Industry Transformation

with IBM Digital TwinEran Gery

CTO, Watson IoT Connected Products Solutions

1

Agenda

• Market Dynamics

• Digital Twin Strategy

• Focused Digital twin use cases

• CE high level roadmap

2

• 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

The market is changing… and so is IBM

Manage

Developmen

t

Analyze data to

informCognitive

Analytics

Gain insight from

products in

operation

Create new

business models,

services and

revenue streams

DeployManage

Requirement

s

Verify and

Validate

Design

System

Asset Management

Product Engineering

Powered by IBM Watson

Connected Products

Connected Operations

3

Product Lifecycle is a web of loosely connected data sources and

processes across enterprises

designs

manufacturing

operation

design

product management

maintenance

commissioning

customer relations

finance

supplier relationship

requirements

cost

Schedule

maintenance schedules, failure predictions

Quality, performance and new requirements feedback

Customer requirements feedback

New design & change request

compliance

cost

quality

cycle-time

profitability

responsiveness

impact

supply chain

Recent technology advancements provide new

ways to meet the challenges

Sensors and pervasive connectivity

Big data

Cognitive analytics

Cloud computing

Descriptive

Diagnostic

Predictive

Prescriptive

Cognitive

Reports past events

Assesses past outcomes

Identifies potential outcomes

Identifies (and may automate execution of)

optimal outcomes

Learning systems based on probabilistic reasoning

2010 2011/2 Today2015/6

Evolution of analytics

Build

Design

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

Better understanding of

design for rapid

development

Increased transparency

for more efficient

manufacturing

Empowered decision making

for optimized operation

Digital Twins: Key to differentiating Innovation

“Shifting Left”: Early Prototyping and Analysis, Customer feedback, Compliance, Quality, etc.

“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"

6

What if you could…

designs

manufacturing

operation

design

product management

maintenance

commissioning

customer relations

finance

supplier relationship

supply chain

Get real time alerts on quality issues and

guide me to the root cause?

Quickly engage with Engineering to resolve

issues… and… Have Engineering use our own

scenarios for their Dev&Test?

Work with Engineering and predictive models to be able to prioritize

true critical maintenance?

Discover new, well defined, duplicate free requirements that truly reflect market needs?

Quickly assess the impact of a design change on

cost, quality, manufacturability and

user experience?

7

So what make up Cognitive Digital Twin solutions?

IBM Cognitive Digital Twin solutions apply foundational capabilities built on key market differentiating enablers

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

Intelligent Experience

Digital Modeling Digital Thread

New user experience for cognitive data made available through natural language processing, augmented and virtual reality, lightweight apps &

existing channels

High performance, scalable, cognitive computing in-device, on-premise, hybrid and in the cloud

Are these all the

components we want

to talk about?

Reduce words?

L1

8

Knowledge GraphDigital Threads

IoT Integration

Intelligent Experience(cognitive interaction, visualize, query, analyze)

Enterprise Applications (ERP, EAM, CRM, MES, etc.)Functional, Logical,

Physical models,Simulations and Mockups

Cognitive Sensing Metric, feedbacks, Continuous Improvements

Digital Twin Solutions – A Component View

9

Digital Twin Architectural Perspective

Cognitive Digital Fabric

IoT Connected Services

Connectivity, Security, Storage, Big Data Platform, M/C Learning Foundation, Watson Hooks

Knowledge Graph, Relationship Discovery/Learning, Unstructured

Data Processing, Cause/Impact Threading,

Aggregation/Transformation, Discovery,

Digital Twin Use Cases

Requirements

Management

Change

ManagementImpact / Root-

Cause Analysis

Design to

Manufacturing

BOM/Process/Test

Prescriptive

MaintenanceQuality

Management

Design to Operations

Recommendations/

Fixes Etc.Compliance

Cognitive SensingRegistry for Sensors, Devices

& Analytics (Physics m/c

learning and cognitive models)

APIs for Applications

Physical

Assets

(Sensors,

Products &

Systems)

CE

Applications/

Processes

Manufacturing

Applications/

Process

PLM

Applications

EAM

Application/P

rocess

MRO

Applications/

Process

Models &

Simulations

IoT for XX

(Auto,

Electronics,

etc.)

IBM Watson Internet of Things 10

System & Software Design

Requirements Management

Data and Analysis

Quality ManagementTask & Change Management

Real-Time Testing

IBM lifecycle integration framework based on OSLC and Jazz

Electrical DesignProduct Lifecycle

ManagementProduct Line Engineering

Multi-domain Simulation

…

Lifecycle

LinksLifecycle

Data Graph

IoT Platform

Axel MauritzHead of Domain Virtual Product Engineering, Airbus Group Innovations

Genius of Things eventMunich, Feb 2017

Multisystem,multidisciplinary navigation

Understand impact of changes

Consistency between viewpoints

IBM material 11

IBM & Airbus pioneered digital twins & threads for complex systems in the European Crystal project

we want new ways of working

we want higher productivity

we want ways to handle complexity

we want interoperability

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

Cross functional team of managers, leaders, administrators and specialist working to evaluate alternative and propose a solution

Digital threads using industry standards e.g. dependency & change impact

Analytics e.g. live status and trend to target

Digital twinsusing industry standards e.g. system safety

& performance

Re-usable engineering methods

Re-usable web services

13

Prof. Dr. -Ing.Peter GutzmerDep CEO & CTO Schaeffler

Genius of Things eventMunich, 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

Market

Analytics

Digital twin usecases along the product lifecycle

Cognitive Requirement analysis

Enterprise Wide Analytics

Enterprise Change Management

Requirements to Model Construction

Cognitive Sensing

Operational data for requirements elicitation

Enterprise Quality Management

Customer

Requirements

System

Requirements

System

Design

Implementation

System

Test

System

Verifcation &

Validation

Deployment/

Release to Mfg.

Operations and

Maintenance

Continuous

Engineering

Electronical/

Electronics

Design

Mechanical

Design

Agile Software

Engineering

Concept

Design

MBSE

Multi DisciplinarySimulation

15

Agile Requirements Management

Operational data for Requirements analysis/elicitation

Requirements Quality Assessment

Detect similar and/or duplicate requirements

Automated lifecycle transition from requirements to design

Model Based Systems Engineering

OperationsField Engineer

ManufacturingIndustrial Engineer

EngineeringSystems Engineer

Re

qu

irem

en

ts

Pam

Product

Manager

Susan

Systems

Engineer

Frank

Field

Engineer

Albert

Industrial

Engineer

16

Scoring requirements quality with Watson NLU

• Watson NLU analyzes the

requirement text

• Watson NLU sends Requirement

Score, Entities and Relations

back

Watson

17

Device/Thing

models

Manufacturingmodels

Physical Models

Systems Engineering

Models

Asset

models

Cognitiveanalytics

CognitiveKnowledge Graph

Structured

Data

Intelligent Experiences

UnStructured

Data

• 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

Enterprise-Wide Analysis

Aras & IBM offer manufacturing companies full data

representation across the entire product lifecycle so they

can better manage the business of engineering

Predictive analytics(PMQ)

Aras: PLM & Business of Engineering (HW & SW)IBM CE (SW-focused)

IBM AM (Maximo)CAD PLM (HW-focused)

As designed As built As maintained

Digital Thread:Full Traceability

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

19

Multi-Disciplinary Simulation

Simulink Blocks

Model Physics of

Vehicle

• 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

Sensor Sensor Sensor Sensor Sensor Sensor Sensor Sensor

IBM Watson IoT For Connected Products/IBM Enterprise Asset Management

Analytics Run-Time Environment:

IBM Watson ML/DSX

Visual Inspection Acoustics Analytics Vibration Analytics

Predictive MaintenanceQEWS – Anomaly

DetectionEnergy Consumption -

Anomaly detection

Multi-Variant Anomaly

Detection

Mill advisor Process

OptimizationAsset Health – General

Formula

Structural Health -

Tunnels

Structural Health -

BridgesStructural Health -

Windtowers

Cognitive Sensing Catalogue

Cognitive SensingLeverage 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 3rd parties to offer their own

Analytics for Rapid Analytics

Deployment using DT SDK

21

High-level CE roadmap with representative items

2015 2016 2017 2018

IBM Confidential. Subject to change

Enterprise performance, scale CE with 50K+ users

DNG 2.5M reqs/server DNG 5M reqs DNG 50M reqs (target)

LQE scale outRTC Clustering++QuickPlanner for stand-ups

Agile tracking and planning at scale: Quick Planner & Scaled Agile Framework

Self-serve reporting with Report Builder RPE Document Builder and simplification

Business-driven development:

Agile / SAFe / Kanban /

reporting at scale

Simplicity for practitioners

Simplicity for administrators Simplify admin with containersQuick Deployer

User experience improvements (on-going)JMX MBeans for monitoring

UX consistencyMyStuff for program collab.

Strategic reuse in complex

product and systems dev

Global Configs with req, test, models, code

SCM for regulated and advanced component-based embedded software development

Fine-grained components

Rhapsody model serverPLE with feature modeling (w/BPs)

Enterprise securityOpen ID Connect

SCIMSAML

Digital Certs / Smartcards

Rich client ext.Jazz Authorization ServerSSOKerberos / SPNEGO Hidden fields in RTC Build

Digital twin and cognitive CEMVP2 – service basedIoT4CE

WatsonAnalytics4CEQuality Advisor Cognitive

Requirements

22

CE and IoT integration

IBM Bluemix

IoT Devices

In operation

IoT Platform• IoT platform device definitions

• (IoT devices and states)

• IoT platform rules

IBM IoT Continuous Engineering environment

OSLC

adapter

IoT Applications

(Node-RED)

Design Twin Operational Twin

IoT Predictive

Analytics

23

Cognitive Quality Advisor

• 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

Watson Data Analytics

24

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

25

Q & A

Q&A