TOOLS AND METHODOLOGY FOR DEVOPS

Transcription

1 TOOLS AND METHODOLOGY FOR DEVOPS Christoph Braeuchle Senior Director, Product Management Nov 22, 2017

2 AGENDA 1. Introduction: PLM 720 and DevOps 2. Tool Architecture for DevOps in Smart Connected Systems Engineering 3. Case Study 1: e.go Life, Electric Car 4. Case Study 2: Enterprise Software 2

3 INTRODUCTION 3

4 PHYSICAL WORLD PHYSICAL THING/ENVIRONMENT Smart Manufacturing (Industry 4.0) 3D Printing IOT & Analytics Augmented Reality Virtual Reality Create CAD / PLM / ALM Operate & Service DIGITAL TWIN DIGITAL WORLD 4

5 360 DEGREES OF THE LIFECYCLE 360 o Service Engineering Operations Manufacturing Sales 5

6 360 DEGREES OF SOURCED DATA 360 o CRM PLC/IoT Service ERP Engineering ALM/PLM Etc. 360 o CAD Operations Manufacturing Sales 6

7 PLM o Service Engineering Etc. 360 o ERP Operations CAD Manufacturing CRM Sales ALM/PLM PLC/IoT 7

8 #1 VALUE DRIVER: EMBEDDED SOFTWARE AND CONNECTIVITY Technology: Functional Innovation largely independent from Hardware Fast innovation cycles in software Hardware changes require visit in repair shop Typically ~90% of functionality is controlled by software Business: Increasing willingness to pay for software-controlled functionality in cars Opportunity to proactively influence customer loyalty Limited profitability in hardware (commoditization?), significant margins in Software sold on top Market expectation and competition Ubiquitous connectivity App Stores and online-marketplaces drive consumer habits Consumers don t want to wait for the latest features 8

9 TREND TOWARDS AGILE, BUT ALIGNED PRODUCT PROCESS Specification to Validation Sequential Stage-Gate Process ME/EE/SW Decomposition and Integration Integrated Verification and Release Short iterations High velocity incremental improvements Fail-fast, improve immediately Immediate feedback from operations Automation wherever possible Code Generation Build, Test Release and Deployment (over-the-air) Data collection and analysis 9

10 INFRASTRUCTURE FOR DEVOPS 10

11 DATA MANAGEMENT AND ANALYTICS Manage Engineering Data Requirements and Models Software Configurations E-BOM and CAD Data System Configurations Store Big Data and Diagnostic Data Driving metrics Usage profiles Fleet Data Analyze and relate information Standard patterns Expected behavior (validation) Anomalies Improvement areas Data Management and Analytics 11

12 AUTOMATION AND ORCHESTRATION Control and automate engineering workflows Orchestrate information exchange and cross-domain collaboration Solution Readiness Change Control Automatic Build Test Release Automated Deployment over-the-air Data Collection, Closed-loop Feedback for planning Automation and Orchestration Data Management and Analytics 12

13 USER INTERACTION AND DATA VISUALIZATION engineering functionality (Coding, R&V, CAD) Role-based applications for purpose-oriented interaction Only Relevant information in actual work environment Augmented Reality Superimpose digital data on physical objects Put information directly into application context Combined mashups for data visualization User Interaction and Data Visualization Automation and Orchestration Data Management and Analytics 13

14 OVERALL ARCHITECTURE eplm REST API SE SCC M API Smart Data Big Data API REST API API Update Planning + Dispatching Software Catalog Vehicle Inventory + Validation Delta Packages Rules and Requests Configuration Ack/Error Inventory Data Usage Data Update Coordination + Execution Data Collection 14

15 CASE STUDY 1: ELECTRIC CAR E.GO LIFE 15

16 Source: e.go Mobile AG Requirements and Test Management Change Management Digital Product Definition Secure Access Validation Results based on Connected Drive Data Validation Results based on Simulation Data Requirements Traceable SysML Model Structural and Functional Specification Bill of Materials Management Workflow / Approvals CAD Design Edge-Device Code in e.go Life Telematic Unit Edge Device Code Generation Virtual System Simulations Launch Model Simulation Digital Twin Generation in IoT Platform Simulationresults for combined visualization Connected Drive Data for Prototype-in-the-loop Visualization Test-Drive with Data analytics and Simulation in-the-loop 16

17 Source: e.go Mobile AG Requirements and Test Management Change Management Design for Connectivity Secure Access Validation Results based on Connected Drive Data Validation Results based on Simulation Data Requirements Traceable SysML Model Structural and Functional Specification Bill of Materials Management Workflow / Approvals CAD Design Edge-Device Code in e.go Life Telematic Unit Edge Device Code Generation Virtual System Simulations Launch Model Simulation Digital Twin Generation in IoT Platform Simulationresults for combined visualization Connected Drive Data for Prototype-in-the-loop Visualization Test-Drive with Data analytics and Simulation in-the-loop 17

18 Source: e.go Mobile AG Requirements and Test Management Change Management Outcomebased Design Secure Access Validation Results based on Connected Drive Data Validation Results based on Simulation Data Requirements Traceable SysML Model Structural and Functional Specification Bill of Materials Management Workflow / Approvals CAD Design Edge-Device Code in e.go Life Telematic Unit Edge Device Code Generation Virtual System Simulations Launch Model Simulation Digital Twin Generation in IoT Platform Simulationresults for combined visualization Connected Drive Data for Prototype-in-the-loop Visualization Test-Drive with Data analytics and Simulation in-the-loop 18

19 PHYSICAL PHYSICAL PHYSICAL Digital 19

20 CHALLENGES AND APPROACHES Area Challenge Approach Connectivity Network coverage Data collector transmits packages when bandwidth is sufficient Data Privacy protection Collaboration Integration Too much data on CAN- Bus Private Users don t want to share information Alignment and integratino across engineering disciplines HW- and Platform Development needs longer cycles Edge device code with analytics on Telematic Unit. Observe and transmit only relevant information (e.g. anomalies) Focus on fleets: DHL, nursing services, etc. Seamless Connectivity and automation within the toolchain Use DevOps monitoring and planning data to inform HW- and platform development 20

21 CASE STUDY 2: ENTERPRISE APPLICATION 21

22 PERFORMANCE ADVISOR FOR INTEGRITY LM Online dashboard viewing, reporting and trend analysis Accessible through esupport Portal visibility into hardware and software environment performance and usage metrics Monitoring and reporting of the health of the environment Comprehensive visibility into software versions Proactively provide approved solutions to identified issues 22

23 CHALLENGES AND RECOMMENDATIONS Area Challenge Recommendation Customer Reservation to allow the system to call home Move to Cloud Operations. Data Integration Platform General Data Protection Guideline Concern about frequent updates Can t have implement bigger changes in platform in short cycles Anonymize data Minor improvements monthly, larger updates every 6 Months. Separate UI from Platform and backend 23

24 BENEFITS - DEVOPS FOR SOFTWARE INTENSIVE SYSTEMS Close alignment with business objectives Maximize productivity and efficiency Fast-track to new business models Optimized value for customer 24

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