SAP Predictive Maintenance and Service - DDV.org

1 SAP Predictive Maintenance and Service Thomas Klyv . Senior Solution Engagement Manager Products and Innovation SAP SE. October 2016. %. 0011001. 1101001. Legal Disclaimer The information in this document 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 about SAP's strategy and possible future developments, directions, and functionality of products and/or platforms, are all subject to change and may be changed by SAP at any time for any reason without notice.

2 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 intentionally or grossly negligent. 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.

3 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 2. Purpose of today's session Establish an overview of SAPs solution and vision on Predictive Maintenance & services (PdMS). Understand the journey Running a Predictive Maintenance project requires huge efforts but wields equally big potentials Case of Trenitalia 2015 SAP SE or an SAP affiliate company. All rights reserved. *) OT = operational technology Customer 3. SAP Predictive Maintenance and Service solution From sensor to outcome Sensor Data Insight Action Outcome Connected assets IT/OT* Convergence Data analysis Maintenance activities Business Value Big Data ingestion Root cause analysis Prioritized Maintenance Customer experience Onboarding and Service activities Connectivity Big Data infrastructure Asset health monitoring Increased quality Optimized warranty Device management Merging sensor data Machine learning and spare parts Lower costs with business management Security Anomaly detection Operational efficiency information Prescriptive Triggering of corrective Maintenance R&D effectiveness Insight actions Action Quality improvements Material procurement 2015 SAP SE or an SAP affiliate company.

4 All rights reserved. *) OT = operational technology Customer 4. SAP Predictive Maintenance and Service Conceptual Architecture Operationalized Analytics & Data Science services Application Extensions Process Connected assets automation Operational analytics Devices, Business OT IoT IT. machines, OT (device) IT systems integration connectivity base services connectivity integration and (SAP S/4 HANA, sensors SAP CRM, and so on). Big Data platform: SAP HANA Platform 2015 SAP SE or an SAP affiliate company. All rights reserved. Public Customer 5. SAP Products and Capabilities How SAP Approaches Predictive Maintenance and Service Actions Operational System, PM, CRM, MRS, etc. Take specific actions utilizing existing business systems Business User /. Operational User Application Deliver insights to the domain Insights expert from interpreted Domain Expert signals Insight Providers (micro- services , operationalized analytics).

5 Derived Signals Translate the OT data into consumable signals custom custom custom Data Scientist /. Data Analyst Capture operations Data Management technology (OT) data through Raw Data Data Fusion Data Processing scalable and cost-effective Data Manager process 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 6. Predictive Maintenance and Service On-Premise Edition (PdMS OPE). Business Applications built on Modular Analytics Predictive Maintenance and Service On-Premise Edition IoT Applications Predictive Maintenance and Service Process Automation Asset Health Control Center (AHCC) Asset Health Fact Sheet (AHFS) Process Integration Closed-loop business Data Science services process Insight Provider Operationalized Connected integration Analytics and Data Science Assets 3D Anomaly into PM and Geo- Key Asset Work Derived Additional Distance- Detection Visualizat Remaining MRS.

6 services Devices, Spatial Figure Explorer Activities Signal Custom Based with machines, ion Useful Life Failure Principal Insight Insight Insight Insight Insight Insight Prediction sensors Insight Analysis Component Provider Provider Provider Provider Provider Provider Analysis Provider Integration possible with IoT Base services Insight Provider Runtime services Telit Product Insight Provider Data Science Modeling &. DeviceWise, Integration Lifecycle Management Scoring Data Management SAP PCo Big Data Platform SAP IQ SAP Predictive Analysis*. SAP HANA Enterprise SAP ESP*. Edition SAP Data services * SAP Lumira*. *Optional components 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 7. SAP Products and Capabilities Asset Health Control Center: Supervise Machine Health and Act on Issues Health status at a glance Health status of the complete fleet Aggregated from component health scores Based on out-of-the-box machine learning Derived Signals Management Personal and extensible Flexibly composed by insight providers Drill-down into 1 machine Integrated into operational processes close-loop integration for services via Multi-Resource Scheduling (MRS).

7 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 8. Maintenance Innovation in Context Railway Case Trenitalia at a Glance Fully owned by the FS Group, 100% controlled by the Ministry of Treasury Staff employees Fleet locos 112 EMUs (electro-trains). MUs (light trains). coaches and freight cars 498 shunting locos Passengers 38,6 Bn of passenger / km per year Tons 14,7 Bn of tons / km per year Trains / day Revenue M . 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 10. The Business Value of Maintenance Innovation Perform ALL the required interventions, and ONLY the required interventions, at the RIGHT TIME, ensuring availability of the RIGHT RESOURCES. Avoid any unnecessary activity Reduce Plan in advance and in detail for Costs of any intervention, ensuring >8-10% of direct savings Ground diagnostic Operations availability of spare parts, facilities, on impacted tools and trained resources Maintenance costs >5-8% increase in asset On board diagnostic availability Prevent breakdowns while trains >x% of reduction of Dynamic Maintenance Reduce are in operations breakdowns of trains in Management System Unplanned Prevent extended Maintenance operations Downtime downtime due to unforeseen activities 2015 SAP SE or an SAP affiliate company.

8 All rights reserved. Customer 11. The Problem with Preventative Maintenance Planning Work done by simple assets can be described by a simple indicator such as time of operations or Depth of Maintenance Activity units of throughput, but if we consider a complex machine like a train, the specific operational conditions have a huge impact on how its various components wear out These operational conditions are not known a priori by the manufacturer of the machine, and can change dramatically in the day-by-day work this creates the need of operating under 10K 20K 30K 40K 50K 60K 70K 80K 90K 100K. extremely conservative assumptions The only real value of these plans is that they are logistically very simple to execute and don't require much information 2015 SAP SE or an SAP affiliate company.

9 All rights reserved. Customer 12. Increase Planning Efficiency and Relevance With Life and Health Indicators Life Health Measure as precisely as possible the expected Takes into account the actual status of operation wear of the part by counting and projecting a set by measuring physical parameters ( closing of relevant parameter ( cycles, hours of time for a door, temperatures of cooling systems). operation, kilometers, energy etc.) or relevant combinations of indicators Maintenance is performed when predefined Maintenance is performed when the parameter thresholds are reached goes out of the normal range, indicating a probable deterioration of condition 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 13. Planning Engine Improving the Relevance of Maintenance Activities Planning based on Km / Time Planning based on Planning based on Life and of operations Life Indicators Health Indicators Current model, standard in the Based on more relevant drivers and Further increase the relevance of the plan, industry indicators that better represent the considering the future effects that the effective current and expected evolution of life indicators will have on the Easy to operate because all the ability of every component to perform.

10 And components in the material share usage of every single component on its risk of failure the same driver Increased precision is directly Requires sophisticated mathematical Sub-optimal for the same reason connected with the quality and methods to predict the behavioral patterns precision of the planning for the of health indicator based on the expected materials usage of the materials Requires optimization methods in Health indicators can in any case used to order to produce a plan, due to the trigger short term Maintenance activities fact that every component in a material can have different life situation 2015 SAP SE or an SAP affiliate company. All rights reserved. Customer 14. Life and Health Indicators in Action Detailed Information Telemetric readings on Infrastructure Operational plans for Rolling Stock Predicted Life and Health Indicators Life Ind.