Enabling Predictive Maintenance: Real-Life Use Case

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Kaeser CompressorsEnabling Predictive Maintenance

Timo Elliott, SAP Innovation Evangelist

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Kaeser Compressor

≈€500 million, 4,800 employees, 50 countries (partners in additional 60 countries)

Rotary screw compressors, vacuum packages, refrigerated and desiccant dryers, condensate management systems, portable compressors, filters, and blowers.

Global leader in manufacturing compressed air systems

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Microswitches

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Dairy Products

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Records

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Bridges

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Service and Innovation

Kaeser’s goal is to provide exceptional customer service and innovative solutions.

“You are doing business with a company with a family tradition of producing quality equipment, not a company focused on meeting Wall Street estimates. Thomas Kaeser is proud to put his name, his father’s name and his father’s father’s name on every product.”

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Business Goals

• Make maintenance and other services offerings more cost-efficient and more valuable to customers

• Streamline the supply chain

• Innovate through new technologies and business models

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Advanced Maintenance Analytics

Predictive and prescriptive maintenance analytics will dominate the analytics market within five years. Revenue from advanced maintenance analytics as % of total maintenance analytics market:

Source: ABI Research forecasts

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Maintenance 101

Corrective Maintenance Preventative Maintenance Predictive Maintenance

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How It Works

Connected: The Sigma Air Manager 2 connects all of the machines within a compressed air station and constantly transmits all operational data from each machine to the Kaeser Data Center located at Kaeser’s headquarters in Coburg, Germany.

Predictive: This allows predictive maintenance and active energy management of the compressed air supply system.

Easy to install: The machines easily connect to building and production control systems – allowing users to “Join the Network” quickly and simply.

Secure: The system architecture complies with the recommendations of the German Federal Information Technology Security Office (BSI), and is safe from external tampering by unauthorized third parties.

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Complex Event Processing

Event stream processing for“data in motion”

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Modeling Example

E.g. Total energy consumption

• Aggregation of 10 sec values

• Calculation of typical consumption patterns

• Pattern associated with each compressor and day

Repeat for temperature, pressure, vibration, etc.

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Using the Predictive Models

Model combines sensor readings and ERP data (location, type of usage, last service, etc.)

• Status alerts: “Oil change / oil analyze / no action”

• Predict machine failure 24 hours in advance

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High-Level Technical View

Predictive Model(in-memory)

Long-term disk storage

User Interfaces

CRMERP

Event Stream Processing

all sampled

Customer Field Svs Sales R&D

DW

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Analysis Across Entire Lifecycle

“This has allowed us to bring the entire lifecycle of the sales process under careful scrutiny—from lead management to requirements analysis, solution planning and solution implementation.

And with real-time information, we have streamlined our supply chain to deliver on customers’ changing needs while generating healthy margins”

Kaeser CIOFalko Lameter

Increase effectiveness

Increase efficiency

IT / OTConnectivity

Time, effort or cost is well used for the intended task or purpose

Effectiveness is the capability of producing a desired result

Condition Monitoring

Remote Service

Fault PatternRecognition

Machine HealthPrediction

Create Maintenance

or Service OrderSchedule Order

Execute Orderon mobile device

Visual Support

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Solution Summary

• Real-time business solution powered by an in-memory computing platform to enable automatic monitoring of customer site air compressors

• M2M interface to monitor customers’ mission-critical air compressors around the clock, with resources on call to address issues swiftly

• Predictive analytics to help customers plan downtime and avoid unexpected outages

• Portal to accelerate problem resolution and enable customer service personnel to be more proactive and more customer-oriented

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Benefits

Customers• Less downtime• Decreased time to resolution• Optimal longevity and performance

Kaeser• More efficient use of spare parts, etc• New sales opportunities• Better product development

“We are seeing improved uptime of equipment, decreased time to resolution, reduced operational risks and accelerated innovation cycles.

Most importantly, we have been able to align our products and services more closely with our customers’ needs.” �

Kaeser CIOFalko Lameter

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Some Future Directions

• Detailed profitability analysis

• Move all business applications to in-memory

• Move CRM to cloud to enable collaboration and mobile

“By thinking big and supplying new service functionality to our customers, Kaeser has substantially extended its market attractiveness and reach.

Using in-memory, we have strengthened our position as a thought leader and market leader in compressed air systems and services.”

Kaeser CIOFalko Lameter

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New Business Models

“People don't want quarter-inch drill bits. They want quarter-inch holes.”

Leo McGinneva

Strategy: create next-level business, selling air and service rather than machines

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Predictive Maintenance

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Connected Cars

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Fixing London Traffic Jams

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Networked Crane Safety

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Smart Washrooms

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Sensors Enable New Processes and Applications

Weissbeerger Beverage Analytics

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Information Ecosystems

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Many Other Examples

Dealer

Sales

Service

Service

Owner/Operator

Fleet Driver/

Operator

OEM

R&D WarrantyProcurement Manufacturing

Predictive Quality

Assurance(Production)

Machine Health

Analysis(Service,

Sales, R&D)

Vibration Analysis(Service,

R&D)

System Mainte-nance

Prediction(Service)

Vehicle Health

Prediction(Production

<> After-Sls.)

Main-tenance

Transpar-ency App

(Service)

Aircraft Health

Prediction(Service)

Train Health

Prediction(Servcie)

Emerging Issues(R&D)

Defect Pattern Identifi-cation(R&D)

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SAP HANA Cloud Platform - the Internet of Things enabled in-memory platform-as-a-service

Machine Cloud (SAP)

HANA CloudIoT Services

End Customer(On site)

Business owner(SAP Customer)

HANA Cloud Integration

Business Suite Systems

(ERP, CRM , etc.)

SAP ConnectorDevice

HANA Big Data Platform

Data Processing

Extended Storage

Hadoop

In-Memory Engines

Streaming

Storage∞

HANA Cloud Platform

Machine Integratio

n

Process Integratio

n

IoT Applications(SAP, Partner and

Custom apps)

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SIEMENS Cloud for Industry

The SIEMENS ‘Cloud for Industry’ connects the worlds of machines and business via:• the HCP for IoT• open APIs • easy connectivity.

It is the successor of the SIEMENS Plant Data Services.

It is planned to be an open platform:

• Open to non-Siemens assets and non-SAP back-ends

• Endorsing the OPC UA Standards

• Creating a separate, yet adjacent & complementary partner developer network

R&D Sales ManufacturingAftermarket

ServiceSupply Chain

HANA Cloud Platform for the Internet of Things

PartnerConnectivity

CustomerConnectivity

SAPConnectivity

SIEMENSConnectivity

PartnerApplications

CustomerApplications

SAPApplications

SIEMENSApplications

Machine connectivity to SIEMENS customers plants

Business Process Integration (SIEMENS or SIEMENS customers)

Cloud for Industry

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Maturity

Networking and Simple Reporting

Controllable Devices and Assets

Condition-Based Monitoring

Analytics and Predictions

Integration into the Corporate Processes

New Service & Business Models

Basic

Intermediate

Advanced

Leader

Expert

Experienced

Added Value for the Company Knowledge Based Society

Source: Accenture

SupportingTechnologies:

Big Data

Internet of Things

Cloud

Mobile

Analytics

Integration

© 2015 SAP SE or an SAP affiliate company. All rights reserved.

Conclusion: IoT For Business Is A Big Opportunity

“as more sensors are added to existing workflows, better customer service, better product support and faster product cycles will quickly be achieved.”

Vernon TurnerSenior Vice PresidentIDC

© 2015 SAP SE or an SAP affiliate company. All rights reserved.

Thank you!

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