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Enabling Predictive Maintenance: Real-Life Use Case

My keynote presentation from Business Analytics for All conference in Belgium, covering the predictive maintenance project at Kaeser Compressors

Enabling Predictive Maintenance: Real-Life Use Case

  1. 1. 1 Kaeser Compressors Enabling Predictive Maintenance Timo Elliott, SAP Innovation Evangelist
  2. 2. 2 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
  3. 3. 3 Microswitches
  4. 4. 4 Dairy Products
  5. 5. 5 Records
  6. 6. 6 Bridges
  7. 7. 7 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.”
  8. 8. 8 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
  9. 9. 9 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
  10. 10. 10 Maintenance 101 Corrective Maintenance Preventative Maintenance Predictive Maintenance
  11. 11. 11
  12. 12. 12 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.
  13. 13. 13 Complex Event Processing Event stream processing for “data in motion”
  14. 14. 14 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.
  15. 15. 15 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
  16. 16. 16 High-Level Technical View Predictive Model (in-memory) Long-term disk storage User Interfaces CRM ERP Event Stream Processing all sampled Customer Field Svs Sales R&D DW
  17. 17. 17 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 CIO Falko Lameter Increase effectiveness Increase efficiency IT / OT Connectivity 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 Pattern Recognition Machine Health Prediction Create Maintenance or Service Order Schedule Order Execute Order on mobile device Visual Support
  18. 18. 18 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
  19. 19. 19 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
  20. 20. 20 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
  21. 21. 21 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
  22. 22. 22 Predictive Maintenance
  23. 23. 23
  24. 24. 24 Connected Cars
  25. 25. 25 Fixing London Traffic Jams
  26. 26. 26 Networked Crane Safety
  27. 27. 27 Smart Washrooms
  28. 28. 28 Sensors Enable New Processes and Applications Weissbeerger Beverage Analytics
  29. 29. 29 Information Ecosystems 29
  30. 30. 30 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)
  31. 31. 31
  32. 32. 32 SAP HANA Cloud Platform - the Internet of Things enabled in-memory platform-as-a-service Machine Cloud (SAP) HANA Cloud IoT 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 Integration Process Integration IoT Applications (SAP, Partner and Custom apps)
  33. 33. 33 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 Manufacturing Aftermarket Service Supply Chain HANA Cloud Platform for the Internet of Things Partner Connectivity Customer Connectivity SAP Connectivity SIEMENS Connectivity Partner Applications Customer Applications SAP Applications SIEMENS Applications Machine connectivity to SIEMENS customers plants Business Process Integration (SIEMENS or SIEMENS customers) Cloud for Industry
  34. 34. 34 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 Supporting Technologies:  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 Turner Senior Vice President IDC
  35. 35. © 2015 SAP SE or an SAP affiliate company. All rights reserved. Thank you! @timoelliott

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My keynote presentation from Business Analytics for All conference in Belgium, covering the predictive maintenance project at Kaeser Compressors


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