Digitisation - Zentrum Digitalisierung.BayernJune 30th, 2016 Ralf Bucksch Business Unit Executive...

June 30th, 2016

Ralf BuckschBusiness Unit Executive Technical Sales

ZD.B I Zentrum Digitalisierung.BayernIndustrie 4.0 - Business in the Internet of Things

ZD.B I Zentrum Digitalisierung.Bayern Augsburg June 2016

© 2016 IBM Corporation2

DigitisationHype, Trend or Reality?

John Cohn, Fellow at IBM, https://en.wikipedia.org/wiki/John_Cohn



Basic Principles for Industrie 4.0■ Manufacturer independent integration ■ Decreased effort of integration and control ■ Green production■ Standardisation of production:

IT technology vs special field-bus■ Connectivity from idea to recycling ■ Customer individual, lot-size 1■ Realtime, event based control ■ Product controlled manufacturing



Source: Frankfurter Allgemeine Zeitung July 21st 2015

Digitisation from a different perspective

88%Customer Loyalty

61%Business Model

50%Products and Services

Advanced predictive analytics analy

Real-time sensors

Explosion of mobile devices

Explobil

Enterprise Social MediaEnterprise SocialInfrastructure optimisation(cloud computing)

Infrastructure opt

Cyber securityCyb



Mission CriticalSystems

YESTERDAY TODAY, INNOVATION IS DISCRETE AND DISPARATE EMERGENT, INTEGRATED SYSTEMS FOR ENTERPRISE INNOVATION

Innovative CoreOperations

Cloud

Social

Big Data & Analytics

Mobile

Security

Systems of Insight

Enterprise Innovation

Systems of Record

Systems of Engagement

Systems of Record

Systems of Insight

Systems of Engagement

Pervasive Security Intelligence

Enabled by Cloud

Enterprise Innovation

Enterprise InnovationIntegration of new era- and core systems

40%of IT hires are recruited

primarily for business skills. (IDC)

60%of IT spend is funded byline of business. (IDC)

80%of IT initiatives will be decided by line

of business by 2016. (IDC)



Collaboration

(embedded)Development

Mobile

Cloud

Security

Horizontal IntegrationValue Networks

Big Data & Analytics

verticalIntegration

Internet of Things and M2M

INTELLIGENT

INTERCONNECTED

INSTRUMENTED

Rules, Events,mathematical Optimisation

The digital advantage:

9% Revenue 26% GP 8% Corporate Value

Source: MIT Sloan Study

Industrie 4.0New business models - innovative and disruptive



iTunes and iPodDigitised content platform

Fitbit vs. WeightWatchersWearable / ‘Internet of Things’ platform

Uber airbnbMaterial goods and services platforms

Innovation and Disruption



Quality

Time

Minimum customer need

Sustaining in

novation

Disruptive

innovation

Source Sogeti

Rome 2005

Rome 2013

Disrupt or be disrupted



80 million wearable health devices will be available by 2017.

2.5 quintillion bytes of datagenerated daily by connected machines.

There will be 28 times more sensor-enabled devices than peopleby the year 2020.

25 gigabytes of data per hour is generated by a connected car.90% of cars will be connected by 2020.

153 exabytesof healthcare data generated by devices in 2013.

Increasing to 2,314 exabytes in 2020.

1.7 megabytes of data per second generated by every human being on the planet by 2020.

The Data Challenge



DataData curation leads

to optimal value

Compute must move to data

New IT platforms will emerge at the edge

Data attract more data

https://www.youtube.com/watch?v=rpsjxnoNc-U

Health datawill grow

99%

Insurancedata will grow

94%

Utilities datawill grow

99%

Manufacturing data will grow

99%

88%unstructured.

84%unstructured.

84%unstructured.

82%unstructured.

3x2017

90% 60%



Weather Land

Un-curated Diverse sources, resolutions, gaps, data types, update frequencies and uncertainty levels

Curated Creates an integrated up-to-date view of layered spatio-temporal data with industry-specific analytics

Roads Soil



Understand

Reason

Learn

Adapt and make sense of all data; “read” text, “see” images and “hear” natural speech with context

Interpret information, organise it and offer explanations of what it means, with rationale for the conclusions

Accumulate data and derive insight at every interaction, perpetually

The era of Cognitive Business



Analytics Cognitive

▪ Addresses predefined problems

▪ Addresses ambiguous problems

▪ Provides accurate and definitive answers

▪ Provides answers with a margin of error

▪ Handles information with known semantics

▪ Interacts in natural language with humans

▪ Interacts in formal digital means (e.g. commands, screens) with humans

▪ Handles information without explicitly knowing semantics

+

The difference of Analytics and Cognitive



Before 2005

Closed and centralised IoT networks

Today

Open access IoT networks, centralised cloud

2025 and beyond

Open access IoT networks, distributed cloud

IoT will evolve to be more open and decentralised


© 2016 IBM CorporationBBB15

IoT in the Blockchain

https://discover-iot-blockchain.eu-gb.mybluemix.net/iot-blockchain.html



AdvancedImage Recognition

Personality Analytics

Symbiotic CognitivePlatforms

Neurosynaptic Brain-inspired Computing

Advanced Dialogue & Reasoning

Exascale Data-centric Systems

Quantum Computing

HybridCloud

PhysicalAnalytics

Curationat Scale

Personalized Learning

0-18 months 18 months - 3 years 3-10+ years

“Ad Hoc” Systems

Leading in the next era of computing

© 2016 IBM Corporation

Lead! Don’t follow.

IBM Deutschland GmbH Wilhelm-Fay-Strasse 30-3465936 Frankfurt

Mobile +49 170 4532 [email protected]

Ralf BuckschBusiness Unit Executive Technical SalesIBM DACH

https://www.youtube.com/watch?v=Twm-hxU-nko



Human brain:100 billion neutrons100 trillion synapses

About 176,000 times more efficient than a modern CPU running the same brain-like workload.



Counter-partyrecords Bank records

Party C’s Records Auditor records

Party B Records

Party A’s Records

API-integrations

Incident

Ledger

Ledger

Ledger

Ledger

Ledger

Ledger

Blockchain explained

Problem - Difficult to monitor asset ownership and transfers in a trusted business network

Inefficient, expensive, vulnerable


© 2016 IBM Corporation20Consensus, provenance, immutability, finality

Counter-partyrecords Bank records

Party C’s Records Auditor records

Party B Records

Party A’s Records

Ledger

Ledger

Ledger

Ledger

Ledger

Ledger

dsds

LedgerLedgerr

Participants have multiple shared

ledgers

NOTE : Participants same

as before

Solution – shared, replicated, permissioned ledger

Blockchain explained



Flexible

AutomationSaftey

within

MRKQuality

Control

Track &

TraceSaftey

within

MRKQQQQQQQQuQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ ality

Control

material and data flow (horizontal integration)

IBM IoT Cloud ComputingEnvironment

SPSS & GW

SPSS & GW

SPSS & GW

APIAPI

APIAPI

HMIHMI HMI

Analytics

Asset Management

Mobile

„CRM/ERP“

UI / DashboardData

data

flow

(ver

tical

int

egra

tion)

Structured and

unstructureddata stream

s

coexist

cooperate

collaborate

IoT Prototype Solution Overview



Incomingparts

Manufacturing

Quality Control Outgoing partsSensor environment connected to the IBM IoT Cloud

IoT Prototype

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