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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
