SAP Enterprise Asset Management Solution Overview and ... · PDF fileSAP Integrated Business Planning SAP Asset Strategy and Performance Management (planned) End-to-end process enablement

PUBLIC

Dr.-Ing. Achim Krüger, Vice President, Line of Business Asset Management, SAP SE

July 12, 2017

SAP Enterprise Asset ManagementSolution Overview and Strategy in a Nutshell

2© 2017 SAP Leonardo Live. All rights reserved. I PUBLIC

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.

Legal disclaimer


Technology

enablers

Business

challenges

SAP Enterprise Asset Management (SAP EAM) solutionTrends in asset management

Internet of Things (IoT)

to scale connectivity

Big Data for getting

insight from IT and OT

Analytics for prediction

and simulation

Machine learning

to improve

business decisions

Enterprise mobility

to empower employees

Cloud for collaboration

ISO 55001, ISO 14001,

ISO 45001, and the like

Optimizing cost, risk,

and performance

Balancing OPEX with

CAPEX

Meeting stakeholder

expectations

Empowering

practitioners

Facilitating

collaboration among

EPCs, OEMs,

service providers,

and operators


Connecting all stakeholders

alongside the asset lifecycle

Connecting corporate objectives

with the asset system

SAP Enterprise Asset ManagementAsset management in an ever-more-connected world

Business needs

analysisPlan and design

Acquisition and

commission

Operation and

maintenance

Configuration and

change management

Decommission

and disposalAsset

Cost

RiskPerformance

Manufacturer

Supplier

EPC

Service provider

Owner or operator

Dealer

IT OTConnecting IT with OT

55001

www.sap.com/eam

http://www.sap.com/eam

https://www.sap.com/documents/2016/12/0421b662-9f7c-0010-82c7-eda71af511fa.html


SAP Enterprise Asset ManagementSupporting asset management processes from end to end

Portfolio and project

management

Portfolio

management

Project connectivity

Project

management

Resource management

Idea management

Asset operations

and maintenance

Maintenance planning and

scheduling

Maintenance

execution

Mobile asset

management

Asset strategy

and performance

Environment, health,

and safety

Health and safety

management

Maintenance safety

and permit to work

Environment

management

Management

of change

Incident management

Asset

network

Asset information

governance

Predictive maintenance

and service

Asset information

collaboration


Systems of

record

Bimodal IT according to GartnerSystems of record versus systems of innovation

Source: Gartner

Systems of

innovation

Systems of

differentiation

Mode 1

Mode 2

Go

ve

rna

nc

e

+

–

Ch

an

ge

+

–

SAP Integrated

Business

Planning

SAP Leonardo


Envisioned logical architecture for SAP Enterprise Asset ManagementStrategic direction

Business intelligence

Mode 1 Mode 2

Data foundation

SAP Ariba

solutions

SAP SuccessFactors

solutions

SAP Fieldglass

solutions

SAP Digital Boardroom

Differentiate Business

Machines Sensors OT Systems Edge

Intelligently

connect people,

things, and

businesses

Differentiate

businessesHyperautomate business

processes

Run businesses

Deliver insights to drive

strategic decisions

Steer businesses

Embrace data

Orchestrate data of any

volume, velocity, and variety

Asset

Intelligence

Network

Predictive

Maintenance

SAP Asset Strategy

and Performance

Management

Mobile…

STO

Internet of Things

SAP Leonardo

IoT Foundation

Master data

EH&SPM

FI

CO

MM PLM …

PS MRS

SAP Asset

Intelligence

Network

SAP Predictive

Maintenance and

Service

Mobile

Digital core

SAP Integrated

Business Planning

SAP Leonardo


SAP Predictive Maintenance and Service From sensor to insight to outcome

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

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


SAP Predictive Maintenance and ServiceHow can information systems help?

Connected

assets and sensors

OT

systems

(such as SCADA)

Other

data sources

(such as weather)

Digital core

(SAP S/4HANA)

SAP Leonardo IoT

capabilities

Engineering

rules

Life

indicators

Distance

scoring

Anomaly

detection…

Agile planningRemote monitoring Alerts and actions


Availability through the cloud or on

premise

Flexible extension concept to

build industry- or customer-specific

models and analytics

Scalable machine learning engine

that drives data science insights

into our business processes

Flexible visualizations across

equipment structures

Comprehensive process

integration: alert, discover,

remedy

SAP Predictive Maintenance and ServiceSolution components and value drivers

Business dataMachine data

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

Insight provider catalog

SAP Predictive Maintenance and Service

Asset health

control center

Asset health fact

sheet

Logistics and maintenance

execution systemsActions

Insights

Alerts

Raw

data


Condition data allows for

a ranking of assets

according to a health score.

For “healthier” assets,

the service interval

can be prolonged, while it can be

shortened for others.

This results in fewer failures

and lower maintenance costs.

SAP Asset Strategy and Performance Management Adding more efficiency by looking at the fleet

Preventive

(static)

Run-to-failure

Preventive

(static)

Preventive

(agile)

Run-to-failure

Cost


Benefits

Increase overall asset availability

Increase MTBF – increase equipment reliability

Improve utilization of assets

Control maintenance spend

Reduce work backlog

Identify savings opportunities through preventive and

predictive maintenance

Reduce capital tied up in spare parts inventory

Adopt a proactive and targeted maintenance strategy

Change the sequence of the process using point apps

Solution integration points

SAP Asset Intelligence Network

SAP Predictive Maintenance and Service

SAP ERP application or SAP S/4HANA (PM, MM, FI/CO,

PP functionalities)

SAP Integrated Business Planning

SAP Asset Strategy and Performance Management (planned)End-to-end process enablement

Process innovation

Classification of assets

into groups

Asset criticality and risk assessment

Collaborative failure

modes libraryPerform FMEA

Analyze systems and

functionsPerform RCM

Analyze and optimize

maintenance strategy

CAPEX / OPEX inputs

to maintenance

strategy

Bad actor

management and

spares optimization

Integrations and

extension points to 3rd-

party systems

Perform RCA, ETA

Project manager

review

This is the current state of planning and may be changed by SAP at any time.

MTBF – mean time between failures; FMEA – failure mode and effects analysis; RCM – reliability-centered maintenance; RCA – root cause analysis


What does digital transformation mean for enterprise asset management?

Connect to the asset

Bring together information from operational and business systems (IT/OT convergence)

Utilize the IoT for scaling transparency without neglecting existing information sources

Predict asset system behavior

Avoid unplanned downtime and major operational consequences through simulation and prediction

Discover patterns of failure and preserve operational integrity

Blend business IT information with operational (OT) data

Share asset information and collaborate

Activate the ecosystem of OEMs, EPCs, service providers, and operators

Make sure there is one version of truth on asset master data

Use a business network to enable integrated processes in the cloud

Digital transformation in asset management driven by IoT, cloud, and

business networks


SAP solutions for the asset management line of businessWhere to find more information about our road map and innovation

Road maps: Value maps:

http://www.sap.com/roadmaps http://www.sap.com/solutionexplorer

Innovation discovery:

http://sapsupport.info/support-innovations/innovation-discovery/

http://www.sap.com/roadmaps

http://www.sap.com/solutionexplorer

http://sapsupport.info/support-innovations/innovation-discovery/

Dr. Achim Krüger

Vice President, Line of Business Asset Management

SAP SE

Dietmar-Hopp-Allee 16

69190 Walldorf, Germany

T +49 6227 7-60975

F +49 6227 78-38446

M +49 160 3603569

S +49 6227 7-49510

[email protected]

Thank you.

More information:

www.sap.com/eam

www.sap.com/roadmaps

www.sap.com/solutionexplorer

http://scn.sap.com/community/eam

mailto:[email protected]

http://www.sap.com/eam

http://www.sap.com/roadmaps

http://www.sap.com/solutionexplorer

http://scn.sap.com/community/eam

No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company.

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.

See http://global.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices.

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

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Building the Smart

Railroad

SAP Leonardo Live event

Jeroen De Roeck

10.176 Main Signals(31/12/2016)

5.905 km Overhead lines(31/12/2016)

6.511 km of Main Track(31/12/2016)

11.769 Civil Engineering Constructions (31/12/2016)

7000+ Employees(31/12/2016)

1.751 Level Crossings (31/12/2016)

THE BELGIAN RAIL INFRA MANAGER

Key Figures Asset Management

SAP objects>500.000 Functional locations>2.000.000 Pieces of equipment

Spare Parts 270 M€

“STUPID Assets” “SMART Assets”

NOT CONNECTED CONNECTED

19July 2017 Infrabel – Building the Smart Railroad

Sensors &

Intranet of Things

Sensors &

Measure Trains

“STUPID Assets” “SMART Assets”

NOT CONNECTED CONNECTED


Preventive

maintenance

plans

ASSET

DATA

Incidents

Preventive/Condition/Predictive-based

maintenance SAP

Technicians

& TABLETS

PLANNER

Digital Strategy


https://www.google.be/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwj4ypDcu7rUAhVCWxoKHV-BCHIQjRwIBw&url=https://www.infrabel.be/nl/over-ons/veiligheid/het-treinverkeer-regelen&psig=AFQjCNHVzwt3iCLuIo6pZLjH8U71DEMYtQ&ust=1497431102877566

Infrabel Movie


0,8

1

1,2

1,4

1,6

1,8

2

0,02 0,2

0,38

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0,74

0,92

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1,64

1,82 2

2,18

2,36

2,54

2,72

2,9

3,08

3,26

3,44

3,62

3,8

3,98

4,16

4,34

4,52

4,7

4,88

5,06

5,24

5,42

5,6

5,78

5,96

01/11/15 01:58:17.920

01/11/15 02:12:07.940

03/11/15 11:07:33.920

04/11/15 05:08:32.920

04/11/15 06:46:44.920

06/11/15 10:10:20.920

07/11/15 13:08:19.940

09/11/15 08:08:56.940

09/11/15 08:12:10.920

09/11/15 08:14:42.920

09/11/15 09:51:52.940

average

EBP

EBP logbook

PQube

Current Curves (1.000.000)

100 Turnouts

Turnout – motor

SAP Enterprise Asset

Management (SAP EAM)

First steps in IOT – the PQUBE case


• Turnouts are more or less a blackboxInformation currently available from distance

• Left or right

• In/out control

• Placing the Pqube sensor should increase the• Visibility on the functioning of a turnout

• Provide Data to examine behaviour 0,8

1

1,2

1,4

1,6

1,8

2

0,02 0,2

0,38

0,56

0,74

0,92

1,1

1,28

1,46

1,64

1,82 2

2,18

2,36

2,54

2,72

2,9

3,08

3,26

3,44

3,62

3,8

3,98

4,16

4,34

4,52

4,7

4,88

5,06

5,24

5,42

5,6

5,78

5,96

01/11/15 01:58:17.920

01/11/15 02:12:07.940

03/11/15 11:07:33.920

04/11/15 05:08:32.920

04/11/15 06:46:44.920

06/11/15 10:10:20.920

07/11/15 13:08:19.940

09/11/15 08:08:56.940

09/11/15 08:12:10.920

09/11/15 08:14:42.920

09/11/15 09:51:52.940

average

First steps in IOT – Goals with Pqube project


Going from individual measurements to current curves

• Plot measurement values in time

• Business value:

Current curves

A correct image of the physical functioning of a turnout

Data analytics – from measurement to data


• Each turnout has it own curve that evolves during time

• Each type of turnout has a typical curve

• It defines the ‘DNA’ of the turnout

Data analytics – What we learned


Determine algorithms to define ‘normal’ behaviour

Each unique turnout has it own characteristic evolution of the curve

Curve:

• Difference for left or right movements

• Variation in current due to changes in mechanical properties

0

1

2

3

4

5

6

7

8

9

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127

136

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181

190

199

208

217

226

235

244

253

262

271

280

289

298

24/03/15 10:53:34.920

average

Data analytics – Define Models


Determine algorithms to define ‘abnormal’ behaviour• Abnormal current consumption

• Longer runtimes

• Larger deviations = malfunction

• Specific patterns ie. Bad alignment worm wheel



Normalisation

• Flatten out differences in timestamp between different systems

• Only keep reliable measurements

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1

2

3

4

5

6

7

8

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300

21U R 01/01/15 10:19:22.920

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21U R 02/01/15 07:48:17.920

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8

1 35 69 103 137 171 205 239 273 307 341 375 409 443 477 511 545 579 613 647 681 715 749 783 817 851 885 919 953 987 1021

L2 Amp (A)

L2 Amp (A)

L2 Amp (A)

L3 Amp (A)

L3 Amp (A)

L2 Amp (A)

L2 Amp (A)

L1 Amp (A)

L3 Amp (A)

L2 Amp (A)



Building a predictive model

• Each movement gets a calculated number

Number Anomaly Category

0.9762 normal

7.84222 failing F26

0.96101 normal

12.7684 failed f30

12.5305 failed f30

12.4308 failed f40

11.8506 failing f30

10.0133 failing f30

6.42567 failing f30

0.71324 normal

6.07957 failing f26

0.71649 normal

0.74115 normal



• Normal curve = 1

• Early warning> 1,4

• Includes false positives

• Warning >= 2

• No false positives

• Failing >= 3,8

• Failed >= 11,9


Building a predictive model

• Each movement gets a calculated number


Data analytics – Introduce AI technology


Unsupervised learning

Dimensionality reduction using neural networks

Data analytics – Introduce AI technology

Normal data

Over Current

Not locking


• Increase installed base: install Pqube on critical turnouts.

• Avoid unnecessary activity in the field

• Better planning and preparation based on indications

from predictive model

• Reduction of unplanned downtime by early warning

• Number of false positives to be managed

• Prevent extended maintenance downtime due to

unforeseen activities

Pqube Project – Business value

Reduce cost of

operations

Reduce

unplanned

downtime


Pqube Project – Leverage IoT Business value

Cost

Risk

Starting from traditional Preventive Maintenance Strategy

• Low Risk

• High Cost

• Adapt preventive Maintenance strategy

• Cost of visits

• Risk

• Use Predictive model to lower the Risk

• Total cost of Predictive Model + Lower Maintenance

strategy < Traditional Preventive Maintenance

• New balance between cost and risk


2017 2018

Turnout

Measurement system

Turnout

Video Inspection system

TVS

Track

Video System

DB 500 Gb/year

Video 100 Tb/year

SGS

Switch

Geometry System

100 Tb/year

Next steps in making assets ‘smart’

Increasing the business valueLin

ear

assets

Sin

gu

lar

Assets


http://www.google.be/url?url=http://www.infrabel.be/fr/etcs&rct=j&frm=1&q=&esrc=s&sa=U&ei=TXdIVbq5MMr5UOPlgIgF&ved=0CB8Q9QEwBQ&sig2=BU-Q287v8FW2kfOONbdGIQ&usg=AFQjCNG2piJjL6PXdvViIbamA524iiYPew

http://www.google.be/url?url=http://www.infrabel.be/fr/etcs&rct=j&frm=1&q=&esrc=s&sa=U&ei=TXdIVbq5MMr5UOPlgIgF&ved=0CB8Q9QEwBQ&sig2=BU-Q287v8FW2kfOONbdGIQ&usg=AFQjCNG2piJjL6PXdvViIbamA524iiYPew

First steps in IoT – What we learned

Pqube case enabled us to:

• Learn and understand possibilities of IoT @ Infrabel

• In the beginning: a lot of manual work involved

• Unsupervised learning had the same result much faster

• Looking for additional IoT opportunities:

• Monitoring Control Unit

• Enhance ‘old’ equipment with nonintrusive new technology


July 2017

Next steps in IoT – Monitoring Control Unit


MCU:

• Remote measurement/monitoring of:

• Batteries

• Current rectifier

• Active alerts

• Configuration parameters managed

remotely

• Business Value:

• Fewer physical visits necessary.

• Visit takes 3,5 hours incl travel * 750 installations = 2625 hours saved

• Active control of parameters, limit human errors

• Early warning on battery issues

Next steps in IoT – Monitoring Control Unit


Next steps in IoT – LED’s tele-monitoring in RTF boxes

using Computer Vision


State-of-the-art research and development at Infrabel

Next steps in IoT – Asset classification with Computer

Vision


Higher

Data

Quality

Quicker

Response

Measurement

database

Reports

Measurement

Data

Incident

Intervention

Malfunction

Asset Master data

Verified measurement results

Predictive notifications

Asset owner (expert) + data scientistTechnician

SAP EAM

Assets checked by

Measurement trains

Assets to be maintained

Asset Management information flow

Model

Live

data

Less

manual

work


How to digest all the data

Use of flexible reporting in

SAP Lumira software

• Meaningful insight on

combined data

• KPI models based on

previous data analysis

experience

• Tree maps and

geographical

visualisation to focus on

the important stuff



SAP EAM with GEOe

support

• Visual way of working

• Quickly identifying

assets that need

maintenance

• Real time follow-up



SAP EAM and Expert

systems

• Visualising linked assets

• Connecting measurement

information

• Display information on

previous measurements

• All information available

from the back-end system



Mobile SAPUI5 apps

• Consult measurement

history

• Perform checklists

• Access location-based

observations by

maintenance crews


Separate engineering and production system

• Production used to plan and predict

• Engineering to develop predictive models

and rules

Proven models to be displayed in production

system

Only hot data in Production

Warm data in Engineering based on extended

Storage.

POC for future architecture

Engineering System

• Larger time series

Based on:• SAP HANA platform

• SAP IQ software

• Hadoop

• SAP Data Services software

Production System

• Actual time series

Based on:• SAP HANA platform

• SAP IQ software

• Hadoop

• SAP Data Services software


Increase step by step the maturity in IOT scenarios

How we proceed: Start Small - Think Big

• Connect and centralize

connected assets

• Make ‘stupid’ assets

‘smart’ with sensors

• Collect data – even if

you don’t use it yet

• Automate the models

with AI

• Interconnect with SAP

EAM for preventive and

corrective actions

• Start working on analytics

and reporting part

• Set up organisations with

data scientists

• Interconnect different

disciplines

• Centralize all data in a Big

Data engineering and

production environment

• Integrate useful data in

mobile apps for field workers

• Work with Geo-enabled

videostreams, photos

• Smart visualisation and

analytics

• Start building models

• Predictive models

• Anomaly detection

• Engineering rules

• Data filtering and alerting

• Build a business confidence

level


• Bring in Predictive skills (AI knowledge) to get started

• Check the economic model for placing sensors

• Quality of sensor is crucial: avoid sensor failures

• Huge change on way of working: build confidence level first

• Easy and simple solutions can work.

• Maturity can increase step by step.

49

Key Messages

July 2017 Infrabel – Building the Smart Railroad

Thank you


Documents

SAP Enterprise Asset Management Solution Overview and ... · PDF fileSAP Integrated Business Planning SAP Asset Strategy and Performance Management (planned) End-to-end process enablement