opportunity Scoring
• Pricing/Trade-spend modeling
• Suggested Next Steps
Amazon EC2
Amazon EBS
Amazon IAM

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Marketing and
Sales
SMART PRODUCT
SMART FACTORY
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
Amazon Forecast
Amazon Sagemaker
• ML for Demand Forecasting
• ML for Campaign Generation and
Execution
• Opportunity Scoring
• Pricing/Trade-spend modeling
• Suggested Next Steps
Amazon EC2
Amazon EBS
Amazon IAM

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Product &
Production Design
PRODUCT DESIGN
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
Amazon EC2
Amazon EFS
• HPC workloads for EDA, CFD, FEA, Crash
Simulation.
• Simulation model optimization with AIML with
live performance model as baseline
• Comparison of virtual sensor data with
physical sensor data
• Comparison of test performance data with
product performance data from field
• Condition monitoring of physical assets tested
• Failure prediction for physical assets tested
Amazon S3
Amazon Glacier
Amazon EBS
Amazon Appstream

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Manufacturing
Operations
SMART FACTORY
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
• Production/Process optimization
• Preventive / predictive maintenance for machines
• Additive manufacturing with AI / ML support
• Condition monitoring of machine tools augmented by ML
• Worker safety
• Knowledge Transfer
• Digital Twin
• Plant as a service
• Computer vision for Quality
• Early machine tool replacement reduction
• Scrap reduction & quality optimization
• Power optimization
• Fleet management
• Integration with SAP & MRP & MES systems (logistics /
inventory / JIT use cases)
• Streamlining logistics
Amazon Sagemaker
AWS IoT AWS Outposts
AWS SiteWise AWS IoT Analytics
AWS IoT Events AWS Greengrass
Amazon Timestream

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Supply Chain
SUPPLY CHAIN VISIBILITY
SUPPLY CHAIN FORECAST
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
Amazon Forecast
Amazon Sagemaker
• ML for Demand Forecasting
• ML for Supply Side Availability
• Improved Inventory Management
Amazon API Gateway

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Service Chain
SMART PRODUCT
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
Amazon Forecast
Amazon Sagemaker
• Preventive / Predictive maintenance
• Remote diagnostics
• Streamlining Warranty (ML)
• Streamlining Recall
• Streamlining service with intelligent analytics from
historian data
• Streamlining spare parts forecasting / inventory
practice
• Next generation service centers
• Streamlining forecasting and logistics processes
• Autonomous cars
• Over The Air Updates
• Retrofit / Spare Parts revenue opportunities
• Vision / ML inference at the edge
AWS IoT
AWS IoT Events
AWS Greengrass

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Business
Operations
SAP ON AWS
Solution Area
DATA LAKE ON AWS
Key Partners
Use Cases AWS Services
Amazon Forecast
Amazon Sagemaker
• Landscape lift and shift
• AnyDB to HANA
• BW on HANA
• FAST + PoC’s
• S/4 HANA
• BW/4 HANA
• Industry Extensions
• AI / ML
• IoT
• Data / Analytics
• Integrated Apps
• Agile Dev Ops
• Intelligent Managed Services
Amazon API Gateway

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SMART MANUFACTURING
This leads to better efficiencies and quality,
and provides competitive advantage in
manufacturing. Smart Manufacturing solutions integrate different layers on the ISA 95 standard architecture.
8
To achieve the benefits of Digitization in its Manufacturing operations, companies should implement a comprehensive solution, that covers
Level 1-4 of the ISA 95 architecture.
Smart Manufacturing, also referred to as
Industry 4.0 or Digital Factory, capability
includes data generation (via sensors,
actuators), data collection, aggregation,
visualization and analytics.
Factory operations can be improved via the
availability of real time information, that
improves the machine operations and
enables line operators and factory leaders to
make immediate, data driven decisions.

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Problem with current model
Functional Layer Data
ERP 10%
MES 25%
SCADA 35%
PLC 50%
Sensors 100%

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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 GW
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
N:1
SiteWise
Snowball Edge
Smart Products
DynamoDB Lambda
IoT Core
Amazon Forecast
Plant Maint. Planning
Sample Business Functions
Greengrass
Connectors
IoT Analytics
Timestream
Outposts
IoT Events
EC2
Lambda
Business Logic

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Manufacturing
Data
Flywheel
YIELD
QUALIT
Y
UPTIM
E
PRODUCT
Shop
Floor Data
LIFECYCLE
MGMT
IMPROVEMENTS
CUST SAT
LOYALTY
Connected
Product Data
R&D
NPS
CHANGEOVER
LOWER
CHANGEOVER
New Product
Introduction

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• Factory visit / Amazon FC visit
• DI workshops
• Working Backwards workshops
• PR/FAQ
Next Steps

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Thank you.