Intelligent Asset Management: ASPM and PDMS · 2018-10-29 · Asset Central is the “Foundation” for AIN, ASPM and PDMS (and others). It is the layer to integrate between SAP Cloud

CUSTOMER

August 2018

Dr. Walter Zimmermann

Product Management

IoT and Digital Connected Asset

Intelligent Asset Management: ASPM and PDMS

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

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Selling equipment

Untrusted & disparate Asset information

Limited analytical capabilities

Reactive Maintenance

Disconnected Systems and Lifecycle

Standalone and Isolated Assets

Traditional OPEX-based planning

Paper-based work instructions

Optimized for Physical Structure

Pay per use / Equipment as a Service

Collaborative Single source of truth

Real Time Analytics with Simulation

Prescriptive maintenance

Closed loop Product and Asset Lifecycle

Connected and Smart Digital Twins

Asset criticality based maintenance strategy

Smart work instructions with 3D visualizations

Mechatronics / Software in Products/Assets

Global Asset Management TrendsTransformation to Smart Digital Connected Assets

NowYesterday

Pay per use

Digital Twin

3D visualization


Production planners, Operators aim for the following…

Overall equipment effectiveness

Return on assets

Unplanned outages

Annual service and maintenance cost

Planned maintenance budget vs. actual cost

Reduce safety incidents

Reduce costs

Maximize asset productivity

Drive safe operations

Reducing energy and input costs actual cost


All physical assets are managed according to an Asset Strategy

The prime objective of an asset strategy is to optimize the balance between

equipment performance, equipment availability and the cost of maintaining the asset.

The asset strategy will dictate how assets are cared for and

is measured by KPIs for performance,

availability and cost. This includes safety and

environmental integrity

Preventive

time- or usage

based interval

On-Condition

the P-F-curve based on

measurable deterioration

Predictive

the P-F-curve using

big data & analytics

Failure Finding

risk-based

interval

Run to Failure

Run to

Repair

Modification

includes accepting

degraded performance

Asset strategies are:

and if none of the above the strategy defaults to:


Asset Strategy and PerformanceMaintenance strategies getting more sophisticated

Source: taken from Gartner and modified.

Run to Failure

Preventive based on time

Preventive based on usage

Based on condition

Predictive Forecasting

Reliability-Centered Maintenance

Financial / Risk Optimized

Main

tenance S

trate

gie

s

Decid

e o

n S

trate

gie

s

Holis

tic E

nte

rprise A

sset

Mgm

t

tactical to

strategic

fragmented to integrated

Few

Data

Big Data

Integrated

Information

Source: Gartner (modified)

Solution Blocks


Concept - Intelligent Asset Suite

• Asset Central

Asset Central is the “Foundation” for AIN, ASPM and PDMS (and others). It is

the layer to integrate between SAP Cloud Platform and S/4HANA Cloud for

some key business objects and full e2e next gen asset & service management

scenarios

• Asset Intelligence Network

Collaborative asset management bringing key stakeholders (operator, OEM,

service providers, others) together in a digital ecosystem solving complex

execution, predictive and planning activities with centrally managed asset

information

• Predictive Maintenance & Service

Predict maintenance events to subsequently predict business processes for

operational excellence (planning, procurement, scheduling, execution, …)

lowering risk and improving asset availability

• Asset Strategy & Performance

Define and plan asset goals and maintenance execution strategies holistically

for improved performance

• Predictive Engineering Insight

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

of stress and fatigue to drive predictions

• Core Service Management

Core service process execution via planned and actual order processing in the

integrated S4HANA Digital Core system (integrated with finance / controlling,

procurement, inventory)

IoT / ML

Asset

Strategy &

Performance

Predictive

Maintenance

& Service

Asset

Intelligence

Network

ERP(Logistics / CRM

Production)

Digital

Platform

Data Hub Cloud

Platform

Maintenance

Execution

Service

Management

Predictive

Engineering

Insight

Asset

Central


Asset Central

Mode 1: System of Record Mode 2: System of Differentiation

Asset Central becomes

▪ the “Glue-Component” between

S/4HANA Cloud Maintenance

Management and Components build on

SAP Cloud Platform

▪ the leading System for the Equipment

(and Functional Location)

▪ mandatory for Cloud Deployments; it

remains optional (but recommended)

for on-Premise

▪ Asset registry for managing complex

asset structures and provide seemless

integration and data consistency.

▪ Consistent Fiori UI and APIs that work

across platforms – CF, XSA, Neo*

SAP Asset

Strategy &

Performance

SAP

Predictive

Maintenance

& Service

SAP Asset

Intelligence

Network

SAP

Predictive

Engineering Insight

Maintenance

& Service

Management Asset

Central

Consumed via Mobile

e.g. Sensor Feeds, Data Historian, etc.

Product

Lifecycle

Management

Digital

Manufacturing

Insights

Logistics

SAP Cloud Platform & XSA Onprem(Big Data Platform)

S/4HANA Cloud

Efficiently manage Core Service

Processes in Service Core▪ Collaboration Platform – Network

▪ IoT-Platform

‒ Sensor Data, Big Data

‒ Prediction / ML


End to end value across the lifecycle of the Digital Twin

Automatic

Onboarding &

Topology Detection

Asset Health & Key

Indicators

2D Sensor Chart

and Alert List

Error Codes and

Knowledge Base

Failure Mode based

Predictions

3D Repair

Instructions

Work Orders & PM

Notifications

Service Ticket

Spare Part Ordering

Questionnaire &

ChecklistImprovement

Request


The DIGITAL TWIN will cover the entire product lifecycle

Ideas

Geometries

Stress simulations

Cost

Production data

Quality fingerprint

Ramp Up

. . .

Output

Performance

. . .

Issues

Cost

Profitability

Closed loop engineering

Designs

. . .

. . .

. . .

As Built

The physical world

As Designed

Engineering

The digital world

As MaintainedAs Delivered

DecommissionProduction Installation Operation

11PUBLIC© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ


Intelligent Asset ManagementVision and Objectives

“Support Manufacturers and

Asset Operators in

defining, planning and monitoring

the optimal service and

maintenance strategy for

physical products and assets

by providing the required level of

collaboration, integration and

analytical insights”


Asset Management Monitoring

Analyze Cost and Performance

Maintenance Strategy Execution

Perform Inspections, Condition Monitoring,

Predictive Maintenance, …

Maintenance Strategy Implementation*

Create/Change/Delete Maintenance Plans, Task Lists, Inspection, Condition Based Maintenance, Predictive Maintenance, Run to Fail

PM Review*

Preventive Maintenance Review

Evaluate the current maintenance

plans and their effectiveness

SAP Asset Strategy and Performance ManagementDeveloping a Maintenance Strategy

Manage Asset InformationManage Locations, Equipment, Groups, Systems, Failure

Modes…

Asset Risk & Criticality Assessment

Rating assets according to criticality for the business

RCM*

Reliability-Centered Maintenance

Evaluating threats to safety,

operations, and maintenance

FMEA

Failure Modes and Effects Analysis

Analyze component failures and

associated results on operations

S/4HANA and SAP ERP

PdMS

Application of Appropriate Methodology

*planned


Equipment: Review Risk & Criticality Matrix


Preventive, Predictive towards Prescriptive Maintenance

Today:

Use of

Maintenance

Strategy*

The Internet of Things is

leading to increased use of

predictive maintenance

Although still relevant,

preventative maintenance

typically results in over-maintaining

assets and high cost

*Proportion of maintenance strategies are for illustration purposes only and will vary based on many factors

Future:

Use of

Maintenance

Strategy*

Run to Failure Preventative Predictive

TODAY FUTURE

The goal is to enable more IT/OT driven

approaches for prescriptive maintenance

with Machine Learning and IoT enabled

Engineering Simulations to reduce

unplanned failures and the number of

maintenance actions


Multiple Approaches to Predictive MaintenanceIT 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

IT Driven (data science & business rules)

Oil Analysis

X-ray Radiography

P Potential Failure

Why more IT driven approaches?▪ 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


Preventive

Companies are moving from a

reactive to a proactive approach

to maintenance.

An opportunity is available for

organizations to leverage machine

data for better business insights.Wait until a machine

fails and then

undertake

maintenance.

Perform

maintenance at

regular intervals,

based on

observations of

abnormalities.

Continuously observe

the status of assets and

react to predefined

conditions and events.

Apply advanced analytics of

operational and business

data to help determine the

condition of specific

equipment and predict when

to perform maintenance.

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 18

Condition-

basedPredictiveReactive

Where are maintenance and service heading?Organizations are maturing their maintenance strategies

Customer


What is the best strategy?How to transform the game?

• Quantum leaps can be reached by changing the

maintenance strategy to a more agile approach.

• 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 less failures while reducing

maintenance cost.

t


SAP Predictive Maintenance and ServiceVision and focus scenarios

Scenario Solution components

Alert-based condition monitoring (including prescriptive maintenance)

Users can drill down into the List of Alerts and use observations to initiate follow-up activities from a

list of possible actions based upon failure modes and maintenance history. Executed actions are

documented and current status visualized.

- Alert analysis tool

- Alert modeling and creation

- Event to action (business rules)

- Deduplication of events

- Work activity analysis tool

Indicator-based condition monitoring

Based on machine learning health scores, users can find the bad-acting asset and drill down to

analyze the root cause using the explanation of health scores. With their observations, users can

initiate follow-up activities by selecting from a list of possible actions based on failure modes and

maintenance history. Executed actions are documented and visualized.

- Equipment scores analysis tool

- Equipment list analysis tool

- Key figure analysis tool

- Indicator management

- Aggregation and categorization

Fleet analysis

The previous scenarios focus on single equipment, while fleet analysis offers functionalities to

operate on a fleet level. This scenario extends the fleet analysis capabilities by including

information from indicator forecasting.

- Analysis tool catalog

Emerging issue detection (EID)

EID allows for the early identification of problems and their root causes in a fleet of machines using

exploration and machine learning.

- Explorations

- Evidence packages

- Collaboration

- Explorations overview

Indicator-based maintenance plan optimization

Users can simulate optimal maintenance plans for equipment based on maintenance and health

score history by applying specific target variables. Simulation results can be transferred to SAP

Asset Intelligence Network to be used in a preventive maintenance review process.

- 2D chart analysis tool


Solution

Customer Example TrenitaliaTrain Operator


• 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


Innovation in Maintenance: Why, and Why Now?

Unsatisfactory current practice:

• Preventive maintenance at fixed deadline based on distance

and time

• Corrective maintenance for fault recovery

• Failures are not prevented

• Low correlation between maintenance deadlines and effective

consumption of components

• Maintenance plans optimized only in terms of logistic execution

• Complex and ad-hoc checks in first level

New opportunities enabled by technology:

• Big data and data science

• Hyperconnectivity / Internet of Things

• Advanced process optimization

• Automated diagnostics and checks based on vision and laser

scanner systems

• Robotic systems for inspections and operations


Guiding Principles

Systemic optimization of the

performance of fleets to fulfill the

requirements of commercial

services

• One platform for all the components, all the trains and all

diagnostic equipment

• Integration of operational and transactional data to drive insight,

business consequences and execution

• Full extensibility to progressively increase the intelligence and

value of the system incorporating additional insights


Planning EngineImproving the Relevance of Maintenance Activities

Planning based on Km /

Time of operations

Planning based on Life Indicators Planning based on Life

and Health Indicators

• Current model, standard in the

industry

• Easy to operate because all the

components in the material share the

same driver

• Sub-optimal for the same reason

• Based on more relevant drivers and

indicators that better represent the

effective current and expected usage

of every single component

• Increased precision is directly

connected with the quality and

precision of the planning for the

materials

• Requires optimization methods in

order to produce a plan, due to the fact

that every component in a material can

have different life situation

• Further increase the relevance of the

plan, considering the future effects that

the evolution of life indicators will have

on the ability of every component to

perform, and on its risk of failure

• Requires sophisticated mathematical

methods to predict the behavioral

patterns of health indicator based on

the expected usage of the materials

• Health indicators can in any case used

to trigger short term maintenance

activities


Life and Health Indicators in Action

BearingsWheelsEngineDoorsPantographA/C…

Life Ind.f(Km, radios, weight)f(Km, exchanges, radios, weight)f(Km, weight)Open / close cyclesUp / down cyclesf(hours, ext temp, int temp)

Health Ind.Vibration patternVibration patternf(power gen, power absrbd)Open / close timeUp / down timef(Delta int temp)

Telemetric readings

Operational plans for Rolling Stock

Check availability of resources

Maintenance calendar

Consolidated picture for the planning unit

Check against safety thresholds

Predicted Life and Health Indicators

Detailed Information on Infrastructure

• “Real” Big Data in action: hundreds of TB, huge number of

sources and entities involved

• Complex algorithms to predict indicators and optimize the

outcome across multiple dimensions

• Huge transformational value and financial impact


Life Indicators in Action: Braking System

Objective and Approach

Calculation of the life indicator energy dissipation by friction braking systems, with separated analysis for locomotives and coaches

Development and test of calculation algorithms for all the possible cases identified

Pressione in Condotta Generale e Cilindro Freno

Energia dissipata cumulata per le carrozze

Results Achieved

Monitoring of the effective usage and level of wear-out for every single component of the braking system against the risk thresholds identified


Life Indicators in Action: Braking System

• High variability of the energy dissipated

per km clearly indicates that distance is

not a good indicator of consumption for

braking systems

• Comparison between traditional

indicators such as km and more precise

life indicators highlights the significant

opportunity for optimization of

maintenance operations


Anomaly Detection

Earth-Movers Distance (EMD)

• Every battery can be compared to a “normal” battery in each mode of operation… idle, charging, discharging

“Normal” Battery Installed Battery

=/


Ranking of Distance Among Homogeneous Components

Rank Battery1 1282 3483 1334 1445 0086 1817 3668 0519 336

10 536…

371 103372 135373 281374 463375 096376 109377 086378 139379 308380 280

Massive automatic analysis of components vs. normal behaviors to measure and rank the distance, and

identify the potential bad actors

Maintenance policies get

differentiated by the various

sections of the ranking (e.g.:

do not perform any

preventive actions for the

batteries in the top 50% of

the ranking)

Very significant projected

savings on maintenance

costs without increase in

failures


Customer exampleCompressor manufacturer

• Provider of compressed air systems and

compressed air consulting services

• Changed their business model from selling

compressors to selling compressed air

• Moved customers from CAPEX to OPEX

• Compressors equipped with sensors

• SAP Predictive Maintenance & Service On-Premise Edition

• SAP HANA

• SAP CRM Service

Company

Solution

Benefits

• IoT as an enabler for the new business model

• Improved availability of compressor stations

• Move from unplanned to planned maintenance

Process Innovation

IT / OT

Connectivity

Condition MonitoringRemote Service

Fault Pattern

Recognition

Machine Health

Prediction

Create Service

OrderSchedule Order

Execute Order

on mobile deviceVisual Support


Customer exampleIndustrial equipment manufacturer

• Leading manufacturer of separators and

decanters for industrial usage

• New service offering which monitors equipment

during the operation to ensure service contract

compliance

• SAP HANA Cloud Platform

• SAP Predictive Maintenance & Service Cloud Edition

• SAP CRM Service

Company

Solution

Benefits

• Service execution based on real-time machine data

• Increased machine uptime

• Improved service contract compliance

• Higher service productivity and customer satisfaction

Process Innovation

Spare Parts &

Tools

Remote

Service

Engineer

Real-time

Monitoring


SAP Predictive Maintenance and ServiceMachine learning challenges

High dimensional data

No labeled failure data

Rare failure events

Outdated models, human scale

Use case specific algorithms

Feature construction/selection requires data

scientists & domain user collaboration

Model management, continuous learning, model

quality assessment and automated scoring

Anomaly detection and adaptive learning

through user feedback

Failure prediction using ensemble learning

Extensibility, integration and productization of new

asset and customer-specific algorithms

SOLUTION

Lack of data science resources Automated machine learning for failure prediction and

anomaly detection


SAP Predictive Maintenance and ServiceMachine Learning Engine - Adaptive Learning

+ No need for labels but normal state has to be

known for some algorithms

+ Finds previously unknown failures

Not every anomaly will be related to a failure…

may lead to false alarms

A domain expert has to validate the anomaly

and decide if action should be taken

+ If the quality of the model is good then the

predictions can be done automatically without

involvement of experts

+ Some algorithms automatically reveal possible defect

patterns which can be interpreted by the domain user

Standard supervised failure prediction

algorithms need sufficient number of failures

Anomaly Detection Failure Prediction

Adaptive Learning

Need adaptive learning to avoid false alarms and

improve accuracy of models

Future capability*


SAP Predictive Maintenance and ServiceMachine Learning Engine – Anomaly Detection Algorithms

Anomaly DetectionType Technique Input Data

Type

Output

High-dimensional

Anomaly Detection

Principal Component

Analysis (PCA)

Sensor Anomaly

score

High-dimensional

Anomaly Detection

One-Class Support

Vector Machine (One-

Class SVM)

Sensor Anomaly

score

Distance-based

Anomaly Detection

Earth-Movers Distance

(EMD)

Sensor Anomaly

score

Time-based

Anomaly Detection

Multivariate Auto

Regression (MAR)

Sensor Anomaly

score


SAP Predictive Maintenance and ServiceMachine Learning Engine - Failure Prediction Algorithms

Type Technique Input Data

Type

Output

Failure Prediction

based on Failure

and Sensor Data

Tree Ensemble

Learning

Sensor and

Failure

Data (IT)

Probability of Failure

Failure Prediction

based on Failure

and Sensor Data

Logistic Regression Sensor and

Failure

Data (IT)

Probability of Failure

Failure Prediction


SAP Predictive Maintenance and ServiceMachine Learning Engine

Apply Machine

Learning ProcessOutput

Machine Learning Engine

Remaining

Useful LifeAnomaly

ScoreHealth

Status

2530 days

SAP Predictive Maintenance and Service

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*Through SAP and Partner**

Train

Model

Configure

Model

Score

model

Feedback

Evaluate

Model

Analysis Tools &

Equipment Pages


SAP Predictive Maintenance and Service, on premise editionMachine Learning Engine

*Roadmap Item

Continuous Improvement & Learning

Failure

Prediction

Trigger prediction when

algorithm detects a

specific combination of

input variables

Anomaly Detection

Trigger anomaly alert

when the algorithm

detects an abnormal

pattern

New

Algorithms

Extensibility

Model

Management

Tools

Reinforcement*

Domain expert

feedback

• Supervised learning enables failure

predictions like Remaining Useful Life

• Finds contributing factors to failure events

• Unsupervised learning detects anomalies

• Enables Health Scores

• Expert feedback

• Models change as operational

environment changes

• Extensibility for out-of-the-box

algorithms

• Possibilities to deploy new

R based algorithms

39CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ *Roadmap Item

SAP Predictive Maintenance and Service, on premise editionMachine 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


SAP Predictive Maintenance and ServiceHow Failure Mode Analytics works

Use text mining to identify

failure modes from

technician notes

System matches topics to

standard failure modes (e.g.

ISO 14224)

Expert user double-checks

matching results

System uses supervised machine

learning based on user reinforcement to

assign all notifications to failure mode

System provides out-of-box metrics and

and visualizations

1 2 3

45


SAP Predictive Maintenance and ServiceFailure mode analytics


1. This is the current state of planning and may be changed by SAP at any time without notice.

Asset CentralProduct road map overview – Key innovations

Asset Master Data▪ Continued Indicators enhancement▪ Simplified Attribute definition through

reusable Code Lists▪ Continued Failure Modes enhancement▪ Modeling Alert Types and Alert Type

Groups▪ Multi language support for Models,

Spare Parts and Attributes▪ Mapping visual components with Spare

Parts▪ Mass assignment of Spare Parts▪ Guided Tour enhancement

Analytics▪ Advanced Analytics for Obsolescence

Management

General Topics▪ New application for data protection

and privacy

▪Integration▪ Enhanced integration to SAP ERP

Plant Maintenance and S/4 HANA▪ Mapping of SAP Cloud Platform IoT

Services 4.0

Asset Master Data▪ Indicators enhancement▪ Failure modes enhancement▪ Multi-standards and change of

classification▪ Mass object assignment and

harmonization

General Topics▪ IoT onboarding

▪Maintenance Execution▪ Reset Indicators and basic collection of

maintenance feedback


Plant Maintenance and S/4 HANA ▪ Automatic onboarding of SAP IoT

Application Enablement Services and SAP Cloud Platform IoT Services 4.0

▪ Integration to SAP Digital Manufacturing Insights

▪ Integration to SAP Predictive Engineering Insights

▪Asset Master Data▪ Further enhancement of multi language

UI▪ Managing multiple Equipment

structures over the lifecycle▪ Initial release of RAMI 4.0

Administration Shell▪ Enhanced geographical Map▪ Hierarchical and fleet-based Indicators

aggregation

▪Maintenance Execution▪ Advanced collection of maintenance

feedback▪ Multiple values capturing of Indicators


Plant Maintenance and S/4 HANA▪ Continued Integration to SAP

Predictive Engineering Insights▪ Continued Integration to SAP Digital

Manufacturing Insights▪ Enhanced Integration with SAP Edge

Services

Asset Master Data▪ Managing multiple Location and

System structures over asset lifecycle▪ Continued RAMI 4.0 Administration

Shell▪ Modelling Model variants▪ Enhanced search capabilities▪ Enhanced roll-up & drill-down of

object content across object hierarchy


Plant Maintenance and S/4 HANA▪ Integration with SAP Analytics Cloud▪ Integration with SAP Mobile Asset

Manager▪ Document management

1805 – Recent innovations1,2 1808 – Planned Q3/18051,2 1811 – Planned Q4/18081,2 1902 – Planned Q1/20191,2



SAP Asset Intelligence NetworkProduct road map overview – Key innovations

Asset Master Data▪ Enhanced Indicators and Component

Indicators▪ Continued Failure Modes enhancement▪ Modeling Alert Types and Alert Type

Groups▪ Multi language support for Models, Spare

Parts and Attributes▪ Mapping visual components with Spare

Parts▪ Harmonized data model, UI and release

cycles for SAP PdMS, SAP ASPM and SAP AIN

General Topics▪ GDPR support and audit report▪ Advanced Analytics for Obsolescence

Management

Integration▪ Enhanced integration to SAP ERP Plant

Maintenance and S/4 HANA

▪ SAP PLM Integration: Model Publication in S/4HANA Cloud 1805

▪ Integration with SAP Edge Services

▪ Mapping of SAP Cloud Platform IoT Services 4.0

Asset Master Data▪ Continuous Improvement for Failure Modes▪ Continuous Improvement for Indicators▪ Multi-language APIs and maintenance UI

for key objects▪ eCl@ss and standards▪ Notification and Work Order details and

enhanced integration

General Topics▪ IoT onboarding

Maintenance Execution▪ Reset Indicators and basic collection of

maintenance feedback

Integration▪ Enhanced integration to SAP ERP Plant

Maintenance and S/4 HANA ▪ Automatic onboarding of SAP IoT

Application Enablement Services and SAP Cloud Platform IoT Services 4.0

▪ Integration to SAP Digital Manufacturing Insights

▪ Integration to SAP Predictive Engineering Insights

Asset Master Data

▪ Content Packages and Digital Services

▪ Automatic mass-upload and publishing

▪ Sharing of work orders and notifications

▪ Model Lifecycle support and generations

▪ Reference Implementation for

RAMI 4.0 Administration Shell

▪ Enhanced geographical Map

▪ Hierarchical and fleet-based Indicators

aggregation

Maintenance Execution

▪ Advanced collection of maintenance

feedback including multiple values

capturing of Indicators

Integration

▪ Enhanced integration to SAP ERP Plant


▪ Enhanced Integration with SAP Edge

Services

▪ Ariba Solution Integration

▪ Blockchain enablement

Asset Master Data

▪ Enhanced search capabilities

▪ Enhanced roll-up & drill-down of object content across object hierarchy

▪ Service Process Integration

▪ Enhanced Firmware processes

Integration

▪ Enhanced integration to SAP ERP Plant


▪ Integration with SAP Analytics Cloud

▪ Integration with SAP Mobile Asset Manager

1805 – Recent innovations1,2 1808 – Planned Q3/18051,2 1811 – Planned Q4/18081,2 1902 – Planned Q1/20191,2



SAP Asset Strategy and Performance ManagementProduct road map overview

1808 – Recent Innovations 1811 – Planned Q4/20181 1902 – Planned Q1/20191 1905 – Planned Q2/20191,2

Enhancements to Assessment

Process

• Ability to display pending

assessments

• Compare two or more assessments

that use the same template

• Swap dimensions on the Risk Matrix

FMEA enhancements

• Guided Activity with a video tutorial for

FMEA assessments

• Identify and include similar equipment for

scope of a FMEA study

Checklists

• Generation of sub-class specific

Inspection Checklists for Equipment,

Locations and Models

• Perform Inspections on multiple objects

including updating attributes and

indicators

• Generate and store Inspections Results as

PDF files

Analytics

• Risk Distribution across fleet and

class/sub-class

• Additional Highlight Cards

Reliability Centered Maintenance

(RCM)

• New assessment for enabling a RCM

study

• Record Operational Context,

Functions and Functional failures

• Configurable questionnaire to help

the assessment team make better

decisions

Root Cause Analysis (RCA)

• Perform RCA using 5-Why and Cause

& Effect tree analysis

• Cascade Findings to similar Systems,

Functions and Locations

PM Review

• Review tasks across all maintenance

plans by Maintenance Type (Repair vs

Replace) and identify redundancies &

discrepancies.

• Recommend changes to maintenance

plans based on unmitigated risk

reduction and Cost

Asset Strategy Workbench

• Strategic view of Equipment and

Functional Locations

• Guidance to Reliability Managers on

maintenance methodology



SAP Predictive Maintenance and Service, cloud editionProduct road map overview – Key innovations

Maintenance Planning, Scheduling &

Optimization

▪ Rule-based alert creation for equipment models

▪ Automatic rule-based actions for alerts, like

sending of emails

▪ Visualization of equipment on a map

▪ Support for thematic maps using indicators

▪ Multi-chart data visualization for a single

equipment

▪ Model quality assessment in Machine Learning

Engine

▪ New machine-learning algorithms for anomaly

detection and failure predictions

Maintenance Planning, Scheduling & Optimization

▪ Enhanced rule definitions supporting advanced rule conditions and rules on equipment level

▪ Alert de-duplication to suppress creation of redundant alerts

▪ Enhanced alert lifecycle information with alert status and processor

▪ Equipment benchmarking by comparing indicators across equipment

▪ Indicator forecasting and visualization of forecasted values in 2D charts

▪ Automated machine learning to enable business user to configure equipment health monitoring

Analytics

▪ Ad-hoc data exploration and analytics through integration with SAP Analytics Cloud

Integration

▪ Basic customer portal functionality through integration with SAP Asset Intelligent Network


Optimization

▪ Fingerprint management

▪ Prescriptive maintenance supporting alert

explanation and recommended actions for

alerts

▪ Machine Learning Engine supporting adaptive

learning through user feedback and health

indicator explanation

▪ Root cause analysis using correlations between

failures and equipment master data and

configuration

Integration

▪ Enhanced customer portal functionality

supporting extensibility

Generic topics

▪ Personalization through Fiori variant

management


Optimization

▪ Extensibility of Machine Learning Engine through

custom algorithms

▪ Support for additional chart types in data

visualization, like histograms and characteristic

curves

▪ Enhanced emerging issue detection enabled by

additional analysis tools and machine learning

algorithms

▪ Optimizing counter-based maintenance strategies

in ERP using indicators

1805 – Recent innovations1 1808 – Planned Q3/20181,2 1811 – Planned Q4/20181,2 1902 – Planned Q1/20191,2


Analysis tools


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

Machine Learning Engine

Analysis Tools Catalog

SAP Predictive Maintenance and Service

Explorer (fleet view)

Explorer Equipment

Page

SAP Leonardo Foundation

SAP Leonardo for Edge Computing

SAP Leonardo Foundation

SAP Leonardo for Edge Computing

Logistics & Maintenance

Execution Systems

Business DataMachine Data

Equipment Page


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

Work Orders Notifications

3D Chart


SAP Predictive Maintenance and Service, on premise editionExplorer

Locations

New Orleans Refinery

Eagle Ford Field

Locations

Explorer



Locations


Eagle Ford Field

Locations Filter by Location

Filter by Locations

Explorer



Locations


Eagle Ford Field

Locations

Filter by Locations

Filter by Location Analysis Tools Catalog


Explorer



Locations Filter by Location Analysis Tools Catalog


Explorer




Key Figures Analysis Tool

Analysis Tools Catalog Analysis Tool


Explorer




Equipment List Analysis Tool



Analysis Tool

Explorer



Locations Filter by Location Analysis Tools Catalog


Analysis Tool

Explorer

Equipment Scores Analysis Tool




Map Analysis Tool



Analysis Tool

Explorer




3D Chart Analysis Tool



Analysis Tool

Explorer




Custom Analysis Tool



Analysis Tool

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

Explorer


SAP Predictive Maintenance and Service, on premise editionEquipment Page

Equipment View Explorer

Explorer

Equipment View

Serial #12345

Equipment Page


Equipment Page

Explorer


Equipment View

Equipment View Explorer

Serial #12345

Equipment Page



Information▪ Highlights

▪ Attributes

▪ Model Information

▪ Installation Information

▪ Life Cycle Information

Structure and Parts▪ Structure

▪ Spare Parts

Documentation▪ Documents

▪ Instructions

▪ Announcements

Monitoring▪ 2D Chart

▪ Error Codes

▪ Failure Modes

▪ Improvement Cases

▪ Work Orders & Notifications

Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line




▪ Attributes





▪ Spare Parts


▪ Instructions

▪ Announcements


▪ Error Codes

▪ Failure Modes



Time Line

Thank you.