Industry Transformation with IBM Digital Twin. Eran Gery CTO, Watson IoT Connected Products Solutions

Transcription

1 Industry Transformation with IBM Digital Twin Eran Gery CTO, Watson IoT Connected Products Solutions

2 Agenda Market Dynamics Digital Twin Strategy Focused Digital twin use cases CE high level roadmap 1

3 The market is changing and so is IBM Market Dynamics (across all industries) Innovation is the key to differentiation Technical Innovation to reduce development cost and increase differentiation Business Innovation to enable new sources of revenues Need to quickly respond to market and customer feedback along the full lifecycle As a result: Digital Twins/Threads becoming a key component of customers digital transformations Lean/Agile/Design Thinking becoming key innovation enablers Gain insight from products in operation Manage Requirement s Design System Connected Operations Analyze data to Asset inform Cognitive Management Analytics Powered by IBM Watson Manage Developmen t Product Engineering Connected Products Deploy Verify and Validate Create new business models, services and revenue streams 2

4 Product Lifecycle is a web of loosely connected data sources and processes across enterprises product management supplier relationship cost supply chain manufacturing quality commissioning Schedule requirements designs operation New design & change request profitability impact cycle-time Quality, performance and new requirements feedback design cost finance Customer requirements feedback customer relations compliance responsiveness maintenance schedules, failure predictions maintenance 3

5 Recent technology advancements provide new ways to meet the challenges Sensors and pervasive connectivity Big data Cognitive analytics Cloud computing Descriptive Reports past events Diagnostic Assesses past outcomes Predictive Identifies potential outcomes Prescriptive Identifies (and may automate execution of) optimal outcomes Evolution of analytics Cognitive Learning systems based on probabilistic reasoning /2 2015/6 Today

6 Digital Twins: Key to differentiating Innovation The Cognitive Digital Twin is the virtual, state-full representation of a physical object or system across its life-cycle (design, build, operate) using operational real-time data and other sources to enable understanding, learning, reasoning, and dynamically recalibrating for improved decision making" Design Better understanding of design for rapid development Build Increased transparency for more efficient manufacturing Operate A comprehensive set of capabilities and information models Enable shorter design cycle and speed up innovation Support change management and solution impact analysis Optimize asset operation & performance Leverage operational insights back to design Empowered decision making for optimized operation Shifting Left : Early Prototyping and Analysis, Customer feedback, Compliance, Quality, etc.

7 What if you could Discover new, well defined, duplicate free requirements that truly reflect market needs? product management supplier relationship supply chain manufacturing Get real time alerts on quality issues and guide me to the root cause? commissioning Quickly engage with Engineering to resolve issues and Have Engineering use our own scenarios for their Dev&Test? designs operation design customer relations Quickly assess the impact of a design change on cost, quality, manufacturability and user experience? finance Work with Engineering and predictive models to be able to prioritize true critical maintenance? maintenance 6

8 So what make up Cognitive Digital Twin solutions? L1 IBM Cognitive Digital Twin solutions apply foundational capabilities built on key market differentiating enablers Intelligent Experience New user experience for cognitive data made available through natural language processing, augmented and virtual reality, lightweight apps & existing channels Digital Modeling Digital Thread Are these all the components we want to talk about? Reduce words? Digital Modeling of physical objects and systems helps validate and experiment behavior, and understand cause-effect relationship of decisions across the entire life-cycle, to derive desired & eliminate undesired behaviour, without real-live prototyping Digital Thread is the traceable, digital flow of information within and across the boundaries between modules that helps each function to perform with enhanced effectiveness and efficiency, leading to more customer responsive and agile life-cycles Cognitive Computing & Cognitive Sensing High performance, scalable, cognitive computing in-device, on-premise, hybrid and in the cloud 7

9 Digital Twin Solutions A Component View Cognitive Sensing Intelligent Experience (cognitive interaction, visualize, query, analyze) Metric, feedbacks, Continuous Improvements IoT Integration Functional, Logical, Physical models, Simulations and Mockups Knowledge Graph Digital Threads Enterprise Applications (ERP, EAM, CRM, MES, etc.) 8

10 Digital Twin Architectural Perspective Digital Twin Use Cases Requirements Management Change Management Compliance Quality Management Impact / Root- Cause Analysis Design to Manufacturing BOM/Process/Test Design to Operations Recommendations/ Fixes Prescriptive Maintenance Etc. Cognitive Digital Fabric Knowledge Graph, Relationship Discovery/Learning, Unstructured Data Processing, Cause/Impact Threading, Aggregation/Transformation, Discovery, Cognitive Sensing Registry for Sensors, Devices & Analytics (Physics m/c learning and cognitive models) APIs for Applications IoT Connected Services Connectivity, Security, Storage, Big Data Platform, M/C Learning Foundation, Watson Hooks CE Applications/ Processes PLM Applications Manufacturing Applications/ Process EAM Application/P rocess MRO Applications/ Process Models & Simulations IoT for XX (Auto, Electronics, etc.) Physical Assets (Sensors, Products & Systems) 9

11 IBM lifecycle integration framework based on OSLC and Jazz System & Software Design Task & Change Management Quality Management Requirements Management Data and Analysis Lifecycle Links Lifecycle Data Graph Real-Time Testing Electrical Design Product Lifecycle Management Product Line Engineering Multi-domain Simulation IoT Platform IBM Watson Internet of Things 10

12 Multisystem, multidisciplinary navigation Axel Mauritz Head of Domain Virtual Product Engineering, Airbus Group Innovations Genius of Things event Munich, Feb 2017 Understand impact of changes Consistency between viewpoints IBM material 11

13 IBM & Airbus pioneered digital twins & threads for complex systems in the European Crystal project Safety critical system case study: Aircraft de-icing Aim is to remove or reduce ice on aircraft surfaces in a reasonable way efficiency, effectiveness, cost, weight, power, operational life, maintenance needs etc we want new ways of working we want higher productivity we want ways to handle complexity we want interoperability Re-usable engineering methods Re-usable web services Cross functional team of managers, leaders, administrators and specialist working to evaluate alternative and propose a solution Analytics e.g. live status and trend to target Digital threads using industry standards e.g. dependency & change impact Digital twins using industry standards e.g. system safety & performance

14 Prof. Dr. -Ing. Peter Gutzmer Dep CEO & CTO Schaeffler Genius of Things event Munich, Feb 2017 Full traceability across design, production, operation and service Optimization and extended services with cognitive algorithms Increased safety and higher speeds IBM material 13 13

15 Digital twin usecases along the product lifecycle Operational data for requirements elicitation Market Analytics Cognitive Requirement analysis Requirements to Model Construction MBSE Customer Requirements System Requirements Concept Design System Design Continuous Engineering System Test Deployment/ Release to Mfg. System Verifcation & Validation Operations and Maintenance Enterprise Quality Management Cognitive Sensing Multi Disciplinary Simulation Electronical/ Electronics Design Implementation Agile Software Engineering Mechanical Design Enterprise Change Management Enterprise Wide Analytics

16 Agile Requirements Management Pam Product Manager Susan Systems Engineer Albert Industrial Engineer Frank Field Engineer Operations Field Engineer Manufacturing Industrial Engineer Requirements Operational data for Requirements analysis/elicitation Requirements Quality Assessment Detect similar and/or duplicate requirements Engineering Systems Engineer Automated lifecycle transition from requirements to design Model Based Systems Engineering 15

17 Scoring requirements quality with Watson NLU Watson Watson NLU analyzes the requirement text Watson NLU sends Requirement Score, Entities and Relations back 16

18 Enterprise-Wide Analysis Impact analysis across the lifecycle and across silos Root cause analysis across the lifecycle, especially across silos, starting from Operations or failure data in the field and/or manufacturing back to design Demonstrating compliance with regulations (time consuming, expensive and incomplete) Cognitive insights from knowledge graph to improve decision making UnStructured Data Systems Engineering Models Asset models Device/Thing models Cognitive analytics Manufacturing models Physical Models Structured Data Intelligent Experiences Cognitive Knowledge Graph 17

19 Enterprise-Wide Change & Quality Management 1. Change Management Analyze, understand and communicate changes quickly across the entire supply chain as needed Identify all the parts of the designed system that may be affected by a change request Visualize relationship among systems & artifacts without manually tracing links across disparate toolsets 2. Orchestrating Quality Effectively leverage test automation tools (SIL/HIL, system, UAT/CAT, V&V,...) and enables orchestration and data aggregation Integrations with PLM QA functions to manage all QA activities/results Aras & IBM offer manufacturing companies full data representation across the entire product lifecycle so they can better manage the business of engineering As designed As built As maintained IBM CE (SW-focused) CAD PLM (HW-focused) Digital Thread: Full Traceability Aras: PLM & Business of Engineering (HW & SW) IBM AM (Maximo) Predictive analytics(pmq)

20 Multi-Disciplinary Simulation Orchestration of joint simulations Leverage SysML, FMI, and Modelica for joint simulations Manage complex simulation runs Leverage RQM and RMM for Simulation Management Leverage operational data for simulation Transforming data injested by IoT platform Simulink Blocks Model Physics of Vehicle 19

21 Cognitive Sensing Leverage cognitive sensing platform to analyze operational data Enable Engineers to quickly leverage a library of analytic models Enable Engineers to reuse and extend existing Analytics Enable 3 rd parties to offer their own Analytics for Rapid Analytics Deployment using DT SDK Visual Inspection Acoustics Analytics Vibration Analytics Predictive Maintenance Multi-Variant Anomaly Detection Structural Health - Tunnels Cognitive Sensing Catalogue QEWS Anomaly Detection Mill advisor Process Optimization Structural Health - Bridges Analytics Run-Time Environment: IBM Watson ML/DSX Energy Consumption - Anomaly detection Asset Health General Formula Structural Health - Windtowers IBM Watson IoT For Connected Products/IBM Enterprise Asset Management Sensor Sensor Sensor Sensor Sensor Sensor Sensor Sensor

22 High-level CE roadmap with representative items Digital twin and cognitive CE IoT4CE MVP2 service based WatsonAnalytics4CE Quality Advisor Cognitive Requirements Enterprise performance, scale Strategic reuse in complex product and systems dev Business-driven development: Agile / SAFe / Kanban / reporting at scale Enterprise security Simplicity for practitioners Simplicity for administrators DNG 2.5M reqs/server DNG 5M reqs DNG 50M reqs (target) CE with 50K+ users LQE scale out QuickPlanner for stand-ups RTC Clustering++ Global Configs with req, test, models, code Fine-grained components PLE with feature modeling (w/bps) Rhapsody model server SCM for regulated and advanced component-based embedded software development Agile tracking and planning at scale: Quick Planner & Scaled Agile Framework Self-serve reporting with Report Builder RPE Document Builder and simplification Open ID Connect Digital Certs / Smartcards Kerberos / SPNEGO Hidden fields in RTC Build SSO Jazz Authorization Server SAML SCIM Rich client ext. User experience improvements (on-going) UX consistency MyStuff for program collab. JMX MBeans for monitoring Quick Deployer Simplify admin with containers 21 IBM Confidential. Subject to change

23 CE and IoT integration Design Twin Operational Twin IBM IoT Continuous Engineering environment IBM Bluemix OSLC adapter IoT Predictive Analytics IoT Applications (Node-RED) IoT Platform IoT platform device definitions (IoT devices and states) IoT platform rules IoT Devices In operation 22

24 Cognitive Quality Advisor Watson Data Analytics Analyze tests which find defects or don t find defects Identify tests which should be run more frequently, or retired Maximize benefits; Find more code change defects Minimize overhead; Eliminate or restructure low value tests Leads to cost savings for customers 23 23

25 Summary Presented the IBM Watson IoT strategic framework for continuous engineering and connected products in the digital twin The goal is to further streamline product innovation, lifecycle agility, value and quality Digital twin expands and expands key CE themes: Product lifecycle end to end traceability and knowledge Model based engineering and continuous verification The digital twin framework adds two new dimensions Big data and cognitive analytics Leveraging product operational data based on cognitive sensing 24

26 Q&A Q & A 25