SAP Asset Management
Digital Twin and Strategic Asset
Management
Dean Fitt, Solution Manager, SAP SE
30 April 2018
PUBLIC
SAP’s Vision for Asset Management
Full Digital Representation of Assets along their Lifecycle delivering an embedded,
collaborative and real-time set of Next Generation Processes and Systems
Collaborative
Base for Industry
Extensions
Partner ecosystem
Opportunity
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Electronics
Mechanics
Services
Real Time Insights
Share & Collaborate
natively
Unified data and processes with
PLM, Manufacturing and Service
Software
Scalable
Cloud Platform
Based
Real Time
Connected
Digital Disruption
Optimization via
Prediction
Next gen tech Machine Learning,
Block chain, 3D printing
Adaptable
Mobile First / Fiori UX
2
Global Asset Management Trends
Transformation to Smart Digital Connected Assets
Yesterday
Now
Selling equipment
Pay per use / Equipment as a Service
Untrusted & disparate Asset information
Collaborative Single source of truth
Limited analytical capabilities
Real Time Analytics with Simulation
Reactive Maintenance
Prescriptive maintenance
Disconnected Systems and Lifecycle
Closed loop Product and Asset Lifecycle
Standalone and Isolated Assets
Connected and Smart Digital Twins
Traditional OPEX-based planning
Asset criticality based maintenance strategy
Paper-based work instructions
Smart work instructions with 3D visualizations
Optimized for Physical Structure
Mechatronics/Software in Products/Assets
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Pay per use
Digital Twin
3D visualization
3
The Network of Digital Twins
Digital, physical, conditional, commercial
Manufacturer
Operator
.
Product intelligence
Collaborative R&D, engineering and
manufacturing
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Business
Transactions
Intelligent
Insights
Asset intelligence
Asset Management, predictive
maintenance and service
4
SAP Connected Assets Portfolio
Networked best-of-breed applications providing high end-to-end business value for key stakeholders
An architecture built for next generation
Enterprise Asset Management
Digital Core: System of
Record
Digital Innovation: System of
Intelligence
Digital Intelligence
Asset
Strategy &
Performance
Management
Maintenance
Management
Predictive
Maintenance
& Service
Integration
Asset
Central
Digital Core
Corrective, emergency and preventive maintenance planning &
execution via notification and order processing in an integrated
system
• Asset Central – Provides a re-usable asset registry across IoT
applications for seamless integration and data consistency
Predictive
Engineering
Insights
Asset
Intelligence
Network
S/4HANA
&
ECC
• SAP Asset Intelligence Network
Collaborative asset management bringing key stakeholders
(operator, OEM, service providers, …) together in a digital
ecosystem solving complex execution, predictive and planning
activities with centrally managed asset information
• SAP Predictive Maintenance and Service
Enables enhanced predictive maintenance techniques to
optimize EAM business processes for greater asset availability
and reduced cost
• SAP Asset Strategy and Performance Management
SAP Leonardo IoT
for Asset Management
Define and plan maintenance execution strategies holistically
(insight/foresight; insights from network) for improved
performance
• SAP Predictive Engineering Insights
Model and visualize the physical structure of an asset for realtime calculation of stress and fatigue to drive predictions
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5
SAP Connected Assets – 3 Complementary Digital Twins
SAP Asset Intelligence
Network
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SAP Predictive
Maintenance
SAP Digital Twin for
Structural Dynamics
6
SAP Asset Intelligence Network
Bringing together Business partners
Insurer
Service
Provider
Models /
Equipment
Manufacturer
Operator
Regulator
SAP Asset Intelligence Network will provide a global registry of equipment; built and shared between multiple
business partners and used across the industry by all stakeholders. This will enable new collaborative business
models resulting in true Operational Excellence.
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7
SAP Asset Intelligence Network
Enabling collaborative asset management
Apps
Content
Network
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Apps for collaborative processing e.g. equipment
lookup, announcements, service bulletins,
performance improvement, spare parts
management, obsolescence management
Combined together
to deliver
A cloud portal of standardized content that defines
and documents models and equipment, shared
and stored, for a consistent definition between
business partners.
A secure network to connect multiple business
partners for inter and intra company information
exchange and collaboration.
SAP Asset
Intelligence
Network
8
Apps
SAP Asset Intelligence Network
Collaboration between manufacturers, service provider, and operators
Job Instructions
Announcements
Nameplate info
Maintenance strategy
Spare Parts
Network
Content
Service bulletins
Obsolescence
Management
Performance
Improvement
Spare Parts
Equipment as a
Service
Work
Collaboration
Commissioning &
Handover
Service bulletin receipt
Service bulletin processed
Usage information
Failure modes
Installation information
Recalls
Bills of Materials
Designs and drawings
Failure / incident data
Design improvements
Design recommendations
Sensor definition
Operating instructions
Maint instructions
Safety instructions
Risks and controls
End to end Firmware process
Manufacturer
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Measurement documents
Telemetry
Share Notification/Work Order Info
Operator
9
Foundation for a successful, global, single network of assets
All business partners need to talk a common language
Based on industry standards e.g. ISO14224, ISO15926, ecl@ss, ….
Class
?
Subclass
Manufacturer
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Class
!
Subclass
Operator
Class
Subclass
Service Provider
10
A Native Collaboration platform
within companies and between companies
Operator
Manufacturer
SAP Asset Intelligence Network
E-23-8384
Model / Reference Equipment
Service Provider
Digital Twin/Equipment
P78-324
P78-453
P78-820
P78-912
Physical Equipment / CMMS
P78-324
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P78-453
P78-820
P78-912
11
SAP Asset Intelligence Network
Use Case at BASF Ludwigshafen
200 production plants connected to each
other by over 2,850 km of pipelines &
over 230 km of rail.
The goal is to establish a fully integrated
and centrally managed, single source
asset information repository, ensuring:
•
Data consistency and availability
•
Efficiency of engineering and
maintenance processes throughout
the asset lifecycle.
“A more integrated digital approach with our business partners would allow us to easily access the
latest and current information when and where needed, leading to quicker and better decision
making and in consequence higher asset effectiveness.”
Dr. Andreas Wernsdörfer, Senior Vice President, Technical Site Services Ludwigshafen, BASF.
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Link
12
SAP Asset Intelligence Network
Applications
Admin Apps
Business Partners
Authorizations
Templates
..
Master Data Apps
Models
Equipment
Locations
Spare Parts
Documents
Instructions
Requests
Smart Matcher
…
Process Apps
Performance Improvement
Obsolescence Report
Error Code Lookup
..
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13
SAP Asset Intelligence Network
Equipment: Features
Information
– Highlights
– Attributes
– Model Information
– Installation Information
– Life Cycle Information
Structure and Parts
– Structure
– Spare Parts
Documentation
– Documents
– Instructions
– Announcements
Monitoring
– Measuring Points
– Error Codes
– Improvement Cases
Time Line
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SAP
Asset
Intelligence
Network
SAP Asset
Intelligence
Network
Application
– Obsolescence
Report
Obsolescence
Management
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Equipment / Functional Locations
Work Orders
Announcements
Documents
Hierarchies
Attributes
Header Information
SAP EAM
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Equipment
Object Creation
Model
Link
SAP AIN
SAP EAM Plant Maintenance
Integrated Objects
Equipment only
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Asset Strategy and Performance
Management
Asset Strategy and Performance
Asset management strategy is influenced by different aspects
Overall equipment effectiveness
Maximize asset performance
Return on assets
Unplanned outages
Drive safe operations
Annual OSHA related incidents
Recordable accident frequency rate
Reduce costs
Annual service and maintenance cost
Planned maintenance budget vs. actual cost
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18
Asset Strategies
What are the options?
The prime objective of an asset strategy is to achieve the optimum balance between asset
performance (which could include also safety and environmental integrity), availability and
the cost of maintenance.
Asset strategies:
• Preventive (time or cycle and based on a “useful life”)
• On-Condition
• Predictive
• Failure Finding (risk-based interval)
and if none of the above then defaulting to:
• Run to Failure (“Run to Repair”)
• Modification (which may also mean accepting degraded performance)
The results of your Asset Strategy (whether intentional or not!) will dictate how you care for
your assets and is measured by KPIs for performance (inc. safety and environmental),
availability and cost.
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Selecting the correct strategy
Reliability Methodologies & Technologies
Maintenance Strategy Development
▪
▪
▪
▪
▪
Reliability Centered Maintenance (RCM)
Failure Modes and Effects (Criticality) Analysis (FME(C)A)
Structured Review (PM Review)
Root Cause Analysis (RCA)
…
Reliability Analytics
▪
▪
▪
▪
Weibull Analysis
System RAM Modeling
Growth Analysis
…
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SAP Asset Strategy and Performance Management
Developing a Maintenance Strategy
Manage Asset Information
Manage Locations, Equipment, Groups, Systems, Failure
Modes…
Asset Risk & Criticality Assessment
Rating assets according to criticality for the business
Application of Appropriate Methodology
RCM*
Reliability-Centered Maintenance
Evaluating threats to safety,
operations, and maintenance
FMEA
Failure Modes and Effects Analysis
Analyze component failures and
associated results on operations
PM Review*
Preventive Maintenance Review
Evaluate the current maintenance
plans and their effectiveness
Maintenance Strategy Implementation*
Create/Change/Delete Maintenance Plans, Task Lists, Inspection, Condition Based Maintenance, Predictive Maintenance, Run to Fail
S/4HANA and SAP ERP
PdMS
Maintenance Strategy Execution
Perform Inspections, Condition Monitoring,
Predictive Maintenance, …
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Asset Management Monitoring
Analyze Cost and Performance
*planned
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Equipment List
Showing Risk, Criticality and Recommended Action
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Equipment: Risk and Criticality Information
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Equipment: Review Highlight Cards
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Equipment: Review Risk & Criticality Matrix
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25
Equipment: Review Risk Assessments and Questionnaire
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FMEA Assessment: Review Assessment – Maintenance Strategy
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SAP Asset Predictive Maintenance and
Service
Maintenance Journey & Asset Optimization
Where are maintenance and service heading?
Asset Collaboration across Supply Chain
Reactive
Wait until a
machine fails
and then
undertake
maintenance.
Preventive
Perform
maintenance at
regular intervals,
based on
observations of
abnormalities.
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Conditionbased
Continuously
observe the status
of assets and react
to predefined
conditions and
events.
Predictive
Apply advanced
analytics of operational
and business data to
help determine the
condition of specific
equipment and predict
when to perform
maintenance.
Digital Twin
29
Technology is changing our approach to maintenance
*Use of Maintenance Strategy – Today
Run to Failure
Preventive
On-Condition
Predictive
*Use of Maintenance Strategy – Future
Run to Failure
Preventive
The Internet of Things is leading to
increased use of on-condition and
predictive maintenance strategies
On-Condition
Predictive
Although still relevant, preventive
maintenance typically results in
over-maintaining assets and high
cost
The goal is to
increase our use of
advanced maintenance
strategies and reduce
reactive maintenance
events
*Proportion of maintenance strategies are for illustration purposes only and will vary based on many factors
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SAP Predictive Maintenance and Service
From Sensor to Insight to Outcome
Sensor
Data
Insight
Action
Outcome
Connected assets
IT/OT Convergence
Data analysis
Maintenance activities
Business Value
• Onboarding
• Big Data ingestion
• Root cause analysis
• Customer experience
• Connectivity
• Big Data infrastructure
• Asset health monitoring
• Device management
• Merging sensor data
with business
information
• Machine learning
• Prioritized maintenance
and service activities
• Optimized warranty
and spare parts
management
• Security
• Anomaly detection
• Triggering of corrective
actions
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• Prescriptive
Maintenance
• Quality improvements
• Increased quality
• Lower costs
• Operational efficiency
• R&D effectiveness
• Material procurement
32
SAP Predictive Maintenance and Service
Solution components and value drivers
Actions
Insights
Logistics & Maintenance
Execution Systems
SAP Predictive Maintenance and Service
Explorer
Business User
Domain Expert
Details
Machine Learning Engine
Raw
Data
SAP
Leonardo
IoTFoundation
Foundation
SAP
Leonardo
SAP Leonardo
IoT Edge
SAP Leonardo
for Edge
Computing
Machine Data
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Business Data
 Flexible extension concept for
customers to build industry or
customer specific models and
analytics
Data Scientist
 A scalable Machine Learning
Engine that drives data science
insights into our business
processes
Data Manager
 Flexible visualizations via Analysis
Tools Catalog
Analysis Tools Catalog
Alerts
 Enables a data science driven
approach to condition monitoring
 End-to-end process integration…
Alert, Discover, Remedy
33
SAP Predictive Maintenance and Service
System and component level visualizations
Logistics & Maintenance
Execution Systems
Asset Health Control Center
SAP Predictive Maintenance and Service
Asset Health
Control Center
Asset Health
Fact Sheet
Insight Provider Catalog
Machine Learning Engine
Asset Health Fact Sheet
SAP Leonardo
LeonardoFoundation
Foundation
SAP Leonardo
Leonardo for
SAP
forEdge
EdgeComputing
Computing
Machine Data
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Business Data
34
Optimization of maintenance and service
Bring business context to your operational data
Predictive Maintenance
and Service
Asset health
control center
Create maintenance
or service order
Schedule order
Increase effectiveness with
valuable insights from operational
systems
Business Systems
Fault pattern
recognition
Machine health
prediction
Execute order
on mobile device
Visual support
Order status
IT and OT
connectivity
Increase efficiency with business
processes for maintenance and
service
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Public
35
35
SAP Predictive Maintenance and Service
Customer Example: Train Operator
Company
Process and Technology Innovation
The company owns and operates a fleet of around 2,000
electrotrains, 2,000 locomotives, and 30,000 coaches and wagons.
Situation: Some 40% of maintenance effort is for corrective
maintenance. Maintenance of rolling stock has been identified as
one aspect of the digital transformation.
Solution
•
•
•
•
•
•
•
Custom solution on SAP HANA software
Data fusion between IT and OT data
Multidimensional description of assets
Remote train diagnostics
Engineering rules and predictive models
Indicator-based planning
Dynamic optimization of maintenance schedules
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Benefits
• Higher asset availability, leading to greater
passenger satisfaction
• Less effort for corrective maintenance
36
SAP Predictive Maintenance and Service
Customer Example: Compressor manufacturer
Company
One of the largest providers of compressed air systems and
compressed air consulting services.
Solution
• Compressors equipped with sensors
• SAP Predictive Maintenance and Service
solution with machine health fact sheet
• SAP HANA software
• SAP Customer Relationship Management
(SAP CRM) application:
Service on SAP HANA
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Predictive Maintenance and Service
IT and OT
connectivity
Asset health
control center
Create maintenance
or service order
Schedule order
Fault pattern
recognition
Machine health
prediction
Execute order
on mobile device
Visual support
Order status
Situation: Changed the business model from selling compressors
to selling compressed air
Process and Technology Innovation
Business Systems
Benefits
• IoT as an enabler for the new business model
• Improved availability of compressor stations
• Move from unplanned to planned maintenance
37
SAP Predictive Engineering Insights
Predictive Engineering Insights
Enable digital insights of industrial assets
based on real-time and predictive
engineering analysis
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39
Value and ROI examples
SAP Predictive Engineering Insights
Use Case
Offshore 6MW
Assumed power
production:
26280
MWh/year
(50%idle, 50% at capacity)
Energy price
50€/MWh
Dec ‘17, N2EX indicator
Turn-over
1 314 k€/year
Maintenance
cost
8€/MWh
Life time:
20 years
Assumed Asset
profit margin
15%
Stipulated
subscription fee
10k€/year
Value Proposition*
Lifetime extension
A lifetime extension of 1 year will
cover the lifetime cost for the
monitoring solution and lead to one
additional year of cash-flow.
Efficiency Increase
An efficiency increase of 0.75% will
cover the subscription fee
Maintenance Cost Reduction
A 5% cost reduction due to fewer
and more efficient maintenance
operations will cover the
subscription fee
*The examples above are based on no combination effects. In real life synergies can be expected.
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40
Product Components
SAP Predictive Engineering Insights
Maintenance Execution Systems
FEDEM (Finite Element Dynamics in Elastic Mechanisms)
The FEDEM code is a very quick and advanced Finite Element
Analysis (FEA) code, perfectly suited for IoT applications
Features
Vibrational
Diagnostics
Foundation
SAP Predictive Engineering Insights
Machine
Learning
Structural
Behavior
FEA in real-time
Finite Element Analysis is a standard engineering
methodology for calculating forces and stresses in structures
of any kind. The methodology is typically used in design. SAP
Predictive Engineering Insights enables it to run in real time
on the cloud, thus linking design and operations.
Fedem
Simulation
Additional features
The FEDEM solver includes effects of hydro- and
aerodynamics, soil stiffness and advanced external forces. It
even includes the effect of control systems (hydraulic,
pneumatic and the control logic behind it.
SAP Data Hub on SAP Cloud Platform
Sensor Data
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Business Data
41
Product Components
SAP Predictive Engineering Insights
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)
Hindsight
▪ record of structural loading (accumulated fatigue
damage)
▪ overview and counting of extreme events and
fatigue (used/remaining lifetime)
▪ verification of design methods
hindsight
insight
foresight
Simulation of different options
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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Product Components
SAP Predictive Engineering Insights
Maintenance Execution Systems
Features
Vibrational
Diagnostics
Foundation
SAP Predictive Engineering Insights
Machine
Learning
Structural
Behavior
Fedem
Simulation
SAP Data Hub on SAP Cloud Platform
Sensor Data
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Business Data
43
Product Components
SAP Predictive Engineering Insights
Maintenance Execution Systems
Features
Vibrational
Diagnostics
Foundation
SAP Predictive Engineering Insights
Machine
Learning
Structural
Behavior
Fedem
Simulation
SAP Data Hub on SAP Cloud Platform
Sensor Data
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Business Data
44
Customer Examples
SAP Predictive Engineering Insights
Company and Situation
Arctic Wind operates the northernmost wind farm in the
world. The farm consists of 16 turbines built in 2003 and due
to the remote location, maintenance inspections are
challenging and at times even impossible.
Scenario
•
•
Problem: Lack of engineering insights for better decision
support in operations and maintenance
Solution: Real-time engineering simulation based on
sensor-feeds and a Finite Element Solver
Benefits
•
•
•
Maintenance cost reduction through predictive qualities
Lifetime extension through a full load history
Improved operational efficiency
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Getting Further Information www.sap.com/eam
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46
Thank you.