CUSTOMER
August 2018
Dr. Walter Zimmermann
Product Management
IoT and Digital Connected Asset
Intelligent Asset Management: ASPM and PDMS
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LEGAL DISCLAIMER
This presentation outlines our general productdirection and should not be relied upon in making apurchase decision. This presentation is not subjectto your license agreement or any other agreementwith SAP. SAP has no obligation to pursue anycourse of business outlined in this presentation orto develop or release any functionality mentioned inthis presentation. This presentation and SAP’sstrategy and possible future developments aresubject to change and may be changed by SAP atany time for any reason without notice. Thisdocument is provided without a warranty of anykind, either express or implied, including, but notlimited to, the implied warranties ofmerchantability, fitness for a particular purpose, ornon-infringement. SAP assumes no responsibilityfor errors or omissions in this document, except ifsuch damages were caused by SAP intentionally orgrossly negligent.
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This document is intended to outline future product direction, and is not acommitment by SAP to deliver any given code or functionality. Any statementscontained in this document that are not historical facts are forward-lookingstatements. SAP undertakes no obligation to publicly update or revise anyforward-looking statements. All forward-looking statements are subject tovarious risks and uncertainties that could cause actual results to differ materiallyfrom expectations. The timing or release of any product described in thisdocument remains at the sole discretion of SAP. This document is forinformational purposes and may not be incorporated into a contract. Readersare cautioned not to place undue reliance on these forward-looking statements,and they should not be relied upon in making purchasing decisions.
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Selling equipment
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
Optimized for Physical Structure
Pay per use / Equipment as a Service
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
Mechatronics / Software in Products/Assets
Global Asset Management TrendsTransformation to Smart Digital Connected Assets
NowYesterday
Pay per use
Digital Twin
3D visualization
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Production planners, Operators aim for the following…
Overall equipment effectiveness
Return on assets
Unplanned outages
Annual service and maintenance cost
Planned maintenance budget vs. actual cost
Reduce safety incidents
Reduce costs
Maximize asset productivity
Drive safe operations
Reducing energy and input costs actual cost
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All physical assets are managed according to an Asset Strategy
The prime objective of an asset strategy is to optimize the balance between
equipment performance, equipment availability and the cost of maintaining the asset.
The asset strategy will dictate how assets are cared for and
is measured by KPIs for performance,
availability and cost. This includes safety and
environmental integrity
Preventive
time- or usage
based interval
On-Condition
the P-F-curve based on
measurable deterioration
Predictive
the P-F-curve using
big data & analytics
Failure Finding
risk-based
interval
Run to Failure
Run to
Repair
Modification
includes accepting
degraded performance
Asset strategies are:
and if none of the above the strategy defaults to:
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Asset Strategy and PerformanceMaintenance strategies getting more sophisticated
Source: taken from Gartner and modified.
Run to Failure
Preventive based on time
Preventive based on usage
Based on condition
Predictive Forecasting
Reliability-Centered Maintenance
Financial / Risk Optimized
Main
tenance S
trate
gie
s
Decid
e o
n S
trate
gie
s
Holis
tic E
nte
rprise A
sset
Mgm
t
tactical to
strategic
fragmented to integrated
Few
Data
Big Data
Integrated
Information
Source: Gartner (modified)
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Concept - Intelligent Asset Suite
• Asset Central
Asset Central is the “Foundation” for AIN, ASPM and PDMS (and others). It is
the layer to integrate between SAP Cloud Platform and S/4HANA Cloud for
some key business objects and full e2e next gen asset & service management
scenarios
• Asset Intelligence Network
Collaborative asset management bringing key stakeholders (operator, OEM,
service providers, others) together in a digital ecosystem solving complex
execution, predictive and planning activities with centrally managed asset
information
• Predictive Maintenance & Service
Predict maintenance events to subsequently predict business processes for
operational excellence (planning, procurement, scheduling, execution, …)
lowering risk and improving asset availability
• Asset Strategy & Performance
Define and plan asset goals and maintenance execution strategies holistically
for improved performance
• Predictive Engineering Insight
Model and visualize the physical structure of an asset for real-time calculation
of stress and fatigue to drive predictions
• Core Service Management
Core service process execution via planned and actual order processing in the
integrated S4HANA Digital Core system (integrated with finance / controlling,
procurement, inventory)
IoT / ML
Asset
Strategy &
Performance
Predictive
Maintenance
& Service
Asset
Intelligence
Network
ERP(Logistics / CRM
Production)
Digital
Platform
Data Hub Cloud
Platform
Maintenance
Execution
Service
Management
Predictive
Engineering
Insight
Asset
Central
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Asset Central
Mode 1: System of Record Mode 2: System of Differentiation
Asset Central becomes
▪ the “Glue-Component” between
S/4HANA Cloud Maintenance
Management and Components build on
SAP Cloud Platform
▪ the leading System for the Equipment
(and Functional Location)
▪ mandatory for Cloud Deployments; it
remains optional (but recommended)
for on-Premise
▪ Asset registry for managing complex
asset structures and provide seemless
integration and data consistency.
▪ Consistent Fiori UI and APIs that work
across platforms – CF, XSA, Neo*
SAP Asset
Strategy &
Performance
SAP
Predictive
Maintenance
& Service
SAP Asset
Intelligence
Network
SAP
Predictive
Engineering Insight
Maintenance
& Service
Management Asset
Central
Consumed via Mobile
e.g. Sensor Feeds, Data Historian, etc.
Product
Lifecycle
Management
Digital
Manufacturing
Insights
Logistics
SAP Cloud Platform & XSA Onprem(Big Data Platform)
S/4HANA Cloud
Efficiently manage Core Service
Processes in Service Core▪ Collaboration Platform – Network
▪ IoT-Platform
‒ Sensor Data, Big Data
‒ Prediction / ML
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End to end value across the lifecycle of the Digital Twin
Automatic
Onboarding &
Topology Detection
Asset Health & Key
Indicators
2D Sensor Chart
and Alert List
Error Codes and
Knowledge Base
Failure Mode based
Predictions
3D Repair
Instructions
Work Orders & PM
Notifications
Service Ticket
Spare Part Ordering
Questionnaire &
ChecklistImprovement
Request
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The DIGITAL TWIN will cover the entire product lifecycle
Ideas
Geometries
Stress simulations
Cost
Production data
Quality fingerprint
Ramp Up
. . .
Output
Performance
. . .
Issues
Cost
Profitability
Closed loop engineering
Designs
. . .
. . .
. . .
As Built
The physical world
As Designed
Engineering
The digital world
As MaintainedAs Delivered
DecommissionProduction Installation Operation
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Intelligent Asset ManagementVision and Objectives
“Support Manufacturers and
Asset Operators in
defining, planning and monitoring
the optimal service and
maintenance strategy for
physical products and assets
by providing the required level of
collaboration, integration and
analytical insights”
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Asset Management Monitoring
Analyze Cost and Performance
Maintenance Strategy Execution
Perform Inspections, Condition Monitoring,
Predictive Maintenance, …
Maintenance Strategy Implementation*
Create/Change/Delete Maintenance Plans, Task Lists, Inspection, Condition Based Maintenance, Predictive Maintenance, Run to Fail
PM Review*
Preventive Maintenance Review
Evaluate the current maintenance
plans and their effectiveness
SAP Asset Strategy and Performance ManagementDeveloping a Maintenance Strategy
Manage Asset InformationManage Locations, Equipment, Groups, Systems, Failure
Modes…
Asset Risk & Criticality Assessment
Rating assets according to criticality for the business
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
S/4HANA and SAP ERP
PdMS
Application of Appropriate Methodology
*planned
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Equipment: Review Risk & Criticality Matrix
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Preventive, Predictive towards Prescriptive Maintenance
Today:
Use of
Maintenance
Strategy*
The Internet of Things is
leading to increased use of
predictive maintenance
Although still relevant,
preventative maintenance
typically results in over-maintaining
assets and high cost
*Proportion of maintenance strategies are for illustration purposes only and will vary based on many factors
Future:
Use of
Maintenance
Strategy*
Run to Failure Preventative Predictive
TODAY FUTURE
The goal is to enable more IT/OT driven
approaches for prescriptive maintenance
with Machine Learning and IoT enabled
Engineering Simulations to reduce
unplanned failures and the number of
maintenance actions
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Multiple Approaches to Predictive MaintenanceIT driven approaches are on the rise
Asset
Conditio
n
TimeTotal Failure
Functional FailureAudible Noise
Ancillary Damage
Battery Impedance Test
Hot to Touch
Potential Failure = First Indication of Failure
Human
Driven
T
F
Equipment
Driven
IT Driven (data science & business rules)
Oil Analysis
X-ray Radiography
P Potential Failure
Why more IT driven approaches?▪ IIoT/device connectivity
▪ Big data available for training models
▪ Declining hardware and software costs
▪ Massive computing powerP
P
P
More time to respond enables
greater flexibility to dynamically plan
maintenance events
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Preventive
Companies are moving from a
reactive to a proactive approach
to maintenance.
An opportunity is available for
organizations to leverage machine
data for better business insights.Wait until a machine
fails and then
undertake
maintenance.
Perform
maintenance at
regular intervals,
based on
observations of
abnormalities.
Continuously observe
the status of assets and
react to predefined
conditions and events.
Apply advanced analytics of
operational and business
data to help determine the
condition of specific
equipment and predict when
to perform maintenance.
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Condition-
basedPredictiveReactive
Where are maintenance and service heading?Organizations are maturing their maintenance strategies
Customer
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What is the best strategy?How to transform the game?
• Quantum leaps can be reached by changing the
maintenance strategy to a more agile approach.
• Condition data allows for a ranking of assets
according to a health score.
• For “healthier” assets the service interval can be
prolonged while it can be shortened for others.
• This results in less failures while reducing
maintenance cost.
t
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SAP Predictive Maintenance and ServiceVision and focus scenarios
Scenario Solution components
Alert-based condition monitoring (including prescriptive maintenance)
Users can drill down into the List of Alerts and use observations to initiate follow-up activities from a
list of possible actions based upon failure modes and maintenance history. Executed actions are
documented and current status visualized.
- Alert analysis tool
- Alert modeling and creation
- Event to action (business rules)
- Deduplication of events
- Work activity analysis tool
Indicator-based condition monitoring
Based on machine learning health scores, users can find the bad-acting asset and drill down to
analyze the root cause using the explanation of health scores. With their observations, users can
initiate follow-up activities by selecting from a list of possible actions based on failure modes and
maintenance history. Executed actions are documented and visualized.
- Equipment scores analysis tool
- Equipment list analysis tool
- Key figure analysis tool
- Indicator management
- Aggregation and categorization
Fleet analysis
The previous scenarios focus on single equipment, while fleet analysis offers functionalities to
operate on a fleet level. This scenario extends the fleet analysis capabilities by including
information from indicator forecasting.
- Analysis tool catalog
Emerging issue detection (EID)
EID allows for the early identification of problems and their root causes in a fleet of machines using
exploration and machine learning.
- Explorations
- Evidence packages
- Collaboration
- Explorations overview
Indicator-based maintenance plan optimization
Users can simulate optimal maintenance plans for equipment based on maintenance and health
score history by applying specific target variables. Simulation results can be transferred to SAP
Asset Intelligence Network to be used in a preventive maintenance review process.
- 2D chart analysis tool
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Solution
Customer Example TrenitaliaTrain Operator
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• Improve effectiveness
of maintenance
programs
• Data fusion between
IT and OT data
• Remote train
diagnostics
• Engineering rules and
predictive models
• Dynamic planning of
maintenance schedules
BRAKES
Energy Dissipation
versus Mileage
DOORS
Open/Closure Cycles &
Times
versus Mileage
• Higher asset availability & passenger satisfaction
• Projecting 100M Euro savings per year in
maintenance operations costs when fully
implemented
Benefits
Improved
Program
Effectiveness
Starting with
Improvements
to Preventative
Maintenance
Plans
Run to Failure Preventative Predictive
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Innovation in Maintenance: Why, and Why Now?
Unsatisfactory current practice:
• Preventive maintenance at fixed deadline based on distance
and time
• Corrective maintenance for fault recovery
• Failures are not prevented
• Low correlation between maintenance deadlines and effective
consumption of components
• Maintenance plans optimized only in terms of logistic execution
• Complex and ad-hoc checks in first level
New opportunities enabled by technology:
• Big data and data science
• Hyperconnectivity / Internet of Things
• Advanced process optimization
• Automated diagnostics and checks based on vision and laser
scanner systems
• Robotic systems for inspections and operations
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Guiding Principles
Systemic optimization of the
performance of fleets to fulfill the
requirements of commercial
services
• One platform for all the components, all the trains and all
diagnostic equipment
• Integration of operational and transactional data to drive insight,
business consequences and execution
• Full extensibility to progressively increase the intelligence and
value of the system incorporating additional insights
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Planning EngineImproving the Relevance of Maintenance Activities
Planning based on Km /
Time of operations
Planning based on Life Indicators Planning based on Life
and Health Indicators
• Current model, standard in the
industry
• Easy to operate because all the
components in the material share the
same driver
• Sub-optimal for the same reason
• Based on more relevant drivers and
indicators that better represent the
effective current and expected usage
of every single component
• Increased precision is directly
connected with the quality and
precision of the planning for the
materials
• Requires optimization methods in
order to produce a plan, due to the fact
that every component in a material can
have different life situation
• Further increase the relevance of the
plan, considering the future effects that
the evolution of life indicators will have
on the ability of every component to
perform, and on its risk of failure
• Requires sophisticated mathematical
methods to predict the behavioral
patterns of health indicator based on
the expected usage of the materials
• Health indicators can in any case used
to trigger short term maintenance
activities
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Life and Health Indicators in Action
BearingsWheelsEngineDoorsPantographA/C…
Life Ind.f(Km, radios, weight)f(Km, exchanges, radios, weight)f(Km, weight)Open / close cyclesUp / down cyclesf(hours, ext temp, int temp)
Health Ind.Vibration patternVibration patternf(power gen, power absrbd)Open / close timeUp / down timef(Delta int temp)
Telemetric readings
Operational plans for Rolling Stock
Check availability of resources
Maintenance calendar
Consolidated picture for the planning unit
Check against safety thresholds
Predicted Life and Health Indicators
Detailed Information on Infrastructure
• “Real” Big Data in action: hundreds of TB, huge number of
sources and entities involved
• Complex algorithms to predict indicators and optimize the
outcome across multiple dimensions
• Huge transformational value and financial impact
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Life Indicators in Action: Braking System
Objective and Approach
Calculation of the life indicator energy dissipation by friction braking systems, with separated analysis for locomotives and coaches
Development and test of calculation algorithms for all the possible cases identified
Pressione in Condotta Generale e Cilindro Freno
Energia dissipata cumulata per le carrozze
Results Achieved
Monitoring of the effective usage and level of wear-out for every single component of the braking system against the risk thresholds identified
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Life Indicators in Action: Braking System
• High variability of the energy dissipated
per km clearly indicates that distance is
not a good indicator of consumption for
braking systems
• Comparison between traditional
indicators such as km and more precise
life indicators highlights the significant
opportunity for optimization of
maintenance operations
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Anomaly Detection
Earth-Movers Distance (EMD)
• Every battery can be compared to a “normal” battery in each mode of operation… idle, charging, discharging
“Normal” Battery Installed Battery
=/
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Ranking of Distance Among Homogeneous Components
Rank Battery1 1282 3483 1334 1445 0086 1817 3668 0519 336
10 536…
371 103372 135373 281374 463375 096376 109377 086378 139379 308380 280
Massive automatic analysis of components vs. normal behaviors to measure and rank the distance, and
identify the potential bad actors
Maintenance policies get
differentiated by the various
sections of the ranking (e.g.:
do not perform any
preventive actions for the
batteries in the top 50% of
the ranking)
Very significant projected
savings on maintenance
costs without increase in
failures
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Customer exampleCompressor manufacturer
• Provider of compressed air systems and
compressed air consulting services
• Changed their business model from selling
compressors to selling compressed air
• Moved customers from CAPEX to OPEX
• Compressors equipped with sensors
• SAP Predictive Maintenance & Service On-Premise Edition
• SAP HANA
• SAP CRM Service
Company
Solution
Benefits
• IoT as an enabler for the new business model
• Improved availability of compressor stations
• Move from unplanned to planned maintenance
Process Innovation
IT / OT
Connectivity
Condition MonitoringRemote Service
Fault Pattern
Recognition
Machine Health
Prediction
Create Service
OrderSchedule Order
Execute Order
on mobile deviceVisual Support
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Customer exampleIndustrial equipment manufacturer
• Leading manufacturer of separators and
decanters for industrial usage
• New service offering which monitors equipment
during the operation to ensure service contract
compliance
• SAP HANA Cloud Platform
• SAP Predictive Maintenance & Service Cloud Edition
• SAP CRM Service
Company
Solution
Benefits
• Service execution based on real-time machine data
• Increased machine uptime
• Improved service contract compliance
• Higher service productivity and customer satisfaction
Process Innovation
Spare Parts &
Tools
Remote
Service
Engineer
Real-time
Monitoring
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SAP Predictive Maintenance and ServiceMachine learning challenges
High dimensional data
No labeled failure data
Rare failure events
Outdated models, human scale
Use case specific algorithms
Feature construction/selection requires data
scientists & domain user collaboration
Model management, continuous learning, model
quality assessment and automated scoring
Anomaly detection and adaptive learning
through user feedback
Failure prediction using ensemble learning
Extensibility, integration and productization of new
asset and customer-specific algorithms
SOLUTION
Lack of data science resources Automated machine learning for failure prediction and
anomaly detection
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SAP Predictive Maintenance and ServiceMachine Learning Engine - Adaptive Learning
+ No need for labels but normal state has to be
known for some algorithms
+ Finds previously unknown failures
Not every anomaly will be related to a failure…
may lead to false alarms
A domain expert has to validate the anomaly
and decide if action should be taken
+ If the quality of the model is good then the
predictions can be done automatically without
involvement of experts
+ Some algorithms automatically reveal possible defect
patterns which can be interpreted by the domain user
Standard supervised failure prediction
algorithms need sufficient number of failures
Anomaly Detection Failure Prediction
Adaptive Learning
Need adaptive learning to avoid false alarms and
improve accuracy of models
Future capability*
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SAP Predictive Maintenance and ServiceMachine Learning Engine – Anomaly Detection Algorithms
Anomaly DetectionType Technique Input Data
Type
Output
High-dimensional
Anomaly Detection
Principal Component
Analysis (PCA)
Sensor Anomaly
score
High-dimensional
Anomaly Detection
One-Class Support
Vector Machine (One-
Class SVM)
Sensor Anomaly
score
Distance-based
Anomaly Detection
Earth-Movers Distance
(EMD)
Sensor Anomaly
score
Time-based
Anomaly Detection
Multivariate Auto
Regression (MAR)
Sensor Anomaly
score
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SAP Predictive Maintenance and ServiceMachine Learning Engine - Failure Prediction Algorithms
Type Technique Input Data
Type
Output
Failure Prediction
based on Failure
and Sensor Data
Tree Ensemble
Learning
Sensor and
Failure
Data (IT)
Probability of Failure
Failure Prediction
based on Failure
and Sensor Data
Logistic Regression Sensor and
Failure
Data (IT)
Probability of Failure
Failure Prediction
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SAP Predictive Maintenance and ServiceMachine Learning Engine
Apply Machine
Learning ProcessOutput
Machine Learning Engine
Remaining
Useful LifeAnomaly
ScoreHealth
Status
2530 days
SAP Predictive Maintenance and Service
Continuous Improvement & Learning
Failure
Prediction
Predictions made when
correlations are found
between input data and
historic failures
Anomaly Detection
Trigger anomaly alert
when the algorithm
detects an abnormal
pattern
New
Algorithms**
Extensibility
Model
Management
Adaptive
Learning*
Domain expert
feedback
Future capability*Through SAP and Partner**
Train
Model
Configure
Model
Score
model
Feedback
Evaluate
Model
Analysis Tools &
Equipment Pages
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SAP Predictive Maintenance and Service, on premise editionMachine Learning Engine
*Roadmap Item
Continuous Improvement & Learning
Failure
Prediction
Trigger prediction when
algorithm detects a
specific combination of
input variables
Anomaly Detection
Trigger anomaly alert
when the algorithm
detects an abnormal
pattern
New
Algorithms
Extensibility
Model
Management
Tools
Reinforcement*
Domain expert
feedback
• Supervised learning enables failure
predictions like Remaining Useful Life
• Finds contributing factors to failure events
• Unsupervised learning detects anomalies
• Enables Health Scores
• Expert feedback
• Models change as operational
environment changes
• Extensibility for out-of-the-box
algorithms
• Possibilities to deploy new
R based algorithms
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SAP Predictive Maintenance and Service, on premise editionMachine Learning Engine – Model Management
• Machine learning models are automatically applied to new incoming data
• Models are regularly re-trained using scheduling capabilities
• Model management capabilities allows us to maintain model versions
Configure model Score model
Deactivate
Train model
Retrain model
Model
ConfigurationModel Version Scores
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SAP Predictive Maintenance and ServiceHow Failure Mode Analytics works
Use text mining to identify
failure modes from
technician notes
System matches topics to
standard failure modes (e.g.
ISO 14224)
Expert user double-checks
matching results
System uses supervised machine
learning based on user reinforcement to
assign all notifications to failure mode
System provides out-of-box metrics and
and visualizations
1 2 3
45
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SAP Predictive Maintenance and ServiceFailure mode analytics
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1. This is the current state of planning and may be changed by SAP at any time without notice.
Asset CentralProduct road map overview – Key innovations
Asset Master Data▪ Continued Indicators enhancement▪ Simplified Attribute definition through
reusable Code Lists▪ Continued Failure Modes enhancement▪ Modeling Alert Types and Alert Type
Groups▪ Multi language support for Models,
Spare Parts and Attributes▪ Mapping visual components with Spare
Parts▪ Mass assignment of Spare Parts▪ Guided Tour enhancement
Analytics▪ Advanced Analytics for Obsolescence
Management
General Topics▪ New application for data protection
and privacy
▪Integration▪ Enhanced integration to SAP ERP
Plant Maintenance and S/4 HANA▪ Mapping of SAP Cloud Platform IoT
Services 4.0
Asset Master Data▪ Indicators enhancement▪ Failure modes enhancement▪ Multi-standards and change of
classification▪ Mass object assignment and
harmonization
General Topics▪ IoT onboarding
▪Maintenance Execution▪ Reset Indicators and basic collection of
maintenance feedback
▪Integration▪ Enhanced integration to SAP ERP
Plant Maintenance and S/4 HANA ▪ Automatic onboarding of SAP IoT
Application Enablement Services and SAP Cloud Platform IoT Services 4.0
▪ Integration to SAP Digital Manufacturing Insights
▪ Integration to SAP Predictive Engineering Insights
▪Asset Master Data▪ Further enhancement of multi language
UI▪ Managing multiple Equipment
structures over the lifecycle▪ Initial release of RAMI 4.0
Administration Shell▪ Enhanced geographical Map▪ Hierarchical and fleet-based Indicators
aggregation
▪Maintenance Execution▪ Advanced collection of maintenance
feedback▪ Multiple values capturing of Indicators
▪Integration▪ Enhanced integration to SAP ERP
Plant Maintenance and S/4 HANA▪ Continued Integration to SAP
Predictive Engineering Insights▪ Continued Integration to SAP Digital
Manufacturing Insights▪ Enhanced Integration with SAP Edge
Services
Asset Master Data▪ Managing multiple Location and
System structures over asset lifecycle▪ Continued RAMI 4.0 Administration
Shell▪ Modelling Model variants▪ Enhanced search capabilities▪ Enhanced roll-up & drill-down of
object content across object hierarchy
▪Integration▪ Enhanced integration to SAP ERP
Plant Maintenance and S/4 HANA▪ Integration with SAP Analytics Cloud▪ Integration with SAP Mobile Asset
Manager▪ Document management
1805 – Recent innovations1,2 1808 – Planned Q3/18051,2 1811 – Planned Q4/18081,2 1902 – Planned Q1/20191,2
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1. This is the current state of planning and may be changed by SAP at any time without notice.
SAP Asset Intelligence NetworkProduct road map overview – Key innovations
Asset Master Data▪ Enhanced Indicators and Component
Indicators▪ Continued Failure Modes enhancement▪ Modeling Alert Types and Alert Type
Groups▪ Multi language support for Models, Spare
Parts and Attributes▪ Mapping visual components with Spare
Parts▪ Harmonized data model, UI and release
cycles for SAP PdMS, SAP ASPM and SAP AIN
General Topics▪ GDPR support and audit report▪ Advanced Analytics for Obsolescence
Management
Integration▪ Enhanced integration to SAP ERP Plant
Maintenance and S/4 HANA
▪ SAP PLM Integration: Model Publication in S/4HANA Cloud 1805
▪ Integration with SAP Edge Services
▪ Mapping of SAP Cloud Platform IoT Services 4.0
Asset Master Data▪ Continuous Improvement for Failure Modes▪ Continuous Improvement for Indicators▪ Multi-language APIs and maintenance UI
for key objects▪ eCl@ss and standards▪ Notification and Work Order details and
enhanced integration
General Topics▪ IoT onboarding
Maintenance Execution▪ Reset Indicators and basic collection of
maintenance feedback
Integration▪ Enhanced integration to SAP ERP Plant
Maintenance and S/4 HANA ▪ Automatic onboarding of SAP IoT
Application Enablement Services and SAP Cloud Platform IoT Services 4.0
▪ Integration to SAP Digital Manufacturing Insights
▪ Integration to SAP Predictive Engineering Insights
Asset Master Data
▪ Content Packages and Digital Services
▪ Automatic mass-upload and publishing
▪ Sharing of work orders and notifications
▪ Model Lifecycle support and generations
▪ Reference Implementation for
RAMI 4.0 Administration Shell
▪ Enhanced geographical Map
▪ Hierarchical and fleet-based Indicators
aggregation
Maintenance Execution
▪ Advanced collection of maintenance
feedback including multiple values
capturing of Indicators
Integration
▪ Enhanced integration to SAP ERP Plant
Maintenance and S/4 HANA
▪ Enhanced Integration with SAP Edge
Services
▪ Ariba Solution Integration
▪ Blockchain enablement
Asset Master Data
▪ Enhanced search capabilities
▪ Enhanced roll-up & drill-down of object content across object hierarchy
▪ Service Process Integration
▪ Enhanced Firmware processes
Integration
▪ Enhanced integration to SAP ERP Plant
Maintenance and S/4 HANA
▪ Integration with SAP Analytics Cloud
▪ Integration with SAP Mobile Asset Manager
1805 – Recent innovations1,2 1808 – Planned Q3/18051,2 1811 – Planned Q4/18081,2 1902 – Planned Q1/20191,2
45CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
1. This is the current state of planning and may be changed by SAP at any time without notice.
SAP Asset Strategy and Performance ManagementProduct road map overview
1808 – Recent Innovations 1811 – Planned Q4/20181 1902 – Planned Q1/20191 1905 – Planned Q2/20191,2
Enhancements to Assessment
Process
• Ability to display pending
assessments
• Compare two or more assessments
that use the same template
• Swap dimensions on the Risk Matrix
FMEA enhancements
• Guided Activity with a video tutorial for
FMEA assessments
• Identify and include similar equipment for
scope of a FMEA study
Checklists
• Generation of sub-class specific
Inspection Checklists for Equipment,
Locations and Models
• Perform Inspections on multiple objects
including updating attributes and
indicators
• Generate and store Inspections Results as
PDF files
Analytics
• Risk Distribution across fleet and
class/sub-class
• Additional Highlight Cards
Reliability Centered Maintenance
(RCM)
• New assessment for enabling a RCM
study
• Record Operational Context,
Functions and Functional failures
• Configurable questionnaire to help
the assessment team make better
decisions
Root Cause Analysis (RCA)
• Perform RCA using 5-Why and Cause
& Effect tree analysis
• Cascade Findings to similar Systems,
Functions and Locations
PM Review
• Review tasks across all maintenance
plans by Maintenance Type (Repair vs
Replace) and identify redundancies &
discrepancies.
• Recommend changes to maintenance
plans based on unmitigated risk
reduction and Cost
Asset Strategy Workbench
• Strategic view of Equipment and
Functional Locations
• Guidance to Reliability Managers on
maintenance methodology
46CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
1. This is the current state of planning and may be changed by SAP at any time without notice.
SAP Predictive Maintenance and Service, cloud editionProduct road map overview – Key innovations
Maintenance Planning, Scheduling &
Optimization
▪ Rule-based alert creation for equipment models
▪ Automatic rule-based actions for alerts, like
sending of emails
▪ Visualization of equipment on a map
▪ Support for thematic maps using indicators
▪ Multi-chart data visualization for a single
equipment
▪ Model quality assessment in Machine Learning
Engine
▪ New machine-learning algorithms for anomaly
detection and failure predictions
Maintenance Planning, Scheduling & Optimization
▪ Enhanced rule definitions supporting advanced rule conditions and rules on equipment level
▪ Alert de-duplication to suppress creation of redundant alerts
▪ Enhanced alert lifecycle information with alert status and processor
▪ Equipment benchmarking by comparing indicators across equipment
▪ Indicator forecasting and visualization of forecasted values in 2D charts
▪ Automated machine learning to enable business user to configure equipment health monitoring
Analytics
▪ Ad-hoc data exploration and analytics through integration with SAP Analytics Cloud
Integration
▪ Basic customer portal functionality through integration with SAP Asset Intelligent Network
Maintenance Planning, Scheduling &
Optimization
▪ Fingerprint management
▪ Prescriptive maintenance supporting alert
explanation and recommended actions for
alerts
▪ Machine Learning Engine supporting adaptive
learning through user feedback and health
indicator explanation
▪ Root cause analysis using correlations between
failures and equipment master data and
configuration
Integration
▪ Enhanced customer portal functionality
supporting extensibility
Generic topics
▪ Personalization through Fiori variant
management
Maintenance Planning, Scheduling &
Optimization
▪ Extensibility of Machine Learning Engine through
custom algorithms
▪ Support for additional chart types in data
visualization, like histograms and characteristic
curves
▪ Enhanced emerging issue detection enabled by
additional analysis tools and machine learning
algorithms
▪ Optimizing counter-based maintenance strategies
in ERP using indicators
1805 – Recent innovations1 1808 – Planned Q3/20181,2 1811 – Planned Q4/20181,2 1902 – Planned Q1/20191,2
48CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionSystem and component level visualizations
Machine Learning Engine
Analysis Tools Catalog
SAP Predictive Maintenance and Service
Explorer (fleet view)
Explorer Equipment
Page
SAP Leonardo Foundation
SAP Leonardo for Edge Computing
SAP Leonardo Foundation
SAP Leonardo for Edge Computing
Logistics & Maintenance
Execution Systems
Business DataMachine Data
Equipment Page
49CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Explorer
SAP Predictive Maintenance and Service, on premise editionExplorer - Analysis Tools Catalog
*”Health Status Overview” is an example of a custom Analysis Tool built using SDK
Work Orders Notifications
3D Chart
50CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations
New Orleans Refinery
Eagle Ford Field
Locations
Explorer
51CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations
New Orleans Refinery
Eagle Ford Field
Locations Filter by Location
Filter by Locations
Explorer
52CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations
New Orleans Refinery
Eagle Ford Field
Locations
Filter by Locations
Filter by Location Analysis Tools Catalog
Analysis Tools Catalog
Explorer
53CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location Analysis Tools Catalog
Analysis Tools Catalog
Explorer
54CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location
Key Figures Analysis Tool
Analysis Tools Catalog Analysis Tool
Analysis Tools Catalog
Explorer
55CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location
Equipment List Analysis Tool
Analysis Tools Catalog
Analysis Tools Catalog
Analysis Tool
Explorer
56CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location Analysis Tools Catalog
Analysis Tools Catalog
Analysis Tool
Explorer
Equipment Scores Analysis Tool
57CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location
Map Analysis Tool
Analysis Tools Catalog
Analysis Tools Catalog
Analysis Tool
Explorer
58CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location
3D Chart Analysis Tool
Analysis Tools Catalog
Analysis Tools Catalog
Analysis Tool
Explorer
59CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionExplorer
Locations Filter by Location
Custom Analysis Tool
Analysis Tools Catalog
Analysis Tools Catalog
Analysis Tool
*”Health Status Overview” is an example of a custom Analysis Tool built using SDK
Explorer
60CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Equipment View Explorer
Explorer
Equipment View
Serial #12345
Equipment Page
61CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Equipment Page
Explorer
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Equipment View
Equipment View Explorer
Serial #12345
Equipment Page
62CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
63CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
64CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
65CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
66CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
67CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
68CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line
69CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and Service, on premise editionEquipment Page
Information▪ Highlights
▪ Attributes
▪ Model Information
▪ Installation Information
▪ Life Cycle Information
Structure and Parts▪ Structure
▪ Spare Parts
Documentation▪ Documents
▪ Instructions
▪ Announcements
Monitoring▪ 2D Chart
▪ Error Codes
▪ Failure Modes
▪ Improvement Cases
▪ Work Orders & Notifications
Time Line