Kaeser Compressor

Kaeser Compressors
Enabling Predictive Maintenance
Timo Elliott, SAP Innovation Evangelist
1
Kaeser Compressor
Global leader in manufacturing compressed air systems
≈€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.
2
Microswitches
3
Dairy Products
4
Records
5
Bridges
6
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.”
7
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
8
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
9
Maintenance 101
Corrective Maintenance
Preventative Maintenance
Predictive Maintenance
10
11
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.
12
Complex Event Processing
Event stream
processing for
“data in motion”
13
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.
14
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
15
High-Level Technical View
Customer Field Svs
Sales
R&D
User Interfaces
Long-term
disk storage
all
Predictive Model
(in-memory)
sampled
CRM
ERP
DW
Event Stream Processing
16
Analysis Across Entire Lifecycle
Increase
effectiveness
Effectiveness is the
capability of producing
a desired result
Increase
efficiency
Time, effort or cost is
well used for the
intended task or
purpose
IT / OT
Connectivity
Create
Maintenance
or Service Order
Condition
Monitoring
Remote Service
Fault Pattern
Recognition
Schedule Order
Execute Order
on mobile device
Machine Health
Prediction
Visual Support
“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 CIO
Falko Lameter
17
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
18
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 CIO
Falko Lameter
19
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 CIO
Falko Lameter
20
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
21
Predictive Maintenance
22
23
Connected Cars
24
Fixing London Traffic Jams
25
Networked Crane Safety
26
Smart Washrooms
27
Sensors Enable New Processes and Applications
Weissbeerger Beverage Analytics
28
Information Ecosystems
29
29
Many Other Examples
OEM
R&D
Procurement
Manufacturing
Warranty
Dealer
Service
Owner/Operator
Sales
Service
Fleet
Driver/
Operator
Emerging
Issues
(R&D)
Predictive
Quality
Assurance
(Production)
(Service,
Sales, R&D)
Vehicle
Health
Prediction
Defect
Pattern
Identification
(R&D)
Machine
Health
Analysis
Vibration
Analysis
Maintenance
Transparency App
(Service)
Aircraft
Health
Prediction
(Production
<> After-Sls.)
(Service)
Train
Health
Prediction
System
Maintenance
Prediction
(Service,
R&D)
(Servcie)
(Service)
30
31
SAP HANA Cloud Platform - the Internet of Things
enabled in-memory platform-as-a-service
Machine Cloud (SAP)
IoT Applications
(SAP, Partner and
Custom apps)
End Customer
(On site)
Device
SAP
Connector
Machine
Integration
Business owner
(SAP Customer)
HANA Cloud
IoT Services
HANA Cloud
Integration
Process
Integration
Business Suite
Systems
(ERP, CRM , etc.)
HANA Cloud Platform
Data Processing
In-Memory
Engines
Extended Storage
Hadoop
∞
Storage
Streaming
HANA Big Data Platform
32
SIEMENS Cloud for Industry
R&D
SIEMENS
Applications
Sales
Partner
Applications
Supply Chain
Manufacturing
Customer
Applications
Aftermarket
Service
Business Process
Integration (SIEMENS or
SIEMENS customers)
It is the successor of the SIEMENS Plant
Data Services.
SAP
Applications
HANA Cloud Platform for the Internet of Things
SIEMENS
Connectivity
Partner
Connectivity
Customer
Connectivity
SAP
Connectivity
The SIEMENS ‘Cloud for Industry’ connects
the worlds of machines and business via:
• the HCP for IoT
• open APIs
• easy connectivity.
It is planned to be an open platform:
Cloud for Industry
• Open to non-Siemens assets and nonSAP back-ends
• Endorsing the OPC UA Standards
Machine connectivity to
SIEMENS customers
plants
• Creating a separate, yet adjacent &
complementary partner developer
network
33
Conclusion: IoT For Business Is A Big Opportunity
Added Value for the Company
Knowledge Based Society
New Service &
Business Models
Leader
“as more sensors are added to existing
workflows, better customer service,
better product support and faster product
cycles will quickly be achieved.”
Experienced
Vernon Turner
Senior Vice President
IDC
Advanced
Intermediate
Expert
Supporting
Technologies:
Integration into the
Corporate Processes
 Big Data
Analytics and Predictions
 Internet of Things
 Cloud
Condition-Based Monitoring
 Mobile
 Analytics
Controllable Devices and Assets
 Integration
Basic
Networking and Simple Reporting
Source: Accenture
© 2015 SAP SE or an SAP affiliate company. All rights reserved.
Maturity
34
Thank you!
@timoelliott
timoelliott.com
[email protected]
© 2015 SAP SE or an SAP affiliate company. All rights reserved.