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Industry 4.0 and Artificial Intelligence
Lazise, 25.10.18 #OI40
Giovanni Miragliotta
Osservatorio Industria 4.0Osservatorio Internet of Things
Osservatorio Artificial Intelligence
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Agenda
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• The fourth Industrial revolution… and beyond• Understanding AI• Artificial Intelligence in Industry• Managing the change
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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
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The Fourth Industrial Revolution1 ‐ Two‐sided transformation
Industry 4.0 Smart Connected Product
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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
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The Fourth Industrial Revolution3 ‐ The data gap
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The Fourth Industrial Revolution3 ‐ The data gap
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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
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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
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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
The Fourth Industrial Revolution5 – The relavance of manufacturing
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Italia – 2017Piano Nazionale Impresa 4.0
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The Fourth Industrial Revolution5 – The relavance of manufacturing
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The Fourth Industrial Revolution…and beyond
Interconnection of physical and digital worlds
Tons of (new) data
Need for new approaches
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Agenda
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• The fourth Industrial revolution… and beyond• Understanding AI• Artificial Intelligence in Industry• Managing the change
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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”
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Natural Language
Processing
Image Processing
LearningReasoning
and Planning
Interacting Physically
Interacting Socially
INPUT
PROCESSING
OUTPUT
Artificial Intelligence capabilities
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The world upside down
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The world upside down
ComputerOUTPUT
DATA
PROGRAM
Traditional Programming
ComputerPROGRAM
DATA
OUTPUT
Machine Learning
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Welcome to machine learning!
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Welcome to machine learning!
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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
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27Marcello Restelli – Osservatorio AI
Why now?
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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
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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
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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
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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
Virtual Assistant/Chatbot
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
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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
Virtual Assistant/Chatbot
Language Processing
Recommendation
Image Processing
Intelligent Data Processing
Support Activities Primary Activities Product
Campione 2017: 721 grandi imprese – 469 Casi
Application areas: process view
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La raccolta di casi: la situazione italiana
AutonomousRobot
Intelligent Object
Autonomous Vehicle
Virtual Assistant/Chatbot
Language Processing
Recommendation
Image Processing
Intelligent Data 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
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Agenda
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• The fourth Industrial revolution… and beyond• Understanding AI• Artificial Intelligence in Industry• Managing the change
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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
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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(*)
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AI in industry
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DECISIONS
PRODUCTION RESOURCES
PROCESSES
DECISIONS
PRODUCTION RESOURCES
PROCESSES
Company 1 Company 2
1
23
4
5
Packaging machine failure
Morningrescheduling
BEFORE
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Artificial Intelligence and Decision making
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DECISIONS
PRODUCTION RESOURCES
PROCESSES
DECISIONS
PRODUCTION RESOURCES
PROCESSES
Company 1 Company 2
Extra defects
Packaging machine failure
Target Cost Deviation
Morningrescheduling
No free pass today!
Correctiveactions
AFTER
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Artificial Intelligence and Decision making
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Agenda
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• The fourth Industrial revolution… and beyond• Understanding AI• Artificial Intelligence in Industry• Managing the change
– 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
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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
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Data Architecture and Decision Making
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Materials categories, BOM, Vendors list, spending, products profitability
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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
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Materials categories, BOM, Vendors list, spending, products profitability
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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
Chief Financial Officer Purchasing Manager
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Data Architecture and Decision Making«Switch»: the suppliers’ risk assessment app
Purchasing ManagerChief Financial Officer
www.rischiodifornitura.it
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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
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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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