industry 4.0 and artificial intelligencepower.gruppocdm.it/wp-content/uploads/2018/10/... · 2018....

Industry 4.0 and Artificial Intelligence

Lazise, 25.10.18 #OI40

Giovanni Miragliotta

Osservatorio Industria 4.0Osservatorio Internet of Things

Osservatorio Artificial Intelligence

3

Agenda

3

• The fourth Industrial revolution… and beyond• Understanding AI• Artificial Intelligence in Industry• Managing the change

4

The Fourth Industrial Revolution

First Industrial Revolution

Second Industrial Revolution

Third Industrial Revolution

Fourth Industrial Revolution

TIME1780 1870 1970

Steam power, machines in factories

Labor organization, mass production, use of electricity

Automation / robots in factories, computers / SW in business processes

Interconnected digital and physical world thanks to new digital technologies

First assembly line (1870)

First PLC (1969)

First loom powered by steam

First appearance of "Industrie 4.0"

2011

55

The Fourth Industrial Revolution1 ‐ Two‐sided transformation

Industry 4.0 Smart Connected Product

6

Industrial Analytics

Advanced Human-Machine

Interface

Advanced Automation

Additive Manufacturing

Cloud Manufacturing

Industrial Internet of Things

Operational TechnologyInformation Technology

SMA

RT

MA

NU

FAC

TUR

ING

TE

CH

NO

LOG

IES

Industry 4.0 is a vision of the future of Industry and Manufacturing in which Digital Technologies are going to boost competitiveness and efficiency by

interconnecting every resource (data, people and machinery) in the Value Chain

The Fourth Industrial Revolution2 ‐ Definition of Industry 4.0

7

The Fourth Industrial Revolution3 ‐ The data gap

8

The Fourth Industrial Revolution3 ‐ The data gap

9

Smart Lifecycle Smart Supply Chain Smart Factory

PROCESSES

• New Product Development

• Product Lifecyclemanagement

• Suppliers RelationshipManagement

• Production & Distribution Planning

• Supply Chain EventManagement

• Suppliers RelationshipManagement

• ProductionManagement

• LogisticsManagement

• MaintenanceManagement

• QualityManagement

• Safety &Compliance

SMA

RT

TEC

HN

OLO

GIE

S

Cloud Manufacturing

Additive Manufacturing

Industrial Analytics

Advanced HMI

Advanced Automation

Industrial IoT

The Fourth Industrial Revolution4 – The application areas

1010

AverageSource: The World Bank (2013)

100

210 –250

%PIL "Manifattura" %PIL "Servizidirettamente legati

all'output industriale"

Services for manufacturing (Italy, 2014)

Source: ISTAT, Rapporto di competitività 2014

10.6%10.7%10.9%11.2%

12.5%13.2%

15.4%15.6%

17.1%17.7%

19.7%21.0%

22.6%30.3%

35.9%

Regno UnitoCanada*

BrasileFrancia

USA*Spagna

ItaliaRussia

IndiaMessico

Giappone*IndonesiaGermania

Korea,…Cina

Manufacturing as a % of GDP – 2014

The Fourth Industrial Revolution5 – The relavance of manufacturing

13

Canada – 2015Conestoga: Centre for Smart Manufacturing

Stati Uniti - 2012Advanced Manufacturing Partnership Francia - 2015

Industrie du Futur

UK - 2011Catapult-High ValueManufacturing

Belgio - 2013Made Different Svezia - 2014

Produktion 2030 Danimarca - 2012Made

Korea del Sud – 2015Manufacturing innovation

3.0 Strategic Action Programme

Giappone - 2015Industrial Value Chain Initiative

Cina - 2015Made in China 2025

India - 2015Make in India

Australia - 2013The Next Waveof Manufacturing

Germania - 2011Industrie 4.0

Olanda - 2014Smart Industry

Portugal -PRODUTECH

Spain - 2016Industria Conectada

Italia – 2016Piano NazionaleIndustria 4.0


14

Italia – 2017Piano Nazionale Impresa 4.0

14


15

The Fourth Industrial Revolution…and beyond

Interconnection of physical and digital worlds

Tons of (new) data

Need for new approaches

17

Agenda

17


18

Definizione

“L’ Artificial Intelligence è il ramo della computer science che studia

lo sviluppo di sistemi hardware e software dotati di capacità

tipiche dell’essere umano

ed in grado di perseguire autonomamente una finalità definita

prendendo delle decisioni che, fino a quel momento,

erano solitamente affidate agli esseri umani”

19

Natural Language

Processing

Image Processing

LearningReasoning

and Planning

Interacting Physically

Interacting Socially

INPUT

PROCESSING

OUTPUT

Artificial Intelligence capabilities

20

The world upside down

21

The world upside down

ComputerOUTPUT

DATA

PROGRAM

Traditional Programming

ComputerPROGRAM

DATA

OUTPUT

Machine Learning

23

Welcome to machine learning!

25

Welcome to machine learning!

26

20x – 50x 100x – 1000x

50x

–10

0x GPUs,Neural

HW

Supercomputers

(Systems of GPUSs or

Neural HWs)

RAM

ComputationSpeedup

PC

Embedded PCs

Embedded

Systems

0.05x – 0.1x0.0001x – 0.0005x

0.1x

–0.

5x0.

01x–

0.05

x

Deep LearningDeep Blue defeated

Kasparov (1996)

Better accuracy than traditional CV (2012),than human (2015)

IBM Watson winning

Jeopardy first prize (2011)

Libratus beats four world’s top players at Texas

hold’em poker (2017);AlphaGo beats the world

No.1 ranked player (2016)

Intelligent Object

Autonomous Vehicle, Autonomous Robot

Why now?

Marcello Restelli – Osservatorio AI

27Marcello Restelli – Osservatorio AI

Why now?

29

Classi di soluzioni (2017)

AutonomousRobot

Intelligent Object

Autonomous Vehicle

Virtual Assistant/Chatbot

Language Processing

Recommendation

Image Processing

IntelligentData Processing

Campione:

721 Aziende

469 Casi (337

Aziende)

7%

4%

7%

25%

10%

8%

4%

35%

Frequenza

29

30

Predictive31%

Monitoring22%

Detection15%

Discovery18%

Creation8%

Others6%

Intelligent data processing

Data Processing

Predictive Analysis

Pattern Discovery

Fraud/AnomalyDetection

Contents / Design Creation

Monitoring & Control

164

Analisi dei dati al fine di fornire previsioni sull’andamento futuro del fenomeno studiato

Analisi e identificazione di pattern all'interno di dati grezzi al fine di identificarne la classificazione

Identificazione di elementi, eventi o osservazioni che non sono conformi a un modello previsto

Analisi dei dati disponibili al fine di creare nuovi contenuti, o progettare nuovi servizi o prodotti

Analisi dei dati al fine di monitorare lo stato di un certo sistema e eventualmente di intervenire sul sistema stesso per raggiungere obiettivi prefissati.

35%Intelligent data processing

Soluzioni che utilizzano algoritmi di intelligenza artificiale su dati strutturati e non strutturati,

per finalità collegate all’estrazione dell’informazione presente nel dato

30

31

Maturità applicativa

5%

39% 40%

62%

27%

50%42%

21%

6%

25%

19%

16%

28%

26%

42%

42%

30%

25%

13%

27%

17%

12%58%

32%24%

10% 6%

30%

6%20%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

A Regime In Implementazione

Campione: 721 grandi imprese – 469 Casi 31

32

4

1

31

2

38

7

15

25

7

11

2

9

1

5

1

6

9

7

1

1

7

2

12

7

10

1

2

1

14

1

9

2

2

1

3

4

9

2

3

1

3

2

18

1

2

1

4

1

6

1

1

1

1

5

9

1

6

7

1

1

7

2

13

20

11

48

5

15

AutonomousRobot

Intelligent Object

Autonomous Vehicle


Language Processing

Recommendation

Image Processing

Intelligent Data Processing

Campione 2017: 721 grandi imprese – 469 Casi

Healthcare, Chemistry,

y,Legal

Healthcare, Chemistry, Electronics, Pharmaceut

ical, ICT, Services, Fashion,

Manufacturing, Media, Metals and Mining, PA,

Military, Construction, Beauty, Hospitality, Security,

Legal

Application areas: industry view

32

36

1

1

18

1

1

4

29

8

6

3

2

1

5

4

1

1

2

1

5

36

1

4

1

5

28

26

16

7

12

3

4

2

44

8

21

3

22

3

3

53

2

11

2

6

1

3

1

2

9

29

4

3

AutonomousRobot

Intelligent Object

Autonomous Vehicle


Language Processing

Recommendation

Image Processing


Support Activities Primary Activities Product

Campione 2017: 721 grandi imprese – 469 Casi

Application areas: process view

36

38

La raccolta di casi: la situazione italiana

AutonomousRobot

Intelligent Object

Autonomous Vehicle


Language Processing

Recommendation

Image Processing


Support Activities Primary Activities Product

2

1

1

1

1

4

1

1

1

1

4

2

1

1

7

3

2

Solo il 56% di aziende italiane analizzate mostra applicazioni di AI, confrontato con il 70% di Francia e Germania

Campione 2017: 721 grandi imprese – 31 Casi italiani 38

39

Agenda

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4040

AI in industryThe ConnectComp case(*)

Objective: Reduce product (connectors) failure rate; ability to predict failures would foster new revenues in service plans

Sensing: • Asset & flow condition monitoring • IoT Platform to collect operating data from

multiple products

Results:• Improved asset‐failure models (61% vs. 1%)• More efficient use of service resources in field• New opportunity for maintenance offering• Targeted purchase of spare parts, less inventory• Enabled R&D to understand causes and improve design

(*) Fictional name due to confidentiality reasons

4141(*) Fictional name due to confidentiality reasons

Objective: real‐time monitoring of location and state of the returnable asset (drum)

Sensing: • GPS (location)• MEMS accelerometer (shocks, rotation)• Smart energy management (sleep mode,

LPWA ready)

Functionalities:• 2‐year operating life, with real time monitoring (location, shocks)

• Local algorithm to estimate the remaining wire (as a function of wire thickness and number of revs)

AI in industryThe Wirecomp case(*)

4242

AI in industry

43

DECISIONS

PRODUCTION RESOURCES

PROCESSES

DECISIONS


PROCESSES

Company 1 Company 2

1

23

4

5

Packaging machine failure

Morningrescheduling

BEFORE

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Artificial Intelligence and Decision making

44

DECISIONS


PROCESSES

DECISIONS


PROCESSES

Company 1 Company 2

Extra defects

Packaging machine failure

Target Cost Deviation

Morningrescheduling

No free pass today!

Correctiveactions

AFTER

44

Artificial Intelligence and Decision making

46

Agenda

46


– Project design– Data Architecture– Organizational change (“AI Journey”)– Legal issues

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AI Application

EmbeddedAI solution

EmbeddedAI solution

IT Data Sources IoT Data Sources

Data Ingestion / Data Supplement

Vertical Solution Provider

AIArchitect

ConsultantSystemIntegrator

Vertical Solution Provider

(Cloud) Platform Provider

AI Project design

Web Data Sources

47

48

SHOP FLOOR

App1 App4 App1 App2 App4 App3 App5

Enterprise Data LakeLegacy

Applications (e.g. ERP, MES,

…)

Manufacturing Apps Store

Machine

App1 Planning

App2 Forecast

App3 Quality

App4 Visual Management

App5 Statistical Process Control

WORKER

Production Planner Expediter Quality Manager

Product ….

Cloud Applications

Forklift

48

Data Architecture and Decision Making

49

Materials categories, BOM, Vendors list, spending, products profitability

49

Data Architecture and Decision Making«Switch»: the suppliers’ risk assessment app

Suppliers financial scores(online data broker)

IT Data Sources IoT Data SourcesWeb Data Sources

Data ingestion and integration

Innovative algorithm to estimate Suppliers risk:

• Probability of adverse event• Magnitudo of adverse event

Chief Financial Officer Purchasing Manager

50

Materials categories, BOM, Vendors list, spending, products profitability

50


Suppliers financial scores(online data broker)

IT Data Sources IoT Data SourcesWeb Data Sources

Data ingestion and integration

Chief Financial Officer Purchasing Manager

5151


Purchasing ManagerChief Financial Officer

www.rischiodifornitura.it

5252

Cited in S. Ceri, "On the role of statistics in the era of big data: a computer science perspective", Politecnico di Milano, 2017, White Paper

Data Architecture and Decision Making

56

Q&A

[email protected]

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ReferencesMust read:

• Erik Brynjolfsson, Andrew McAfee, 2014, "The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies", W&W Norton & Company (Italian edition available)

• Michael E. Porter, James E. Heppelmann, 2014, "How Smart, Connected Products Are Transforming Competition", HBR, November issue (Italian edition available)

• Pedro Domings, 2015, "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World ", Basic Book (Italian edition available)

(Near) Must read:

• Nicholas Carr, 2014, The Glass Cage: Automation and Us, W&W Norton & Company • Thomas H. Davenport, Julia Kirby, 2015, Beyond Automation: Strategies for remaining gainfully employed

in an era of very smart machines, HBR, June issue • Erik Brynjolfsson, Andrew McAfee, 2017, "Machine, Platform, Crowd: harnessing our digital future", W&W

Norton & Company • Daron Acemoglu, Pascual Restrepo, 2017, Robots and Jobs: Evidence from US Labor Markets, NBER

Working Paper No. 23285 (introduction only)

Into the future:

• Tim Urban, The Road to SuperIntelligence, https://waitbutwhy.com/2015/01/artificial‐intelligence‐revolution‐1.html (part 2 also)

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