MIT Enterprise Forum of Cambridge Connected Things 2017 Keynote Speaker: Alan Southall, SVP of Engineering, Head of IoT Predictive Maintenance, SAP

1. © 2017 SAP SE. All rights reserved.
SAP Predictive Maintenance & Services
Alan Southall, SVP Engineering, Head of PdMS | SAP IoT | March 2017

2. © 2017 SAP SE. All rights reserved.
Disclaimer
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mentioned in this presentation. This presentation and SAP's strategy and possible
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responsibility for errors or omissions in this document, except if such damages were
caused by SAP intentionally or grossly negligent.

3. © 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of Things
Connecting Things to Business Processes
Predictive Maintenance & Services
Combining IT/OT data to optimize maintenance
Customer Stories
Real life applications of predictive maintenance

4. © 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of Things
Connecting Things to Business Processes
Predictive Maintenance & Services
Combining IT/OT data to optimize maintenance
Customer Stories
Real life applications of predictive maintenance

5. © 2017 SAP SE. All rights reserved.
Delivering Outstanding Results to Customers and Stakeholders
Customers
87%
of Forbes
Global 2000
98%
of the 100
most valued
brands
Financials
€14.87B
(+6%) software
and software-
related services
revenue
€5.5 B
(+4%)
software and
cloud revenue
100%
of Forbes top
sustainability
companies
80%+
are SME
companies
€17.5 B
(+4%)
Total revenue
Solutions
25
Industries
11
Lines of
business
Employees
74,406
employees
EMEA: 33,340
Americas: 22,071
APJ: 18,995
79%
Employee
Engagement
Index
SAP HANA
5,800 SAP HANA customers
1,800 startups
8,500 trained partners
120+
nationalities
worldwide
70%
Business Health
Culture Index
Source:
SAP Corporate Fact Sheet 1/2015; SAP Integrated Report 3/2015

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SAP Leonardo empowers the LIVE business
Connecting Things to Business Processes
Business
Processes
SAP Leonardo Foundation
SAP Cloud Platform
SAP Leonardo
Applications
Things
Next Level of Experience
Sources Of DataIntegration | Business Partners | Networks
Machine LearningBlockchain

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SAP Leonardo Portfolio

8. © 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of Things
Connecting Things to Business Processes
Predictive Maintenance & Services
Combining IT/OT data to optimize maintenance
Customer Stories
Real life applications of predictive maintenance

9. © 2017 SAP SE. All rights reserved.
Connected Asset Lifecycle
SAP IoT and PdMS delivers tangible business outcome
Failurerate
Burn-in
"infant mortality"
Wear-outNormal life
Asset lifetime
Emerging Issues Detection
(EID)
Early identify, monitoring and
managementof emergingasset issues
using exploration, root cause and
warranty analytics
PredictiveMaintenanceandService
(AHCC& VA)
Holistic managementof asset health and dynamic
optimizationof maintenance schedules and resources
based on health scores, anomaly detection and spectral
analysis
AssetInvestmentOptimization
and Simulation
Analyze remaining useful life of assets to
optimallyplan for new investments based on
business needs, asset health and risk of
failure.
SAP ERP, S4HANA, CRM, C4C

10. © 2017 SAP SE. All rights reserved.
Connected Asset Lifecycle
SAP IoT and PdMS delivers tangible business outcome
Failurerate
Burn-in
"infant mortality"
Wear-outNormal life
Asset lifetime
Emerging Issues Detection
(EID)
Early identify, monitoring and
managementof emergingasset issues
using exploration, root cause and
warranty analytics
AssetInvestmentOptimization
and Simulation
Analyze remaining useful life of assets to
optimallyplan for new investments based on
business needs, asset health and risk of
failure.
SAP ERP, S4HANA, CRM, C4C
PredictiveMaintenanceandService
(AHCC& VA)
Holistic managementof asset health and dynamic
optimizationof maintenance schedules and resources
based on health scores, anomaly detection and spectral
analysis

11. © 2017 SAP SE. All rights reserved.
Connected Asset Lifecycle
SAP IoT and PdMS delivers tangible business outcome
Fleet Level View Single Asset Details

12. © 2017 SAP SE. All rights reserved.
Business Value for SAP PdMS
Overall cost reduction in maintenance efforts
~1% machines with down time
Convert unplanned
maintenance to planned
maintenance to avoid down-
time and improved equipment
effectiveness
NumberofAssets
Health of Asset / Maintenance Need

13. © 2017 SAP SE. All rights reserved.
Business Value for SAP PdMS
Overall cost reduction in maintenance efforts
Health of Asset / Maintenance Need
Dynamically optimize the
entire maintenance schedule in
order to reduce the overall
maintenance costs and
reduce components on stock
NumberofAssets
Standard maintenance interval
for all assets the same
Optimized maintenance interval
per asset
6 services executed
4 services really needed
4 weeks 4 weeks4 weeks 4 weeks 4 weeks 4 weeks
5 weeks 5 weeks 5 weeks9 weeks
6 weeks 6 weeks 6 weeks 6 weeks
4 weeks 4 weeks4 weeks 4 weeks 4 weeks 4 weeks

14. © 2017 SAP SE. All rights reserved.
Edge, Connectivity
and Storage
Key Challenges
Rare Events | Data Quality and Varsity | Data Variety, Fusion and Volume
v Dynamically optimize
maintenance and service
activities with prescriptive
analytics
v Integration into EAM, PM,
MRS and AIN
v Condition Monitoring
v Onboarding
v Device management
v Security
v Connectivity
v Data ingestion
v Big Data infrastructure
v Avoid unplanned down-time
v Improved equipment
effectiveness
v Reduce overall maintenance
costs
v Reduce components on stock
v Data Preparation
v Support data fusion process,
i.e. sensor data combined with
business information
v Operationalized data fusion
services
v KFR Engine
v Machine Learning Engine
v Anomaly Detection
v Ensemble Learning
v Model Base Engine
v FEA Engine
IT/OT Convergence PdMS Derived
Signal Management
PdMS AHCC, DMM
& Integration
PdMS Business
Outcome

15. © 2017 SAP SE. All rights reserved.
Edge, Connectivity
and Storage
Key Challenges
Rare Events | Data Quality and Varsity | Data Variety, Fusion and Volume
v Dynamically optimize
maintenance and service
activities with prescriptive
analytics
v Integration into EAM, PM,
MRS and AIN
v Condition Monitoring
v Onboarding
v Device management
v Security
v Connectivity
v Data ingestion
v Big Data infrastructure
v Avoid unplanned down-time
v Improved equipment
effectiveness
v Reduce overall maintenance
costs
v Reduce components on stock
v Data Preparation
v Support data fusion process,
i.e. sensor data combined with
business information
v Operationalized data fusion
services
v KFR Engine
v Machine Learning Engine
v AnomalyDetection
v Ensemble Learning
v Model Base Engine
v FEA Engine
IT/OT Convergence PdMS Derived
Signal Management
PdMS AHCC, DMM
& Integration
PdMS Business
Outcome

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PDMS Machine Learning Engine Overview
Usable for any asset type and manufacturer
Data
Data Preparation, Fusion
and Feature Selection
Reinforcement
using user feedback
Health Scores & Alerts Create Work Activities
Continuous learning &
application to new data
Continuous learning &
application to new data
Continuous learning &
application to new data
Failure Prediction
using automatic ensemble
learning on known failures
New Algorithm
Using extensibility
Anomaly Detection
using unsupervisedlearning
without labeled data

17. © 2017 SAP SE. All rights reserved.
General Approach
• Learn the normal behavior
• Principal Component Analysis (PCA) rotates the coordinate system to explain a
major part of the variation of the data by the first few new coordinates
• Detect deviation from normal
• We apply PCA coordinates to search for multivariate anomalies using an adjusted
sum of squares as scoring function
• Choose a threshold at which a data point is considered an anomaly
• An alert is being raised which has to be validate by a domain expert
Anomaly Detection
With Principal Component Analysis
PCA

18. © 2017 SAP SE. All rights reserved.
Anomaly Detection
With Distance Based Failure
Rank Battery
1 128
2 348
3 133
4 144
5 008
6 181
7 366
8 051
9 336
10 536
…
371 103
372 135
373 281
374 463
375 096
376 109
377 086
The algorithm is trained to inspect the data for you

19. © 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of Things
Connecting Things to Business Processes
Predictive Maintenance & Services
Combining IT/OT data to optimize maintenance
Customer Stories
Real life applications of predictive maintenance

20. © 2017 SAP SE. All rights reserved.
Customer Example
Compressor Manufacturer
Company
One of the largestproviders of compressed airsystems
and compressed air consultingservices.
Situation:Changed the business model from selling
compressors to sellingcompressed air
Solution
• Compressors equippedwith sensors
• SAP PredictiveMaintenanceand Service solution
• SAP HANA software
• SAP CRM applicationfor use in service on SAP HANA
IT and OT
connectivity
Asset health control
center
Fault pattern
recognition
Machine health
prediction
Create maintenance
or service order
Execute order
on mobile device
%
0011001
1101001
Visual supportSchedule order
OrderStatus
Non-SAP
applications
SAP S/4HANA
C4C / CRM
Process Innovation
Benefits
• IoT as an enabler for the new business model
• Improved availability of compressor stations
• Move from unplanned to planned maintenance

21. © 2017 SAP SE. All rights reserved.
Customer Example
GEA Separators
Company
GEA is one of the largest suppliersof process technology
for the food industry and for a wide range of other
industries. In 2015, GEA generated consolidatedrevenues
in excess of about EUR 4.6 billion.
Situation:Need for IoT solution in order to extend service
business and as differentiatorto their competition.
Solution
• SAP PredictiveMaintenanceand Service solution
• SAP PredictiveAnalyticssoftware
• SAP CRM
Process Innovation
Benefits
• Company: Ability to offer new higher margin service business
models with lower service costs.
• Its customer: Improved equipmentuptime and guidance for
optimizedmaintenanceschedules.
• Improved transparencyfor machine availabilityand usage pattern.
• Remote monitoring and analysis of remote equipment
• Optimizedspare parts exchange timelinesbased on
maintenancecosts and costs due to materialdeterioration
causing lower productionthroughputs
• Classificationand patternrecognition
basedon historic sensor data
and error codes

22. © 2017 SAP SE. All rights reserved.
Customer Example
Train Operator
Company
The company owns and operatesa fleet of around 2.000
electro-trains,2.000 locomotivesand 30.000 coaches
and wagons.
Situation:40% of maintenanceeffort is for corrective
maintenance.
Solution
• Data fusion betweenIT and OT data
• Multidimensionalassets description
• Remote train diagnostics
• Engineeringrules and predictivemodels
• Indicators-basedplanning
• Dynamic optimizationof maintenanceschedules
Process Innovation
Benefits
• Higher asset availability leading to higher passenger
satisfaction
• Less effort for corrective maintenance

23. © 2017 SAP SE. All rights reserved.
SAP Predictive
Maintenanceand
Services
Thank you.