SAP Predictive Maintenance and Service

Alessandra Vargas & Carolina CostaSAP Custom Development & Strategic Projects

Trends

1.3 billionpeople on

business & socialnetworks today

50 billionconnected devicesand “internet ofthings” by 2020

Network Effect/Explosion in Structured and Unstructured data

5 billionpeople worldwide

will becomemiddle class

50%of the world’s

populationwill live under

water shortage

Rising Customer Expectations Pressure on Resources

75%of global workforce

will beMillennials

A Dramatically Changing Workforce

Challenges

While the world's GDP is around $ 46.6 trillion, the valueof all assets is assumed to be much higher. Maintenancespend alone comes up to $ 447 billion.

60

200

Annualmaintenancecosts

Loss due toineffectivemaintenance

USMaintenance Costs 20112

in billion USD

70

300450

Loss due toineffectivemaintenance

Annualmaintenancecosts

Maintenancecostsaddressable bymaintenanceimprovements

EuropeMaintenance Costs 20101

in billion EUR

Valuable Insight Lost in Disparate Information Flows& Disconnected Databases

Business & ProductData Technical & Operational

Data

Unstructured Data

OEM DealerCustomer

Common Objective Maximize Equipment UptimeDisconnected Databases Valuable Insights Lost

Solution

Predict Act

devices connectedby 2020*

50 billion

price of sensors,microprocessors &wireless technologiestoday vs. 4 years ago**

1/5

40-50%CAGR for M2Mmarket until 2020*

Sensor Data

Business Data

EnvironmentalData

PatternandRoot

CauseAnalysis P

redi

ctio

ns

• Create work order

• Change productspecs

• Alter maintenanceschedule

• Preposition spareparts

• …

Sense

Predictive Maintenance: a key building block forimproved asset performance

Benchmarking

Prioritizemaintenance andservice activities

Early warningsto prevent

downtimes

Designimprovements

Machine healthmonitoring

Optimized warrantyand spare parts

mgmt

Business &Product Data

Technical &Operational Data

UnstructuredData

Single Source of Truth with business processoptimized in real time

SAP HANA

Predictive Maintenance and Service:Business Integration

Machine HealthPrediction

Increaseeffectiveness

Create Maintenanceor Service Order

Execute Orderon mobile device Visual SupportIncrease

efficiencySchedule Order

Fault PatternRecognition

IT / OTConnectivity

Condition MonitoringRemote Service

Time, effort or cost is wellused for the intended taskor purpose

Effectiveness is thecapability of producinga desired result

PredictiveMaintenanceand Service

Business Data

Equipment Data

SalesServiceEngineeringAsset Management

Recordings

Weather

Unstructured Data

Temperature

Voltage

Data

Service Coordinator

Account Manager

R&D Engineer

Asset Manager

WarrantyManager

People

Mobile App

Web App

UI

UX

Data Models

createactionable

insights

predicthealth

monitorasset

Prediction Notification

FlexibleModeling

Visual RulesDesigner

PredictiveAnalysis

UnifiedData Model

PredictionEngine

NotificationServer

Analysis &Exploration

Solution

Business Data Import

TelemetryData Import

Predictive Maintenance and Service:Key elements

Examples

Based on claims, damage reports, business and configuration dataCombining

VisualizationMapping of expert knowledge into HANAStatistical analysis

Improved product quality at lower costsQuick identification of new defect patternsGuidance for root cause analysisReduction of manual work in quality managementCost reduction & increased customer satisfaction

PdMS Sample Scenario: Defect Pattern Identification

Identification and prioritization of machinefailure pattern for product improvement

Industrial Machinery& Construction

Using machine dataPredict breakdownsCalculate energy consumption pattern profilesModel domain expert knowledge in SAP HANAProvide 360° view on machines

Scale and improve service business byBetter transparency of current conditionMachine health prediction from historic dataOptimize field technicians scheduling, including

failure predictions

PdMS Sample Scenario: Machine Health Prediction

Industrial Machinery& Construction

Sense & predict machine health supportingthe solution provider transformation

Machine A

Single SAP HANA based platform that combines sensorand warranty data

Analysis of equipment's telematics dataDetection of potential issues and relation to equipment's service

and warranty data using text mining, association analysis and HANAdatabase capabilitiesShorter detection-to-correction cycle.

Reduced warranty costsQuick identification of potentially defective behavior of fleetCreation of evidence packages as input to root causeanalysisMachine warranty cost reduction & improved up-time

PdMS Sample Scenario: Emerging Issues

Faster product improvement and earlyfailure prediction to reduce downtime

Industrial Machinery& Construction

Predictive modelingPredict unscheduled maintenance based on engine health data patternsDetect outliers and anomalies in the data with machinelearningUse text analysis to classify scheduled vs. unscheduledmaintenance events

Higher margins throughAlternative maintenance schedules which avoidunplanned downtime,Increased aircraft availability and maintenance revenueswhile reducing costs of service

PdMS Sample Scenario: Systems Trending andAlert Management

Predicting unscheduled maintenance eventsbased on historical data

Aerospace

MovingForward

Our individualized innovations are designed to enable acompetitive edge for you

MobileCustom

ApplicationsUser

Experience

Enabled in the Cloud

We Build Solutions We Productize Solutions We Offer Support

SAP Custom Development & Strategic Projects

Built on the SAP HANA Platform Protected with Support

Predictive Maintenance and Service,technical foundation: Building Blocks

HANA DB

OT Data Model IT Data Model

Unified View (Data Fusion)

Analysis View

PdMS foundation Configuration UI

PdMS foundation

XS Engine

The Analysis View offers datain the format required forPredictive Analysis

This is used to configure orsetup PdMS Foundation percustomer implementation

The Unified View isthe standard base

interface forapplications as well as

predictive analysis

Storage for IT data or business datafrom ERP/CRM systems

Storage for OT data or sensor data frommachines. Includes meta-data requiredto interpret measurements and alerts .

SAP HANA

HANA DB

XS EnginePdMS foundation- Configuration UILumira Server Application

Services

PdMS foundation

Browser

Sybase ESP(streaming

data)

Data Services(bulk data) Hadoop / IQ

BusinessSystem

(non-HANA)

BusinessSystem on

HANA

Lumira Desktop

Replicate

Smart DataAccessData

Movement

PdMS Aaapplication

(UI5 WebApp)

LumiraStoryboards ….

Applications

Predictive Maintenance and Service,technical foundation: Big Picture

Customer Engagement Model for PdMS

CustomerCall

One DayWorkshop

Custom SpecificImplementation

AssessmentService

Going Live

Run Faster Run Better Run moreInnovative

For everything that makes your company unique

Running SAP HANA with SAP Custom Development

All can be addressed with CD on HANA

Summary

ImprovedQuality

BetterVisibility

ReducedCosts

HigherUptime

IncreasedReliability

OptimizedPlanning

CustomerRetention

LowerInventory

SAP Custom Development is the PredictiveMaintenance partner to transform SAP HANAtechnology into tangible business value for your

customers

Your Key Takeaways

Thank you

Alessandra VargasBusiness Development ManagerSAP Custom Development & StrategicProjectsSAP Brasil

M +55 51 8153-3443E alessandra.vargas@sap.com

Carolina CostaSales SpecialistSAP Custom Development &Strategic ProjectsSAP Brasil

M +55 11 9 8674-3135E carolina.costa@sap.com

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Data Sources:• Gartner – “Top 10 Tech Trends for 2013” – 2012

• Economist Intelligence Unit – ”The Rise of the Machines” – 2012

• Global Framework on Maintenance and Asset Management

• ConMoto, Wertorientierte Instandhaltung (2011)

• ARC Advisory Group, Predictive Maintenance Survey

Appendix