Alan Southall, SVP of Engineering, Head of IoT Predictive Maintenance, SAP

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SAP Predictive Maintenance & ServicesAlan Southall, SVP Engineering, Head of PdMS | SAP IoT | March 2017


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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


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


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


SAP Leonardo Portfolio


Agenda





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



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



Fleet Level View Single Asset Details


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


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


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


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



Failure Predictionusing automatic ensemblelearning on known failures

New AlgorithmUsing extensibility

Anomaly Detectionusing unsupervised learning

without labeled data


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


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


Agenda





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


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


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


SAP PredictiveMaintenance and

Services

Thank you.

Alan Southall, SVP of Engineering, Head of IoT Predictive Maintenance, SAP

Business

Transcript of Alan Southall, SVP of Engineering, Head of IoT Predictive Maintenance, SAP