Post on 09-Apr-2017
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SAP Predictive Maintenance & ServicesAlan Southall, SVP Engineering, Head of PdMS | SAP IoT | March 2017
© 2017 SAP SE. All rights reserved.
DisclaimerThis presentation outlines our general product direction and should not be relied on inmaking a purchase decision. This presentation is not subject to your license agreementor any other agreement with SAP. SAP has no obligation to pursue any course ofbusiness outlined in this presentation or to develop or release any functionalitymentioned in this presentation. This presentation and SAP's strategy and possiblefuture developments are subject to change and may be changed by SAP at any time forany reason without notice. This document is provided without a warranty of any kind,either express or implied, including but not limited to, the implied warranties ofmerchantability, fitness for a particular purpose, or non-infringement. SAP assumes noresponsibility for errors or omissions in this document, except if such damages werecaused by SAP intentionally or grossly negligent.
© 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of ThingsConnecting Things to Business Processes
Predictive Maintenance & ServicesCombining IT/OT data to optimize maintenance
Customer StoriesReal life applications of predictive maintenance
© 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of ThingsConnecting Things to Business Processes
Predictive Maintenance & ServicesCombining IT/OT data to optimize maintenance
Customer StoriesReal life applications of predictive maintenance
© 2017 SAP SE. All rights reserved.
Delivering Outstanding Results to Customers and Stakeholders
Customers
87%of ForbesGlobal 2000
98%of the 100most valuedbrands
Financials
€14.87B(+6%) softwareand software-related servicesrevenue
€5.5 B(+4%)software andcloud revenue
100%of Forbes topsustainabilitycompanies
80%+are SMEcompanies
€17.5 B(+4%)Total revenue
Solutions
25Industries
11Lines ofbusiness
Employees
74,406employeesEMEA: 33,340Americas: 22,071APJ: 18,995
79%EmployeeEngagementIndex
SAP HANA5,800 SAP HANA customers1,800 startups8,500 trained partners
120+nationalitiesworldwide
70%Business HealthCulture Index
Source:SAP Corporate Fact Sheet 1/2015; SAP Integrated Report 3/2015
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SAP Leonardo empowers the LIVE businessConnecting Things to Business Processes
BusinessProcesses
SAP Leonardo FoundationSAP Cloud Platform
SAP LeonardoApplications
Things
Next Level of Experience
Sources Of DataIntegration | Business Partners | Networks
Machine LearningBlockchain
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SAP Leonardo Portfolio
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Agenda
SAP & Internet of ThingsConnecting Things to Business Processes
Predictive Maintenance & ServicesCombining IT/OT data to optimize maintenance
Customer StoriesReal life applications of predictive maintenance
© 2017 SAP SE. All rights reserved.
Connected Asset LifecycleSAP IoT and PdMS delivers tangible business outcome
Failu
rera
te
Burn-in"infant mortality" Wear-outNormal life
Asset lifetime
Emerging Issues Detection(EID)
Early identify, monitoring andmanagement of emerging asset issues
using exploration, root cause andwarranty analytics
Predictive Maintenance and Service(AHCC & VA)
Holistic management of asset health and dynamicoptimization of maintenance schedules and resources
based on health scores, anomaly detection and spectralanalysis
Asset Investment Optimizationand Simulation
Analyze remaining useful life of assets tooptimally plan for new investments based on
business needs, asset health and risk offailure.
SAP ERP, S4HANA, CRM, C4C
© 2017 SAP SE. All rights reserved.
Connected Asset LifecycleSAP IoT and PdMS delivers tangible business outcome
Failu
rera
te
Burn-in"infant mortality" Wear-outNormal life
Asset lifetime
Emerging Issues Detection(EID)
Early identify, monitoring andmanagement of emerging asset issues
using exploration, root cause andwarranty analytics
Asset Investment Optimizationand Simulation
Analyze remaining useful life of assets tooptimally plan for new investments based on
business needs, asset health and risk offailure.
SAP ERP, S4HANA, CRM, C4C
Predictive Maintenance and Service(AHCC & VA)
Holistic management of asset health and dynamicoptimization of maintenance schedules and resources
based on health scores, anomaly detection and spectralanalysis
© 2017 SAP SE. All rights reserved.
Connected Asset LifecycleSAP IoT and PdMS delivers tangible business outcome
Fleet Level View Single Asset Details
© 2017 SAP SE. All rights reserved.
Business Value for SAP PdMSOverall cost reduction in maintenance efforts
~1% machines with down time
Convert unplannedmaintenance to planned
maintenance to avoid down-time and improved equipment
effectiveness
Num
bero
fAss
ets
Health of Asset / Maintenance Need
© 2017 SAP SE. All rights reserved.
Business Value for SAP PdMSOverall cost reduction in maintenance efforts
Health of Asset / Maintenance Need
Dynamically optimize theentire maintenance schedule in
order to reduce the overallmaintenance costs and
reduce components on stock
Num
bero
fAss
ets
Standard maintenance intervalfor all assets the same
Optimized maintenance intervalper 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
© 2017 SAP SE. All rights reserved.
Edge, Connectivityand Storage
Key ChallengesRare Events | Data Quality and Varsity | Data Variety, Fusion and Volume
v Dynamically optimizemaintenance and serviceactivities with prescriptiveanalytics
v Integration into EAM, PM,MRS and AIN
v Condition Monitoring
v Onboardingv Device managementv Securityv Connectivityv Data ingestionv Big Data infrastructure
v Avoid unplanned down-timev Improved equipment
effectivenessv Reduce overall maintenance
costsv Reduce components on stock
v Data Preparation
v Support data fusion process,i.e. sensor data combined withbusiness information
v Operationalized data fusionservices
v KFR Enginev Machine Learning Engine
v Anomaly Detection
v Ensemble Learning
v Model Base Enginev FEA Engine
IT/OT Convergence PdMS DerivedSignal Management
PdMS AHCC, DMM& Integration
PdMS BusinessOutcome
© 2017 SAP SE. All rights reserved.
Edge, Connectivityand Storage
Key ChallengesRare Events | Data Quality and Varsity | Data Variety, Fusion and Volume
v Dynamically optimizemaintenance and serviceactivities with prescriptiveanalytics
v Integration into EAM, PM,MRS and AIN
v Condition Monitoring
v Onboardingv Device managementv Securityv Connectivityv Data ingestionv Big Data infrastructure
v Avoid unplanned down-timev Improved equipment
effectivenessv Reduce overall maintenance
costsv Reduce components on stock
v Data Preparation
v Support data fusion process,i.e. sensor data combined withbusiness information
v Operationalized data fusionservices
v KFR Enginev Machine Learning Engine
v Anomaly Detection
v Ensemble Learning
v Model Base Enginev FEA Engine
IT/OT Convergence PdMS DerivedSignal Management
PdMS AHCC, DMM& Integration
PdMS BusinessOutcome
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PDMS Machine Learning Engine OverviewUsable for any asset type and manufacturer
Data
Data Preparation, Fusionand Feature Selection
Reinforcementusing 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 Predictionusing automatic ensemblelearning on known failures
New AlgorithmUsing extensibility
Anomaly Detectionusing unsupervised learning
without labeled data
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General Approach
• Learn the normal behavior
• Principal Component Analysis (PCA) rotates the coordinate system to explain amajor 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 adjustedsum 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 DetectionWith Principal Component Analysis
PCA
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Anomaly DetectionWith Distance Based Failure
Rank Battery1 1282 3483 1334 1445 0086 1817 3668 0519 336
10 536…
371 103372 135373 281374 463375 096376 109377 086
The algorithm is trained to inspect the data for you
© 2017 SAP SE. All rights reserved.
Agenda
SAP & Internet of ThingsConnecting Things to Business Processes
Predictive Maintenance & ServicesCombining IT/OT data to optimize maintenance
Customer StoriesReal life applications of predictive maintenance
© 2017 SAP SE. All rights reserved.
Customer ExampleCompressor Manufacturer
Company
One of the largest providers of compressed air systemsand compressed air consulting services.
Situation: Changed the business model from sellingcompressors to selling compressed air
Solution
• Compressors equipped with sensors• SAP Predictive Maintenance and Service solution• SAP HANA software• SAP CRM application for use in service on SAP HANA
IT and OTconnectivity
Asset health controlcenter
Fault patternrecognition
Machine healthprediction
Create maintenanceor service order
Execute orderon mobile device
%
0 01 100 11 10 100 1
Visual supportSchedule orderO
rderStatus
Non-SAPapplications
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
© 2017 SAP SE. All rights reserved.
Customer ExampleGEA Separators
Company
GEA is one of the largest suppliers of process technologyfor the food industry and for a wide range of otherindustries. In 2015, GEA generated consolidated revenuesin excess of about EUR 4.6 billion.
Situation: Need for IoT solution in order to extend servicebusiness and as differentiator to their competition.
Solution
• SAP Predictive Maintenance and Service solution• SAP Predictive Analytics software• SAP CRM
Process Innovation
Benefits
• Company: Ability to offer new higher margin service businessmodels with lower service costs.
• Its customer: Improved equipment uptime and guidance foroptimized maintenance schedules.
• Improved transparency for machine availability and usage pattern.
• Remote monitoring and analysis of remote equipment• Optimized spare parts exchange timelines based on
maintenance costs and costs due to material deteriorationcausing lower production throughputs
• Classification and pattern recognitionbased on historic sensor dataand error codes
© 2017 SAP SE. All rights reserved.
Customer ExampleTrain Operator
Company
The company owns and operates a fleet of around 2.000electro-trains, 2.000 locomotives and 30.000 coachesand wagons.
Situation: 40% of maintenance effort is for correctivemaintenance.
Solution
• Data fusion between IT and OT data• Multidimensional assets description• Remote train diagnostics• Engineering rules and predictive models• Indicators-based planning• Dynamic optimization of maintenance schedules
Process Innovation
Benefits
• Higher asset availability leading to higher passengersatisfaction
• Less effort for corrective maintenance
© 2017 SAP SE. All rights reserved.
SAP PredictiveMaintenance and
Services
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