SAP Leonardo: IoT based Big Data Scenarios with Predictive Analytics

Transcription

1 , Oslo, Norway SAP Leonardo: IoT based Big Data Scenarios with Predictive Analytics Kay Rohweder

2 Our Plan for the Next 40 minutes How does Predictive Analytics work? How can we scale up a solution? How could a SAP infrastructure look like? Question & Answers 2

3 IoT / BIG DATA / Predictive Analytics The Internet of Things (IoT) and Big Data enable enterprises to raise strategic and operational decisions to a new level by analysing big amounts of data ü ü ü Behavior of customers and markets Development of assets Impact on products and services Uncertainties exists about the transformation from Business Intelligence to Predictive Analytics: ü Specific information demands ü Heterogeneous system landscapes ü Lack of transparency about availability and data sources A company-specific technical and conceptual Predictive Strategy is needed to identify the added value and to enable the orchestration of business processes using Big Data 3

4 Example Case: Predictive Maintenance / Wind Industry Predictive Maintenance on Wind Power Stations as example case to illustrate advanced analytics with SAP High Importance of Maintenance Activities about 40% of total asset costs of a wind power station (WPS) service avoids expensive plant damage and production downtime High Importance of Planning Activities assets are hard to reach (esp. offshore assets) resources like technicians and spare parts need to be scheduled 4

5 Live Demo 5

6 How to scale this solution? SCADA (SENSOR DATA) SCADA (SENSOR DATA) + SAP PM Malfunction Reports Maintenance Plan ü Integration of additional data sources ü Usage of Data science for failure prediction ü Automation of the Learning process ü Visualization of upcoming failures

7 BUSINESS CASE Cost Reduction A Reduction of Maintenance Costs Postpone! Maintenance Interval A JAN FEB MAR APR MAY JUN JUL Extending productive life span of spare parts and bundling of maintenance actions maintenance costs per year roughly (with ½ year interval) Stretching this to a ¾ y interval could save up to /y (10y lifespan ) Asset Condition The usual preventive Maintenance Interval* is ½-year The asset would be serviced in short term But the condition of the asset still seems to be fine (only 2 out of 10 readings can not be explained) 100 7

8 BUSINESS CASE Breakdown Prevention B Prevention of Asset Breakdowns Priorize! Maintenance Interval B JAN FEB MAR APR MAY JUN JUL Forecasting and avoidance of complete breakdowns Delays due to weather and in spare part supply up to 9 weeks (Revenue per day up to 900 /d) Unrealized revenue 9x 7d x 900 /d = Assumed maintenance Cost Saving Potential: = Asset Condition The wind power station is in an early stage of the MI* Next Service is supposed in 6-9 month But something seems to be wrong with the asset (7 out of 10 readings can not be explained) 100 8

9 Live Demo 9

10 Architecture approach by the use of 10

11 SAP Leonardo Internet of Things (IoT) 11

12 Lambda-Architecture Source Data Speed Layer Update 1 Real time Processing Real time Views 3 New Data Query Processing Query Result Fast Data All Data Append All Data Batch Layer 2 Batch Processing Overwrite Batch Views Serving Layer 12

13 Possible Architecture using SAP SCP/ Leonardo portfolio Reporting SAP HANA Platform SAP BW on Hana Application: Combine + Analyse Sensor / Machine data Connect, query Stream (push) SAP ERP ECC Tables HANA Database Smart Data Streaming, MQTT Realtime Batch existing data flow new / optional data flow SAP VORA, SDA, Data Services 13

14 Summary Collect Check and harmonization of sensor data collection Supervising vs. controlling system Store On-Premise vs. Cloud vs. Hybrid Architecture Batch vs. Realtime processing Combine Data preparation Connection of sensor and business data Analyze Data Mining / Identification of correlations Derivation of models for prediction Apply Development of user orientierted Predictive Applications Integration of the results in the business processes 14

15 Thank you! Kay Rohweder Director, SAP BI & Analytics 15