SAP Predictive Maintenance and Service · Collaborative asset management bringing key stakeholders (operator, OEM, service providers, …) together in a digital ecosystem solving

PUBLIC

June 8th, 2018

SAP Digital Connected AssetsSAP Predictive Maintenance and Service

2© 2018 SAP SE or an SAP affiliate company. All rights reserved.

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▪ The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP´s willful misconduct or gross negligence.

▪ All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.


Standalone and isolated assets

Untrusted & disparate asset information

Limited analytical capabilities

Reactive maintenance

Disconnected systems and lifecycle

Selling equipment

Traditional budget based maintenance

planning

Paper-based work instructions

Optimized for physical structure

Connected and smart digital twins

Collaborative single source of truth

Real time analytics with simulation

Prescriptive maintenance

Closed loop product and asset lifecycle

Pay-per-use / Equipment-as-a-Service

Cost / Risk / Performance based maintenance

strategy

Interactive work instructions with 3D visualizations

Mechatronics / Software in products/assets

NowYesterday

Pay per use

Asset Management TrendsTransformation to digital connected assets

Digital Twins and 3D

Visualization


SAP Vision for Asset Management

Collaborative

Scalable

Real Time

Connected

Cloud Platform

BasedDigital Disruption

Adaptable

Real Time Insights

Optimization via

Prediction

Next gen tech ML,

Block chain, 3D printing

Mobile First / Fiori UX

Unified data and processes with

PLM, Manufacturing and Service

Share & Collaborate

natively

Base for Industry

Extensions

Partner ecosystem

Opportunity

ElectronicsSoftware Modular Services

Full Digital Representation of Assets along their Lifecycle delivering an embedded,

collaborative and real-time set of Next Generation Processes and Systems


SAP Leonardo IoT

for Asset Management

Digital Core: System of

Record

Digital Innovation: System of

Innovation

SAP Leonardo IoT for Asset ManagementEnabling intelligent insights

Asset

Strategy &

Performance

Asset

Intelligence

Network

Predictive

Maintenance

& Service

Predictive

Engineering

Insights

S/4HANA

&

ECC

Maintenance

Management

An architecture built for next generation

Enterprise Asset Management

Digital Core

Corrective, emergency and preventive maintenance planning &

execution via notification and order processing in an integrated

system

Digital Innovation

• Asset Central – Provides a re-usable asset registry across IoT

applications for seamless integration and data consistency

• SAP Asset Intelligence Network

Collaborative asset management bringing key stakeholders

(operator, OEM, service providers, …) together in a digital

ecosystem solving complex execution, predictive and planning

activities with centrally managed asset information

• SAP Predictive Maintenance and Service

Enables enhanced predictive maintenance techniques to

optimize EAM business processes for greater asset availability

and reduced cost

• SAP Asset Strategy and Performance Management

Define and plan maintenance execution strategies holistically

(insight/foresight; insights from network) for improved

performance

• SAP Predictive Engineering Insights

Model and visualize the physical structure of an asset for real-

time calculation of stress and fatigue to drive predictions

Integration

Asset

Central

SAP Predictive Maintenance and Service



Preventive

(static)

Run-to-Failure

Preventive

(static)

Predictive/

On-Condition

(agile)

Run-to-Failure

OP

EX

Step 1today Step 2

future

Few Data

Big Data

Few Data

The Internet of Things is leading to

increased use of on-condition and

predictive maintenance strategies

Although still relevant, reactive

and preventive maintenance

strategies do little to guard against

unplanned equipment downtime

and result in high cost

The goal is to

increase our use of

more advanced

maintenance strategies


Multiple Approaches to Predictive MaintenanceData science 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

Data Science Driven

Oil Analysis

X-ray Radiography

P Potential Failure

Why now?▪ 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 and to shift

unplanned to planned events


Company

Owns and operates a

fleet of around

2,000 electro-trains,

2,000 locomotives

and 30,000 coaches

and wagons

Customer ExampleTrain Operator

9CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ

40% of maintenance is currently reactive

The maintenance strategy proportions are for illustration purposes only and not reflective of actual customer percentages

Run to Failure Preventative Predictive*

*


Solution

Customer ExampleTrain Operator

10CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ

• 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


IT/OT* convergence

Big Data ingestion

Big Data infrastructure

Merging sensor data

with business

information

Maintenance activities

Prioritized maintenance

and service activities

Optimized warranty

and spare parts

management

Prescriptive

maintenance

Quality improvements

Data analysis

Root-cause analysis

Asset health

monitoring

Machine learning

Anomaly detection

Failure Prediction

Triggering of

corrective actions

Connected assets

Onboarding

Connectivity

Device management

Security

Business value

Customer experience

Increased quality

Lower costs

Operational efficiency

R&D effectiveness

Material procurement

Sensor Data Insight Action Outcome

* Operational technology

SAP Predictive Maintenance and Service solutionFrom sensor to outcome


Service

Service Provider

Sales

Increase

customer

satisfaction

and loyalty

Dealer

Deliver the

value added

service at the

right price

Fleet

Owner/Operator

Decrease

maintenance

costs

Operator

Increase

asset up-time

R&D

Improve

asset

reliability

and up-time

Monitor

quality of

purchased

components

Improve

manufacturing

processes

Comply

with contract

service level

agreements

AftermarketProcurement Production

OEM

SAP Predictive Maintenance and ServiceDecision support across the ecosystem & asset lifecycle

DESIGN

BUILDSUPPORT

PURCHASE

OPERATE &

MAINTAINDISPOSE

Decision support to ALERT, DISCOVER AND REMEDY

Business DataMachine Data

Combining IT & OT data gives machine data context


SAP Predictive Maintenance and Service, on premise editionSolution components and value drivers


SAP Leonardo Foundation

SAP Leonardo for Edge Computing

Business User

Domain Expert

Data Scientist

Data ManagerSAP Leonardo IoT Foundation

SAP Leonardo IoT Edge

Machine Learning Engine

Analysis Tools Catalog


Explorer Equipment

Page

Logistics & Maintenance

Execution SystemsActions

Insights

Alerts

Raw

Data

Enables a data science driven

approach to condition monitoring

Flexible extension concept for

customers to build industry or

customer specific models and

analytics

A scalable Machine Learning

Engine that drives data science

insights into our business

processes

Flexible visualizations across

equipment structures

End-to-end process integration…

Alert, Discover, Remedy


SAP Predictive Maintenance and Service, on premise editionSystem and component level visualizations

Machine Learning Engine



Explorer (fleet view)

Explorer Equipment

Page





Logistics & Maintenance

Execution Systems


Equipment Page


SAP Predictive Maintenance and Service, on premise edition

Fiori Launchpad – ApplicationsProcess AppsExplorer

Performance Improvement

Obsolescence Management

...

Master Data AppsModels

Equipment

Locations

Spare Parts

Documents

Instructions

…

Machine Learning Engine AppsAlgorithms

Model Management

Admin AppsTemplates

Application Settings

...


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

Health Status Overview

Health Status Overview


SAP Predictive Maintenance and Service, on premise editionExplorer


Explorer

Explorer

(Fleet View)




Key Figures Analysis ToolAnalysis Tools Catalog

Explorer

Explorer

(Fleet View)

Analysis Tools Catalog Analysis Tool



Equipment List Analysis ToolAnalysis Tools Catalog

Explorer

Explorer

(Fleet View)





Explorer

Equipment Scores Analysis Tool

Explorer

(Fleet View)




Map Analysis ToolAnalysis Tools Catalog

Explorer

Explorer

(Fleet View)




Custom Analysis ToolAnalysis Tools Catalog

*”Health Status Overview” is an example of a custom Analysis Tool built using SDK

Explorer

Explorer

(Fleet View)



Dissolved Gas Analysis using Duval Triangle/Pentagon as well as IEC Basic Gas Ratios methods

Utility-specific Insight Provider:

Oil Quality Analysis


Trend charts

Utility-specific Insight Provider:

Oil Quality Analysis


What is Machine Learning?Traditional Rule-Based Approach vs. Machine Learning


▪ Maintenance

and Ops data

▪ Telemetry data,

System faults

Machine Learning basics Process

Data

sources

▪ Technical

publications

▪ Design data

Prepare

input data

▪ Exploration

▪ Selection &

Transformation

▪ Cleaning &

Integration

Apply Machine

Learning ProcessOutput


Machine Learning Engine Analysis Tools

Train

Model

Configure

Model

Score

model

Feedback

Remaining

Useful LifeAnomaly

ScoreHealth

Status

2530 days


Machine Learning basics

Supervised Learning –

Failure Prediction

Unsupervised Learning -

Anomaly Detection

Input and output

variables (failures)

Algorithm learns

mapping function

from input variables

to output variable

Predictions made

when correlations

are found

between input

data and historic

failures

Trigger anomaly

alert when the

algorithm detects

an abnormal

pattern

Only input variables…

no output variable

Algorithm

learns normal

patterns from

input variables

Date Time Pressure Temperature Amperage RPM Failure event

Input Variables Target Variable

16-Apr 1:21 1003 154 220 1500 NO

16-Apr 1:22 1003 154 220 1500 NO

16-Apr 1:23 1003 154 255 1500 YES

Predicted failure

Date Time Pressure Temperature Amperage RPM

Input Variables Target Variable

17-Apr 1:21 1003 154 220 1500

17-Apr 1:22 1003 154 220 1500

17-Apr 1:23 1003 214 220 1500

Anomaly alert


SAP Predictive Maintenance and ServiceMachine Learning Engine

Apply Machine

Learning ProcessOutput

Machine Learning Engine Analysis Tools

Train

Model

Configure

Model

Score

model

Feedback

Remaining

Useful LifeAnomaly

ScoreHealth

Status

2530 days


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*


SAP Predictive Maintenance and Service OPEMachine 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

Model

Management

Thank you.

Stephan Koenig

Product Management

SAP Digital Connected Assets,

Predictive Maintenance and Service

SAP SE

Dietmar-Hopp-Allee 16

69190 Walldorf

Phone: +49 6227 7 - 67939

Mail: [email protected]

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The information contained herein may be changed without prior notice. Some software products marketed by SAP SE and its distributors contain proprietary software components

of other software vendors. National product specifications may vary.

These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP or its affiliated

companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP or SAP affiliate company products and services are those that are

set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.

In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release

any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products,

and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The

information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various

risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements,

and they should not be relied upon in making purchasing decisions.

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company)

in Germany and other countries. All other product and service names mentioned are the trademarks of their respective companies.

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© 2018 SAP SE or an SAP affiliate company. All rights reserved.

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