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Sergey Frolov, SAP
April 26, 2018
Digital twin of industrial equipment – contexts and use cases
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What is a Digital Twin?
A Digital TwinIs a digital representation of a physical
object
The implementation of a digital twin is an
encapsulated software object that mirrors a
unique physical object's characteristics
The minimum elements of a digital twin include
the model of the physical object, data from
the object, a unique one-to-one
correspondence to the object and the ability
to monitor the object.”
Gartner: Innovation Insight for Digital Twins — Driving Better IoT-Fueled Decisions
Business Process
Perspective
Statistics and Big Data
Perspective
Law of Physics
Perspective
Integration
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Untrusted & disparate Asset information
Limited analytical capabilities
Reactive Maintenance
Disconnected Systems and Lifecycle
Standalone and Isolated Assets
Traditional OPEX-based planning
Paper-based work instructions
Collaborative Single source of truth
Real Time Analytics with Simulation
Prescriptive maintenance
Closed loop Product and Asset Lifecycle
Connected and Smart Digital Twins
Asset criticality based maintenance strategy
Smart work instructions with 3D visualizations
NowYesterday
Global Asset Management Trends in Oil & GasTransformation to Smart Digital Connected Assets
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DECOMISSION
AS MAINTAINED
?
Ideas
Geometries
Stress simulations
Cost
Production data
Quality fingerprint
Ramp Up
. . .
Output
Performance
. . .
Issues
Cost
ProfitabilityClosed loop engineering
Designs
. . .
. . .
. . .
AS DELIVEREDAS BUILT
The physical world
PRODUCTION INSTALLATION
AS DESIGNED
ENGINEERING
The digital world
Industrial equipment – modern lifecycle
OPERATION
KPIs
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Digital twins building blocks which enable new business models
Physical manifestation,
hierarchies, and business
context
API-based digital access to
live configuration, state,
condition, and control
Enriched with physical,
statistical, or machine learning
insights
Process support to create,
access, and update digital twin
types and instances
Integrated data model from
design, engineering,
production, maintenance to
service
Cloud-based collaboration across
different business network partners
Secure data access, sharing, and
governance on global scale
Composition and integration of
digital components into digital
systems
Live enrichment during the product
or asset lifecycle
4. Digital twin systems
3. Business network
collaboration
1. Live digital
representation of
physical objects
2. End-to-end business
process support
Enrich and integrate
with business context
Compose and integrate
digital twins
Operate on digital twins and
enable new business models
Collaborate across the
business network
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1. Live digital representation of physical objects
1 Structural Load Insights 2 Fatigue Insights 3 Vibration Insights
• Leverage a sensor-based Finite element
method to monitor the dynamic behavior of
structures in real-time to improve operations
of industrial assets like pumping system
• Monitor structural fatigue and determine the
remaining lifetime to improve safety and
localize structural damage by performing
digital inspections
• Observe equipment's exposed to vibrations
to enable early detection of faults with
improved (automated) diagnostic capabilities
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2. End-to-end business process support
As designed As built As maintained
Design and engineer Build and install
Engineering BOM
Material master
Drawings and Specifications
Spare Parts
Maintenance strategy
Firmware Update
Manufacturing BOM
Configuration
Serialization
Maintain and service
As operated
Operate
Monitoring
Optimization
Equipment as a Service
Digital twin instanceDigital twin type
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3. Business network collaboration between manufacturers of
products and operators of assets
Design
Produce
DeliverService
Retire
Plan
Acquire
DeployOperate
Dispose
Manufacturer Operator
Product Asset
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4. Digital twin systems
Airplanes
In-house maint.
& repair
3rd party
maintenance
After-sales
service
Service
provider
Internal External
No (serialized)
product
Consumer
products
Industrial
goods
Complex assets
/ systems
Co
mp
os
itio
n
Trains
Turbines
Pumps
Mining
trucks
Vehicles
PC
Construction
machines
Paper machine
Injection
molding
machines
Heating systems
Medical
Devices
Consumables
White
Goods
Smart Meters
Service business model
Asset Hierarchy
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▪ One single source of truth for the asset/equipment
▪ Information about the utilization of the asset
▪ Accessible through APIs, based on a service-oriented
architecture
▪ Information about different application aspects
(installation, business partners, condition monitoring,
tickets, spare parts, …)
▪ Support for unique identifiers, types, and instances
▪ Manufacturer- and operator-specific attributes
▪ SAP Asset Intelligence Network supports the RAMI 4.0
administration shell
Technically synchronizing network of digital twins is a part of Industry 4.0
Source: http://www.plattform-i40.de/I40/Redaktion/EN/Downloads/Publikation/structure-of-the-administration-
shell.pdf?__blob=publicationFile&v=5
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How to start?Enabling Digital Twins with SAP Predictive Engineering Insights
hindsight insight foresight
Simulation of different options
Hindsight
▪ record of structural loading (accumulated fatigue
damage)
▪ overview and counting of extreme events and
fatigue (used/remaining lifetime)
▪ verification of design methods
Insights (What is happening now)
▪ degradation of performance due to structural
failure
▪ Insights: Stresses, strains, forces (axial, shear),
bending moments, torque, oscillations, vibrations,
modal analysis (Fourier-transformations)
Foresight (Simulation & Prediction)
▪ simulation of proposed extended operation
scenarios: Side steps, extended testing,
▪ evaluation of structural loading as a
consequence of a control decision
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The first steps of Digital Twin journey in Oil & GasSAP Predictive Engineering Insights
Discover Use
Cases
1 DAY
Design Thinking
Session
▪ Understand the impact on the
business
▪ Identify areas for innovation
Viable engagement
▪ Assess feasibility and
viability with business
stakeholders
▪ Identify key value drivers
Step 1: Design Thinking Workshop
Step 2: Rapid Prototype
Prototype
Rapid Prototyping
(Learn fast)
▪ Narrow down on one core
value driver
▪ Redesign business
process/ model on real
data
▪ Rapid prototype (cloud
solution) to demonstrate
vision
6-8 Weeks
Step 3: Implementation & Roll-out
Roll-out
Implementation (Scale and
Improve Yield)
▪ Realize former prototype as live
implementation by onboarding
further assets or devices
▪ Provide software as a service
▪ Testing and launch support
▪ Define roadmap
Decision Gate Decision Gate
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Customer Examples
Manufacturing Aerospace Heavy Machinery Oil & Gas Marine
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.
The Network of Digital TwinsDigital, physical, conditional, commercial
Product intelligenceAsset Management, predictive
maintenance and service
Asset intelligenceCollaborative R&D, engineering and
manufacturing
Manufacturer Operator
Business
Transactions
Intelligent
Insights
Contact information:
Sergey Frolov
Head of SAP Center of Excellence in Oil and Gas Industry,
SAP CIS LLC
Email: [email protected]
Thank you!
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