SAP Predictive Maintenance and Service, on premise edition CUSTOMER

Transkript

1

2 Legal disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP s strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this document is not a commitment, promise or legal obligation to deliver any material, code, or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP s willful misconduct or gross negligence. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. 2

3 Agenda Introducing the SAP Leonardo IoT Portfolio Benefits Across the Maintenance Program SAP Predictive Maintenance and Service Overview Asset Visualization Details Machine Learning Engine 3

4 Introducing the SAP Leonardo IoT Portfolio Benefits Across the Maintenance Program SAP Predictive Maintenance and Service Overview Asset Visualization Details Machine Learning Engine

5 Introducing the SAP Leonardo IoT Portfolio Business Process Integration Social & Collaboration Big Data Business Models Intelligently connecting People, Things and Processes Artificial Intelligence and Machine Learning APIs & Microservices Natural Language Assets & Machines Business Applications 5

6 Introducing the SAP Leonardo IoT Portfolio SAP Leonardo IoT Bridge Enterprise Management The Digital Core Connected Products Connected Assets Connected Fleet Connected Infrastructure Connected Markets Connected People Product Insights Goods and Equipment Fixed Asset Insights Manufacturing Execution Mobile Asset Insights Logistics Safety Building Insights Construction Market Insights Rural Areas People and Work People and Health Supply Networks Manufacturing Networks Logistics Networks Energy Grids Urban Areas People and Homes SAP Leonardo IoT Edge SAP Leonardo IoT Foundation SAP Cloud Platform / SAP HANA Platform Things / Physical Layer 6

7 Introducing the SAP Leonardo IoT Portfolio Connected Assets Fixed Asset Insights Optimize asset performance throughout the entire lifecycle and build an asset network collaboration for better service and maintenance process As companies move from a reactive to a proactive approach to maintenance, fixed asset insights provides an end-to-end solution for predictive maintenance and service from identification of emerging issues to procuring spare parts, scheduling and executing maintenance. These capabilities can serve assets that are both owned and operated by companies as well as assets that are installed at a customer site and are covered by service contracts. This can be achieved through an integrated asset network to collaborate between manufacturers, service providers and end customers. SOLUTION SPOTLIGHT SAP Predictive Maintenance and Service SAP Asset Intelligence Network SAP Plant Maintenance 7

8 Introducing the SAP Leonardo IoT Portfolio Benefits Across the Maintenance Program SAP Predictive Maintenance and Service Overview Asset Visualization Details Machine Learning Engine Customers Roadmap

9 Asset Condition Multiple Approaches to Predictive Maintenance IT driven approaches are on the rise IT (data science & rules) Driven Equipment Driven Human Driven More time to respond enables greater flexibility to dynamically plan maintenance events P P Potential Failure X-ray Radiography Battery Impedance Test Oil Analysis Hot to Touch P Why more IT driven approaches? IIoT/device connectivity Big data available for training models Declining hardware and software costs Massive computing power Audible Noise F Functional Failure P Potential Failure = First Indication of Failure Time Ancillary Damage T Total Failure 9

10 Technology is changing our approach to maintenance *Use of Maintenance Strategy Today Run to Failure Preventative Predictive *Use of Maintenance Strategy Future Run to Failure Preventative Predictive The Internet of Things is leading to increased use of predictive maintenance Although still relevant, preventative maintenance typically results in over-maintaining assets and high cost The goal is to enable more IT driven (data science & rules driven) approaches to predictive maintenance in order to reduce unplanned failures and the number of overall maintenance actions *Proportion of maintenance strategies are for illustration purposes only and will vary based on many factors 10

11 Asset Health Distribution Two areas of focus drive business value Greatest ROI potential 2. Dynamic decision making for the healthy population of assets Healthy Unhealthy 1. Alerting for rare events 11

12 The Internet of Things Benefits the Entire Maintenance Program Run to Failure Preventative Predictive Replacements driven by more logical utilization rates WHEELS & BRAKES Energy Dissipation versus Mileage Preventive Drive scheduled maintenance based on the right utilization metric Rules driven alerting and health indicators BEARINGS Predictive Rules driven Automated and real-time condition monitoring based on rules engine Data science driven alerting and health indicators BATTERY Installed battery = Normal battery Predictive - Data Science Driven Automated and real-time condition monitoring based on machine learning The Internet of Things improves existing strategies and enables new data science & rules driven approaches to maintenance 12

13 Customer Example Train Operator * Run to Failure Preventative Predictive Company Owns and operates a fleet of around 2,000 electro-trains, 2,000 locomotives and 30,000 coaches and wagons 40% of maintenance is currently reactive * The maintenance strategy proportions are for illustration purposes only and not reflective of actual customer percentages 13

14 Customer Example Train Operator Run to Failure Preventative Predictive Solution Improve effectiveness of maintenance programs Data fusion between IT and OT data Remote train diagnostics Improved Program Effectiveness Starting with Improvements to Preventative Maintenance Plans BRAKES Energy Dissipation versus Mileage Benefits DOORS Open/Closure Cycles & Times versus Mileage Engineering rules and predictive models Dynamic planning of maintenance schedules Higher asset availability & passenger satisfaction Projecting 100M Euro savings per year in maintenance operations costs when fully implemented 14

15 Introducing the SAP Leonardo IoT Portfolio Benefits Across the Maintenance Program SAP Predictive Maintenance and Service Overview Asset Visualization Details Machine Learning Engine

16 Decision support across the ecosystem & asset lifecycle DESIGN PURCHASE SUPPORT BUILD DISPOSE OPERATE & MAINTAIN OEM Dealer Service Provider Owner/Operator R&D Procurement Production Aftermarket Sales Service Fleet Operator Improve asset reliability and up-time Monitor quality of purchased components Improve manufacturing processes Comply with contract service level agreements Increase customer satisfaction and loyalty Deliver the value added service at the right price Decrease maintenance costs Increase asset up-time Decision support to ALERT, DISCOVER AND REMEDY Machine Data Business Data Combining IT & OT data gives machine data context 16

17 Decision support across the ecosystem & asset lifecycle DESIGN PURCHASE SUPPORT BUILD DISPOSE OPERATE & MAINTAIN OEM Dealer Service Provider Owner/Operator R&D Procurement Production Aftermarket Sales Service Fleet Operator Improved service profitability OEMs and 3 rd party service providers can offer high margin services with reduced risk through increased visibility of fielded assets. New business models OEMs are now able to offer new service business models such as full-service agreements or payper-use. Reduced warranty cost Service, manufacturing and design engineers can reduce warranty costs through increased visibility of fielded assets. Increased first-visit fix rate Service Managers can identify the required skills and spare parts prior to arriving at the job site. Reduced maintenance cost Maintenance schedules can now be dynamically planned and packaged to better utilize resources and scheduled asset downtime. Increased asset availability With the use of data science, asset operators or service providers can predict failures early and implement corrective actions to avoid unplanned downtime. 17

18 Solution components and value drivers Actions Logistics & Maintenance Execution Systems Business User Enables a data science driven approach to condition monitoring Insights SAP Predictive Maintenance and Service Details Domain Expert Flexible extension concept for customers to build industry or customer specific models and analytics Alerts Machine Learning Engine Data Scientist A scalable Machine Learning Engine that drives data science insights into our business processes Raw Data SAP SAP Leonardo IoT Foundation SAP Leonardo SAP Leonardo for Edge IoT Edge Computing Machine Data Business Data Data Manager Flexible visualizations across equipment structures End-to-end process integration Alert, Discover, Remedy 18

19 System and component level visualizations Logistics & Maintenance Execution Systems SAP Predictive Maintenance and Service Details Machine Learning Engine Details SAP Leonardo Foundation SAP Leonardo for Edge Computing Machine Data Business Data 19

20 * Health Status Overview is an example of a custom Analysis Tool built using SDK 20

21 Locations Eagle Ford Field New Orleans Refinery Locations 21

22 Locations Filter by Locations Eagle Ford Field New Orleans Refinery Locations Filter by Location 22

23 Locations Eagle Ford Field Filter by Locations New Orleans Refinery Locations Filter by Location 23

24 Locations Filter by Location 24

25 Key Figures Analysis Tool Locations Filter by Location Analysis Tool 25

26 Equipment List Analysis Tool Locations Filter by Location Analysis Tool 26

27 Equipment Scores Analysis Tool Locations Filter by Location Analysis Tool 27

28 Map Analysis Tool Locations Filter by Location Analysis Tool 28

29 3D Chart Analysis Tool Locations Filter by Location Analysis Tool 29

30 Custom Analysis Tool * Health Status Overview is an example of a custom Analysis Tool built using SDK Locations Filter by Location Analysis Tool 30

31 Details Details Equipment View Serial #12345 Equipment View 31

32 Details Details Equipment View Serial #12345 Equipment View 32

33 Details Details Equipment View 33

34 Details Alerts Analysis Tool Details Equipment View Analysis Tool 34

35 Details 2D Chart Analysis Tool Details Equipment View Analysis Tool 35

36 Details Work Activities Analysis Tool Details Equipment View Analysis Tool 36

37 Introducing the SAP Leonardo IoT Portfolio Benefits Across the Maintenance Program SAP Predictive Maintenance and Service Overview Asset Visualization Details Machine Learning Engine

38 Machine learning challenges High dimensional data SOLUTION Feature construction/selection requires data scientists & domain user collaboration No labeled failure data Anomaly detection and reinforcement through user feedback Rare failure events Failure prediction using ensemble learning Outdated models, human scale Model management, continuous learning and scoring Use case specific algorithms Extensibility and integration of new algorithms 38

39 Machine Learning Engine Logistics & Maintenance Execution Systems Failure Prediction SAP Predictive Maintenance and Service Details Trigger prediction when algorithm detects a specific combination of input variables Machine Learning Engine New Algorithms Extensibility Reinforcement* Domain expert feedback Model Management Tools SAP Leonardo Foundation SAP Leonardo for Edge Computing Anomaly Detection Machine Data Business Data Trigger anomaly alert when the algorithm detects an abnormal pattern Continuous Improvement & Learning *Roadmap Item 39

40 Machine Learning Engine Supervised learning enables failure predictions like Remaining Useful Life Finds contributing factors to failure events Expert feedback Models change as operational environment changes Extensibility for out-of-the-box algorithms Possibilities to deploy new R based algorithms Unsupervised learning detects anomalies Enables Health Scores New Algorithms Extensibility Failure Prediction Trigger prediction when algorithm detects a specific combination of input variables Reinforcement* Domain expert feedback Anomaly Detection Trigger anomaly alert when the algorithm detects an abnormal pattern Model Management Tools Continuous Improvement & Learning *Roadmap Item 40

41 Machine Learning Engine - Model management Machine learning models are automatically applied to new incoming data Models are regularly re-trained using scheduling capabilities Model management capabilities allows us to maintain model versions Retrain model Configure model Train model Score model Model Configuration Model Version Scores Deactivate 41

42 Extending the machine learning engine 1. Write R Package 2. Install Package 3. Maintain Metadata 4. Configure MLE PropertySetType PropertyName1::Type PropertyName2::Type PropertyName3::Type Wrap custom algorithm in R package Install the package on the R Server Create metadata for health score created by the new algorithm Register the package at the MLE Export training and scoring function Start using the new algorithm for model creation, training and scoring! 42

43 Operational Data Ingestion with SAP Integration with SAP MII and PCo Safety Control DCS PLC/SCADA Data Historians SAP MII SAP PCo Devices Advance Control Real Time On-demand, Near Realtime Plant Databases e.g. OPC SAP Predictive Maintenance and Service Manual Data SAP Data Services Batch Offline files MII = Manufacturing Intelligence and Integration PCo = Plant Connectivity 43

44 2017 SAP SE oder ein SAP-Konzernunternehmen. Alle Rechte vorbehalten. Weitergabe und Vervielfältigung dieser Publikation oder von Teilen daraus sind, zu welchem Zweck und in welcher Form auch immer, ohne die ausdrückliche schriftliche Genehmigung durch SAP SE oder ein SAP-Konzernunternehmen nicht gestattet. In dieser Publikation enthaltene Informationen können ohne vorherige Ankündigung geändert werden. Die von SAP SE oder deren Vertriebsfirmen angebotenen Softwareprodukte können Softwarekomponenten auch anderer Softwarehersteller enthalten. Produkte können länderspezifische Unterschiede aufweisen. Die vorliegenden Unterlagen werden von der SAP SE oder einem SAP-Konzernunternehmen bereitgestellt und dienen ausschließlich zu Informationszwecken. Die SAP SE oder ihre Konzernunternehmen übernehmen keinerlei Haftung oder Gewährleistung für Fehler oder Unvollständigkeiten in dieser Publikation. Die SAP SE oder ein SAP-Konzernunternehmen steht lediglich für Produkte und Dienstleistungen nach der Maßgabe ein, die in der Vereinbarung über die jeweiligen Produkte und Dienstleistungen ausdrücklich geregelt ist. Keine der hierin enthaltenen Informationen ist als zusätzliche Garantie zu interpretieren. Insbesondere sind die SAP SE oder ihre Konzernunternehmen in keiner Weise verpflichtet, in dieser Publikation oder einer zugehörigen Präsentation dargestellte Geschäftsabläufe zu verfolgen oder hierin wiedergegebene Funktionen zu entwickeln oder zu veröffentlichen. Diese Publikation oder eine zugehörige Präsentation, die Strategie und etwaige künftige Entwicklungen, Produkte und/oder Plattformen der SAP SE oder ihrer Konzernunternehmen können von der SAP SE oder ihren Konzernunternehmen jederzeit und ohne Angabe von Gründen unangekündigt geändert werden. Die in dieser Publikation enthaltenen Informationen stellen keine Zusage, kein Versprechen und keine rechtliche Verpflichtung zur Lieferung von Material, Code oder Funktionen dar. Sämtliche vorausschauenden Aussagen unterliegen unterschiedlichen Risiken und Unsicherheiten, durch die die tatsächlichen Ergebnisse von den Erwartungen abweichen können. Dem Leser wird empfohlen, diesen vorausschauenden Aussagen kein übertriebenes Vertrauen zu schenken und sich bei Kaufentscheidungen nicht auf sie zu stützen. SAP und andere in diesem Dokument erwähnte Produkte und Dienstleistungen von SAP sowie die dazugehörigen Logos sind Marken oder eingetragene Marken der SAP SE (oder von einem SAP-Konzernunternehmen) in Deutschland und verschiedenen anderen Ländern weltweit. Alle anderen Namen von Produkten und Dienstleistungen sind Marken der jeweiligen Firmen. Zusätzliche Informationen zur Marke und Vermerke finden Sie auf der Seite

45 2017 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. The information contained herein may be changed without prior notice. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE s or its affiliated companies strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, and they should not be relied upon in making purchasing decisions. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. All other product and service names mentioned are the trademarks of their respective companies. See for additional trademark information and notices.