SAP Enterprise Asset Management Solution Overview and Strategy in a Nutshell Dr.-Ing. Achim Krüger, Vice President, Line of Business Asset Management, SAP SE July 12, 2017 PUBLIC
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SAP Enterprise Asset Management (SAP EAM) solution Trends in asset management ISO 55001, ISO 14001, ISO 45001, and the like Internet of Things (IoT) to scale connectivity Optimizing cost, risk, and performance Big Data for getting insight from IT and OT Balancing OPEX with CAPEX Meeting stakeholder expectations Empowering practitioners Facilitating collaboration among EPCs, OEMs, service providers, and operators Business challenges Technology enablers Analytics for prediction and simulation Machine learning to improve business decisions Enterprise mobility to empower employees Cloud for collaboration 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 3
SAP Enterprise Asset Management Asset management in an ever-more-connected world Cost Risk Connecting corporate objectives with the asset system 55001 Performance Asset Business needs analysis Plan and design Acquisition and commission Operation and maintenance Configuration and change management Decommission and disposal Supplier Manufacturer Connecting all stakeholders alongside the asset lifecycle Service provider Owner or operator EPC IT Connecting IT with OT OT Dealer www.sap.com/eam 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 4
SAP Enterprise Asset Management Supporting asset management processes from end to end Portfolio and project management Asset operations and maintenance Environment, health, and safety Asset network Idea management Asset strategy and performance Incident management Asset information collaboration Portfolio management Maintenance planning and scheduling Health and safety management Asset information governance Project management Maintenance execution Environment management Predictive maintenance and service Resource management Mobile asset management Management of change Project connectivity Maintenance safety and permit to work 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 5
Change Bimodal IT according to Gartner Systems of record versus systems of innovation + Mode 2 SAP Leonardo Systems of innovation SAP Integrated Business Planning Systems of differentiation Governance Mode 1 Systems of record + Source: Gartner 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 6
Envisioned logical architecture for SAP Enterprise Asset Management Strategic direction Run businesses Hyperautomate business processes Business intelligence Mode 1 Mode 2 SAP Integrated SAP Leonardo Business Planning SAP Ariba solutions PM SAP SuccessFactors solutions SAP Fieldglass solutions Master data EH&S SAP Digital Boardroom SAP Predictive Predictive Maintenance and Service Mobile Internet of Things SAP Asset Asset Intelligence Network SAP Asset Strategy and Performance Management STO Steer businesses Deliver insights to drive strategic decisions Differentiate businesses Intelligently connect people, things, and businesses Differentiate Business Embrace data Orchestrate data of any volume, velocity, and variety CO FI Data foundation PS MRS MM PLM Digital core SAP Leonardo IoT Foundation Machines Sensors OT Systems Edge 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 7
SAP Predictive Maintenance and Service From sensor to insight to outcome Sensor Data Insight Action Outcome Connected assets IT/OT convergence Data analysis Maintenance activities Business value Onboarding Connectivity Device management Security Big Data ingestion Big Data infrastructure Merging sensor data with business information Root cause analysis Asset health monitoring Machine learning Anomaly detection Triggering of corrective actions Prioritized maintenance and service activities Optimized warranty and spare parts management Prescriptive maintenance Customer experience Increased quality Lower costs Operational efficiency R&D effectiveness Material procurement Quality improvements 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 8
SAP Predictive Maintenance and Service How can information systems help? Remote monitoring Agile planning Alerts and actions SAP Leonardo IoT capabilities Engineering rules Life indicators Distance scoring Anomaly detection Connected assets and sensors OT systems (such as SCADA) Other data sources (such as weather) Digital core (SAP S/4HANA) 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 9
SAP Predictive Maintenance and Service Solution components and value drivers Actions Logistics and maintenance execution systems Business user Availability through the cloud or on premise Insights SAP Predictive Maintenance and Service Asset health control center Asset health fact sheet Insight provider catalog Domain expert Flexible extension concept to build industry- or customer-specific models and analytics Scalable machine learning engine that drives data science insights into our business processes Alerts Machine learning engine Data scientist Flexible visualizations across equipment structures Raw data SAP SAP Leonardo IoT Foundation SAP Leonardo SAP Leonardo for Edge IoT Edge Computing Data manager Comprehensive process integration: alert, discover, remedy Machine data Business data 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 10
SAP Asset Strategy and Performance Management Adding more efficiency by looking at the fleet 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 fewer failures and lower maintenance costs. Cost Run-to-failure Preventive (static) Run-to-failure Preventive (agile) Preventive (static) 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 11
SAP Asset Strategy and Performance Management (planned) End-to-end process enablement Benefits Increase overall asset availability Increase MTBF increase equipment reliability Improve utilization of assets Control maintenance spend Reduce work backlog Identify savings opportunities through preventive and predictive maintenance Reduce capital tied up in spare parts inventory Adopt a proactive and targeted maintenance strategy Change the sequence of the process using point apps Process innovation Classification of assets into groups Analyze systems and functions Asset criticality and risk assessment Perform RCM Collaborative failure modes library Analyze and optimize maintenance strategy Perform FMEA Project manager review Solution integration points SAP Asset Intelligence Network SAP Predictive Maintenance and Service SAP ERP application or SAP S/4HANA (PM, MM, FI/CO, PP functionalities) SAP Integrated Business Planning Perform RCA, ETA Bad actor management and spares optimization Integrations and extension points to 3 rd - party systems CAPEX / OPEX inputs to maintenance strategy 2017 SAP Leonardo Live. All rights reserved. I PUBLIC MTBF mean time between failures; FMEA failure mode and effects analysis; RCM reliability-centered maintenance; RCA root cause analysis This is the current state of planning and may be changed by SAP at any time. 12
Digital transformation in asset management driven by IoT, cloud, and business networks What does digital transformation mean for enterprise asset management? Connect to the asset Bring together information from operational and business systems (IT/OT convergence) Utilize the IoT for scaling transparency without neglecting existing information sources Predict asset system behavior Avoid unplanned downtime and major operational consequences through simulation and prediction Discover patterns of failure and preserve operational integrity Blend business IT information with operational (OT) data Share asset information and collaborate Activate the ecosystem of OEMs, EPCs, service providers, and operators Make sure there is one version of truth on asset master data Use a business network to enable integrated processes in the cloud 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 13
SAP solutions for the asset management line of business Where to find more information about our road map and innovation Road maps: http://www.sap.com/roadmaps Value maps: http://www.sap.com/solutionexplorer Innovation discovery: http://sapsupport.info/support-innovations/innovation-discovery/ 2017 SAP Leonardo Live. All rights reserved. I PUBLIC 14
Thank you. Dr. Achim Krüger Vice President, Line of Business Asset Management SAP SE Dietmar-Hopp-Allee 16 69190 Walldorf, Germany T +49 6227 7-60975 F +49 6227 78-38446 M +49 160 3603569 S +49 6227 7-49510 achim.krueger@sap.com More information: www.sap.com/eam www.sap.com/roadmaps www.sap.com/solutionexplorer http://scn.sap.com/community/eam
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Building the Smart Railroad SAP Leonardo Live event Jeroen De Roeck
THE BELGIAN RAIL INFRA MANAGER Key Figures Asset Management 10.176 Main Signals (31/12/2016) 1.751 Level Crossings (31/12/2016) 6.511 km of Main Track (31/12/2016) 5.905 km Overhead lines (31/12/2016) 7000+ Employees (31/12/2016) 11.769 Civil Engineering Constructions (31/12/2016) SAP objects >500.000 Functional locations >2.000.000 Pieces of equipment Spare Parts 270 M
STUPID Assets NOT CONNECTED SMART Assets CONNECTED 19
STUPID Assets NOT CONNECTED SMART Assets CONNECTED Sensors & Measure Trains Sensors & Intranet of Things 20
Digital Strategy Technicians & TABLETS Preventive/Condition/Predictive-based maintenance Preventive maintenance plans Incidents SAP PLANNER ASSET DATA 21
Infrabel Movie 22
0,8 0,02 0,2 0,38 0,56 0,74 0,92 1,1 1,28 1,46 1,64 1,82 2 2,18 2,36 2,54 2,72 2,9 3,08 3,26 3,44 3,62 3,8 3,98 4,16 4,34 4,52 4,7 4,88 5,06 5,24 5,42 5,6 5,78 5,96 First steps in IOT the PQUBE case EBP PQube Turnout motor 2 01/11/15 01:58:17.920 1,8 01/11/15 02:12:07.940 1,6 03/11/15 11:07:33.920 1,4 1,2 1 04/11/15 05:08:32.920 04/11/15 06:46:44.920 06/11/15 10:10:20.920 07/11/15 13:08:19.940 09/11/15 08:08:56.940 09/11/15 08:12:10.920 09/11/15 08:14:42.920 09/11/15 09:51:52.940 average EBP logbook Current Curves (1.000.000) 100 Turnouts SAP Enterprise Asset Management (SAP EAM) 23
0,02 0,2 0,38 0,56 0,74 0,92 1,1 1,28 1,46 1,64 1,82 2 2,18 2,36 2,54 2,72 2,9 3,08 3,26 3,44 3,62 3,8 3,98 4,16 4,34 4,52 4,7 4,88 5,06 5,24 5,42 5,6 5,78 5,96 First steps in IOT Goals with Pqube project Turnouts are more or less a blackbox Information currently available from distance Left or right In/out control Placing the Pqube sensor should increase the Visibility on the functioning of a turnout 2 1,8 1,6 01/11/15 01:58:17.920 01/11/15 02:12:07.940 03/11/15 11:07:33.920 Provide Data to examine behaviour 0,8 06/11/15 10:10:20.920 07/11/15 13:08:19.940 09/11/15 08:08:56.940 04/11/15 05:08:32.920 1,4 04/11/15 06:46:44.920 1,2 1 09/11/15 08:12:10.920 09/11/15 08:14:42.920 09/11/15 09:51:52.940 average 24
Data analytics from measurement to data Going from individual measurements to current curves Plot measurement values in time Business value: Current curves A correct image of the physical functioning of a turnout 25
Data analytics What we learned Each turnout has it own curve that evolves during time Each type of turnout has a typical curve It defines the DNA of the turnout 26
1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 Data analytics Define Models Determine algorithms to define normal behaviour Each unique turnout has it own characteristic evolution of the curve Curve: Difference for left or right movements Variation in current due to changes in mechanical properties 9 8 7 6 5 4 3 24/03/15 10:53:34.920 average 2 1 0 27
Data analytics Define Models Determine algorithms to define abnormal behaviour Abnormal current consumption Longer runtimes Larger deviations = malfunction Specific patterns ie. Bad alignment worm wheel 28
1 14 27 40 53 66 79 92 105 118 131 144 157 170 183 196 209 222 235 248 261 274 287 300 1 35 69 103 137 171 205 239 273 307 341 375 409 443 477 511 545 579 613 647 681 715 749 783 817 851 885 919 953 987 1021 Normalisation Data analytics Define Models Flatten out differences in timestamp between different systems Only keep reliable measurements 8 7 6 5 4 3 2 1 0 L2 Amp (A) L2 Amp (A) L2 Amp (A) L3 Amp (A) L3 Amp (A) L2 Amp (A) L2 Amp (A) L1 Amp (A) L3 Amp (A) L2 Amp (A) 8 7 6 5 4 3 2 21U R 01/01/15 10:19:22.920 21U R 01/01/15 12:49:56.940 21U R 01/01/15 14:49:15.940 21U R 01/01/15 18:48:53.940 21U R 02/01/15 07:48:17.920 1 0 29
Data analytics Define Models Building a predictive model Each movement gets a calculated number Number Anomaly Category 0.9762 normal 7.84222 failing F26 0.96101 normal 12.7684 failed f30 12.5305 failed f30 12.4308 failed f40 11.8506 failing f30 10.0133 failing f30 6.42567 failing f30 0.71324 normal 6.07957 failing f26 0.71649 normal 0.74115 normal 30
Data analytics Define Models Building a predictive model Each movement gets a calculated number Normal curve = 1 Early warning> 1,4 Includes false positives Warning >= 2 No false positives Failing >= 3,8 Failed >= 11,9 31
Data analytics Introduce AI technology 32
Data analytics Introduce AI technology Unsupervised learning Dimensionality reduction using neural networks Over Current Not locking Normal data 33
Pqube Project Business value Increase installed base: install Pqube on critical turnouts. Reduce cost of operations Avoid unnecessary activity in the field Better planning and preparation based on indications from predictive model Reduce unplanned downtime Reduction of unplanned downtime by early warning Number of false positives to be managed Prevent extended maintenance downtime due to unforeseen activities 34
Pqube Project Leverage IoT Business value Cost Starting from traditional Preventive Maintenance Strategy Low Risk High Cost Adapt preventive Maintenance strategy Cost of visits Risk Use Predictive model to lower the Risk Total cost of Predictive Model + Lower Maintenance strategy < Traditional Preventive Maintenance New balance between cost and risk Risk 35
Linear assets Singular Assets Next steps in making assets smart Increasing the business value 2017 2018 Turnout Measurement system Turnout Video Inspection system DB 500 Gb/year Video 100 Tb/year TVS Track Video System SGS Switch Geometry System 100 Tb/year 36
First steps in IoT What we learned Pqube case enabled us to: Learn and understand possibilities of IoT @ Infrabel In the beginning: a lot of manual work involved Unsupervised learning had the same result much faster Looking for additional IoT opportunities: Monitoring Control Unit Enhance old equipment with nonintrusive new technology 37
Next steps in IoT Monitoring Control Unit 38
Next steps in IoT Monitoring Control Unit MCU: Remote measurement/monitoring of: Batteries Current rectifier Active alerts Configuration parameters managed remotely Business Value: Fewer physical visits necessary. Visit takes 3,5 hours incl travel * 750 installations = 2625 hours saved Active control of parameters, limit human errors Early warning on battery issues 39
Next steps in IoT LED s tele-monitoring in RTF boxes using Computer Vision 40
Next steps in IoT Asset classification with Computer Vision State-of-the-art research and development at Infrabel 41
Asset Management information flow Measurement Data Asset owner (expert) + data scientist Reports Quicker Response Technician Intervention Live data Malfunction Incident Measurement database Model Verified measurement results Predictive notifications Higher Data Quality Asset Master data SAP EAM Assets checked by Measurement trains Less manual work Assets to be maintained 42
How to digest all the data Use of flexible reporting in SAP Lumira software Meaningful insight on combined data KPI models based on previous data analysis experience Tree maps and geographical visualisation to focus on the important stuff 43
How to digest all the data SAP EAM with GEOe support Visual way of working Quickly identifying assets that need maintenance Real time follow-up 44
How to digest all the data SAP EAM and Expert systems Visualising linked assets Connecting measurement information Display information on previous measurements All information available from the back-end system 45
How to digest all the data Mobile SAPUI5 apps Consult measurement history Perform checklists Access location-based observations by maintenance crews 46
POC for future architecture Separate engineering and production system Production used to plan and predict Engineering to develop predictive models and rules Proven models to be displayed in production system Only hot data in Production Warm data in Engineering based on extended Storage. Engineering System Larger time series Based on: SAP HANA platform SAP IQ software Hadoop SAP Data Services software Production System Actual time series Based on: SAP HANA platform SAP IQ software Hadoop SAP Data Services software 47
Increase step by step the maturity in IOT scenarios How we proceed: Start Small - Think Big Start building models Predictive models Anomaly detection Engineering rules Data filtering and alerting Build a business confidence level Automate the models with AI Interconnect with SAP EAM for preventive and corrective actions Start working on analytics and reporting part Set up organisations with data scientists Interconnect different disciplines Centralize all data in a Big Data engineering and production environment Integrate useful data in mobile apps for field workers Work with Geo-enabled videostreams, photos Smart visualisation and analytics Connect and centralize connected assets Make stupid assets smart with sensors Collect data even if you don t use it yet 48
Key Messages Bring in Predictive skills (AI knowledge) to get started Check the economic model for placing sensors Quality of sensor is crucial: avoid sensor failures Huge change on way of working: build confidence level first Easy and simple solutions can work. Maturity can increase step by step. 49
Thank you 50