View
228
Download
0
Category
Preview:
Citation preview
07/04/2016 Prof. Luca Fumagalli ‐ POLITECNICO DI MILANO
Marco MacchiPolitecnico di Milano
Factory 4.0 ‐ Towards the 4th Industrial Revolution SCENARIOS, TOPICS & TRENDS
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it2
The Fourth Industrial Revolution (aka Industry 4.0)
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it3
Evolution or Revolution?
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it4
Smart … Advanced … 4.0
Source: Osservatorio Smart Manufacturing, Yearly Report 2016
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it5
Piano nazionale Industria 4.0Milano, 21 settembre 2016
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it6
Factory 4.0: it is time to change
The good habitsto promote
The bad habitsto be abandoned
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it7
The «voice» of the companyHow to implement the «Industry 4.0» vision in a company?
A company has its own business pressures & priorities …
Velocity
Collaboration
Transparency
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it8
GermanyIndustrie 4.0
Initiatives in other countries: Germany
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it9
Initiatives in other countries: USA
USASmart Manufacturing Leadership Coalition
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it10
UKCatapult‐High Value Manufacturing
Initiatives in other countries: UK
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it11
Initiatives worldwide
Source: Osservatorio Smart Manufacturing, Yearly Report 2016
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it12
Italy in worldwide manufacturingRank 2000 2010 2014
Cina
USA
Giappone
Germania
Korea, Rep.
Italia
Brasile
Francia
India
Regno Unito
Russia
Spagna
Messico
USA
Giappone
Cina
Germania
Regno Unito
Italia
Francia
Korea, Rep
Messico
Spagna
Brasile
Canada
India
USA
Giappone
Germania
Italia
Francia
Regno Unito
Cina
Brasile
Spagna
Canada
Korea, Rep
Olanda
Messico
1990
Cina
USA
Germania
Giappone*
Korea, Rep.
India
Italia
Regno Unito
Francia
Russia
Brasile
Messico
Canada
12345678910111213
Top 13 manufacturers in the worldShare of global nominal manufacturing gross value added Data source: The World Bank
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it13
Labour productivity
Source: OECD (Organisation for Economic Co-operation and Development )
80
85
90
95
100
105
110
115
120
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013
(199
8=10
0)
Total Factor Productivity
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it14
ufacturing
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it15
Identity
Design
Manufacturing
DistributionUsage
Maintenance
Disposal & Recycle
Each item has its electronic passport: traceability is enabled along the life cycle of the single item
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it16
Internet of Things
• According to some estimates there will be 50 billion mobilewireless devices connected to the Internet across the globeby 2020.
• The total number of devices connected to the Internet in someway could reach 500 billion.
OECD (2012), “Machine-to-Machine Communications: Connecting Billions of Devices”, OECD Digital Economy Papers, No. 192, OECD Publishing.
PHYSICAL PRODUCT
ID
DATA & KNOWLEDGE
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it17
Internet of Things
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it18
Cloud computing
Infrastructureas a Service
(IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Manufacturing as a Service
(MaaS)
CLO
UD
CO
MPU
TIN
G“Two types of cloud computing adoptions in themanufacturing sector have been suggested:manufacturing with direct adoption of cloudcomputing technologies, and cloud manufacturing,the manufacturing version of cloud computing.… Incloud manufacturing, distributed resources areencapsulated into cloud services and managed in acentralized way ….Cloud users can request services ranging from productdesign, manufacturing, testing, management and allother stages of a product lifecycle”
Xun Xu, 2012, “From cloud computing to cloud manufacturing”,Robotics and Computer-Integrated Manufacturing.
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it19
Cloud computing
Infrastructureas a Service
(IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Manufacturing as a Service
(MaaS)
CLO
UD
CO
MPU
TIN
G
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it20
Cloud computing
Infrastructureas a Service
(IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Manufacturing as a Service
(MaaS)
CLO
UD
CO
MPU
TIN
G
Virtual machines, storage, ... as resources in order to runsolutions demanding highly computing capabilities
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it21
Cloud computing
Infrastructureas a Service
(IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Manufacturing as a Service
(MaaS)
CLO
UD
CO
MPU
TIN
G
IoT platform that provides a set of functionalities for smartdevice management, DBMS, data analytics, etc.
Virtual machines, storage, ... as resources in order to runsolutions demanding highly computing capabilities
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it22
Cloud computing
Infrastructureas a Service
(IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Manufacturing as a Service
(MaaS)
CLO
UD
CO
MPU
TIN
G
IoT platform that provides a set of functionalities for smartdevice management, DBMS, data analytics, etc.
Virtual machines, storage, ... as resources in order to runsolutions demanding highly computing capabilities
Platform in order to support applications and data for thecollaboration in the value chain
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it23
Cloud computing
Infrastructureas a Service
(IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Manufacturing as a Service
(MaaS)
CLO
UD
CO
MPU
TIN
G
IoT platform that provides a set of functionalities for smartdevice management, DBMS, data analytics, etc.
Virtual machines, storage, ... as resources in order to runsolutions demanding highly computing capabilities
Platform in order to support applications and data for thecollaboration in the value chain
Platform in order to support the virtualization and sharingof manufacturing resources
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it24
Big Data and Analytics
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it25
Big Data and Analytics
What about unstructured data? “80 percent of global data isunstructured, so what do we do? …”
The majority of content tends to be human-generated content:this does not fit neatly into database tables.
«… While ‘Big Data’ technologies and techniques are unlockingsecrets previously hidden in enterprise data, the largest source ofpotential insight remains largely untapped … the ‘Big Content’remains grossly underutilized and its potential largelyunexplored…» (“Big Content: The Unstructured Side of Big Data”http://blogs.gartner.com/)
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it26
Big Data and Analytics
http://www.ibm.com/watson/
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it27
Cyber Physical SystemsThe term «Cyber-Physical Systems» was forged by the NationalScience Foundation, in USA:
«a broad range of complex, multi-disciplinary, physically-aware next generation engineered systems that integratesembedded computing technologies (cyber part) into thephysical world»
The integration includes capabilities to sense, to communicateand to control the physical systems. Indeed, cyber and physicalpart are tighly integrated at different scales and levels.
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it28
Cyber Physical Systems
Cyberized plant/ «Plug & Produce»
Next step production efficiency
Digital Ergonomics
New data-driven servicesand business models
Data-based improvedproducts
Closed-loop manufacturing
1
2
3
4
5
6
Cyber-Physical Systems are technologicalsystems that will boost the transformation of business models, factories and supply chains
sCorPiuS – European Roadmap for Cyber-Physical Systems in Manufacturingwww.scorpius-project.eu
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it29
Cyber Physical Systems
Cyberized plant/ «Plug & Produce»
Next step production efficiency
Digital Ergonomics
New data-driven servicesand business models
Data-based improvedproducts
Closed-loop manufacturing
1
2
3
4
5
6
Cyber-Physical Systems are technologicalsystems that will enable new opportunitiesfor the product & the factory life cycle
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it30
Cyber Physical SystemsIntegration & Intelligence are required for the implementation ofCyber Physical Systems.
Lee, J., Bagheri, B., Kao, H., 2015. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems.Manufacturing Letters, Volume 3, 18–23.
Self-comparisonb/w digital twins
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it31
Cyber Physical Systems
Lee, J., Bagheri, B., Kao, H., 2015. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems.Manufacturing Letters, Volume 3, 18–23.
The inter-connection between machinehealth analytics through a machine–cyber interface (at the cyber level) isconceptually similar to social networks.
Self-comparisonb/w digital twins
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it32
Cyber Physical SystemsCyber-Physical Systems (CPS) are the key enablers for the“digital factory” creation since they bridge the gap between thereal and virtual world by connecting different smart objects withthe factory’s information systems and making all the actors of thefactory communicating each other across the entire value chain.E. Lee, “The Past, Present and Future of Cyber-Physical Systems: A Focus on Models,” Sensors, vol. 15, no. 3, pp. 4837–4869, 2015.
Digital twins
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it33
Cyber Physical Systems• Extended intra- and inter-enterprise integration• Empowerment of collaboration & velocity within and between the factories• New role of human resources, and new jobs & skills
Horizontal INTEGRATIONHorizontal INTEGRATION
Vertical IN
TEGRA
TION
Vertical IN
TEGRA
TION
• OEM – asset owner• Inter‐plant collaboration
• Production – Maintenance• Plant engineering – Maintenance
Typical Components for the «recipe» of Cyber
Physical Systems
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it34
Cyber Physical Systems
01010011011101010110111101101101011001010110111000100000011010110110111101101110011001010010110100100000011010100110000100100000011011010110010101110100011000010110110001101100011010010111010001110101011011110111010001100101011101000110010101101111011011000110110001101001011100110111010101110101011001000110010101101100011011000110000100100000011011110110111000100000011010000111100101110110110000111010010001110100001000000110010101110110110000111010010011000011101001000111010000100000011011010110010101101110011001010111001101110100011110010110101101110011011001010110010101101110
Analysis of data
Better machines and equipmentImproved processes
More efficient production
• Enhanced transparency and subsequent business potentials
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it35
A Smart Maintenance tool for a safe Electric Arc Furnace
MIMOSA OSA-CBM specification / ISO-13374
Outcome: E‐maintenance tool extending thefunctionality of the Plant Automation withPHM capabilities:State Detection‐ Injection systems AND Hearth/bottom + PanelsHealth Assessment‐ Injection systems AND PanelsAdvisory Generation‐ Injection systems
Electric Arc Furnace (EAF)
‐ Burning system carbon injection and oxygen injection systems‐ Hearth/bottom lower sheet metal keel (coated with refractory bricks)‐ Cooling system panels (cooled by a water circuit).
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
ExternalSystems, Data
Archiving& BlockConfiguration
Technical Dysplays
& Informati
on Presentation (HMI)
Sensors / Transducers / Manual Entry
DM,SD functions
SD,HA,AG functions
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it36
A Smart Maintenance tool for a safe Electric Arc Furnace
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
Sensors / Transducers / Manual Entry EAF/Burning system/Injection systems
MIMOSA OSA-CBM specification / ISO-13374
Outcome: PHM algorithm(key variables: pressures, flow rates)
State Detection‐ Deviations from normal operating conditions [based on a regression
model and a control chart]Health Assessment & Advisory Generation‐ Causes of degradation, recommended counteractions, etc. [based on
HAZOP tables]
Item n. 1.1 – Deviation: High Pressure
Causes Effects Counteractions Suggestions
The pipeline is obstructed by slag
Injection nozzle is crushed
Malfunction in pressure sensor
Malfunction in flow control valve
Errors in PLC data communication
Crushed pressure sensor
Crushed flexible pipe Wrong mounted nozzle Malfunction in non-
return-valve
Increase in pressure level may cause a damage in flexible pipe, possible fire triggering with explosion or damage to other plants
Malfunction in the burner with consequent missed melting of the scrap
An error in pressure measure may cause the control system to turn off the burner
Possible mixing of different fluid in the pipeline
During supersonic injection phase of the oxygen the burner may cause some melted metal to be projected and/or have a poor oxidation of metal
Check nozzle status during planned stops
Check annually pressure level of the pipelines
Increase flexible pipe reliability Change nozzle Annually check flow control
valve status In case of explosion
immediately stop the system by pushing emergency button
Evaluate the possibility to add indicators on operator monitor to help them in finding possible malfunctions
Evaluate the possibility to implement a safety stop under particular conditions
Evaluate the possibility to divide working area in different compartments
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it37
A Smart Maintenance tool for a safe Electric Arc Furnace
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
Sensors / Transducers / Manual Entry EAF/Sole & Cooling system (panels)
MIMOSA OSA-CBM specification / ISO-13374
Outcome: PHM algorithm(key variables: temperatures)
State Detection‐ Deviation from safe conditions due to presence of water in the hearth
/ bottom [based on a regression model and a control chart]‐ Occurrence of a series of “critical” events of the melting process – i.e.
events featuring relevant stress on panels during normal operatingconditions [based on a control chart]
Health Assessment‐ Deviations toward unhealthy conditions for the panels [based on
statistical indicators to extract the process features (i.e. actual vs.reference) and a risk matrix to map the features]
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it38
A Smart Control systemfor reconfigurable manufacturing
Reconfigurability
SemanticsModularity
Plug and Produce
Fastercommissioningof plants
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it39
A Smart Control systemfor reconfigurable manufacturing
eScop : Embedded systems forService-based Control of OpenManufacturing and Processautomation
Semantic-based reasoning
The MSO (Manufacturing Systems Ontology)stores the knowledge of the configuration ofthe production system + information from fielddevices in the physical layer (i.e. status of thedevices)
E. Negri. The Role of Ontologies for Smart Manufacturing.PhD thesis
Self-awareness of each digital twin
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it40
Smart Sensors for Condition Based Maintenance
Reconfigurability
Diagnosabilityand quality ofproduction
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it41
Smart Sensors for Condition Based Maintenance
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
Sensors / Transducers / Manual Entry
MIMOSA OSA-CBM specification / ISO-13374 sensors
Data Acquisition
sensors
Data Acquisition
sensors
Data Acquisition
Data Manipulation & State Detection
Web service Health Assessment
Robot control Human Machine Interface
1
2
3
5
4
6
Web service
eSONIA Embedded Service OrientedMonitoring, Diagnostics and Control:Towards the Asset-aware and Self-Recovery Factory
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it42
Smart Sensors for Condition Based Maintenance
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
Sensors / Transducers / Manual Entry
MIMOSA OSA-CBM specification / ISO-13374 sensors
Data Acquisition
sensors
Data Acquisition
sensors
Data Acquisition
Data Manipulation & State Detection
Web service Health Assessment
Robot control Human Machine Interface
1
2
3
5
4
6
Web service
eSONIA Embedded Service OrientedMonitoring, Diagnostics and Control:Towards the Asset-aware and Self-Recovery Factory
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it43
Smart Sensors for Condition Based Maintenance
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
Sensors / Transducers / Manual Entry
MIMOSA OSA-CBM specification / ISO-13374 sensors
Data Acquisition
sensors
Data Acquisition
sensors
Data Acquisition
Data Manipulation & State Detection
Web service Health Assessment
Robot control Human Machine Interface
1
2
3
5
4
6
Web service
eSONIA Embedded Service OrientedMonitoring, Diagnostics and Control:Towards the Asset-aware and Self-Recovery Factory
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it44
Smart Sensors for Condition Based Maintenance
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostic Assessment (PA)
Advisory Generation (AG)
Sensors / Transducers / Manual Entry
MIMOSA OSA-CBM specification / ISO-13374
eSONIA Embedded Service OrientedMonitoring, Diagnostics and Control:Towards the Asset-aware and Self-Recovery Factory
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it45
The key is the improvement
The resources (people and machines) shouldbe managed towards the improvement of theoperational excellence, with the purpose toachieve enhanced performances together withadequate cost balance
VelocityCollaboration
Transparency
Collaboration
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it46
Which model for improvement?
There is a German, a Japanese, an American and an Italian…
Understandthe problems
Foresee &
solutions
Foresee & Develop new solutions
Collaboration
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it47
The German
[DER SPIEGEL 1964]
Understandthe problems
Foresee &
solutions
Foresee & Develop new solutions
Automation of resources:technology push
Collaboration
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it48
Understandthe problems
Foresee &
solutions
Foresee & Develop new solutions
Operatorsengagement
The Japanese
Planned activitesof continuousimprovemenyCollaboration
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it49
Understandthe problems
Foresee &
solutions
Foresee & Develop new solutions
The American
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it50
The Italian
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it51
Soluzioni tradizionali – Livello di Implementazione
The most complex traditional solutions are still scarcely diffused. Can we do the Fourth Industrial Revolution, if the third one has not yet been done?
The most complex traditional solutions are still scarcely diffused. Can we do the Fourth Industrial Revolution, if the third one has not yet been done?
67%47% 31% 31% 29% 28% 22% 15% 14% 12%
21%37%
31% 34% 28% 34% 31% 37% 37% 27%
12% 17%38% 35% 43% 38% 47% 48% 50% 61%
0%10%20%30%40%50%60%70%80%90%
100%
Non implementata
Parzialmente implementata
Completamente implementata
*Sample size: 289 companies
Let’s understandthe italian scenario
Not implementedNot implemented
Partially implementedPartially implemented
Completely implementedCompletely implemented
Traditional solutions – implementation level
Source: Osservatorio Smart Manufacturing, Yearly Report 2016
Diapositiva 51
g3 in media hanno risposto in 289gianluca.tedaldi@gmail.com; 20/06/2016
g4 ogni tecnologia ha la sua base di rispondenti...gianluca.tedaldi@gmail.com; 20/06/2016
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it52
The Italian scenario and Small-Medium Enterprises
The Italian industry is highly represented by small-mediumenterprises, in a highly competitive, turbulent and uncertainmarket !
But the voice of the company is still ….
Velocity
Collaboration
Transparency
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it53
The Italian scenario and Small-Medium Enterprises
With some specificity …• Difficulties with fluctuations (cash flows, orders, …)• Limited customer basis, closeness to the customers• Tending to adopt a reactive approach• Risk of a digital divide in the industry landscape
Velocity
Collaboration
Transparency
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it54
The Italian scenario and Small-Medium Enterprises
Some characteristis in the operations …• Synchonization with the material flows• Short-loop control• Flexibility and reconfigurability• Responsiveness
Velocity
Collaboration
Transparency
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it55
Fabbrica 4.0: le tecnologie abilitanti
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it56
Fabbrica 4.0: la visione
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it57
The «voice» of the companyHow to assess the digital readiness of a company?
Ask the main stakeholders …
• Production manager; • Asset manager;• Quality manager;• Product engineering manager;• Process engineering manager;• Operations manager;• Logistics manager;• IT manager
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it58
Digital Readiness & Opportunities Identification
How to assess the digital readiness of a company?
A. De Carolis. Building Smart Manufacturing through Cyber-Physical Systems: a model to generate awareness and toguide manufacturing companies towards digitalisation. PhD thesis
Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ marco.macchi@polimi.it59
Digital Readiness & Opportunities Identification
How to look for opportunities of manufacturing digitisation?
Process Area Dimension Strengths Weaknesses
Process 1 Process •
Monitoring and control • •
Technology • •
Organization • .
Process 2 Process • •
Monitoring and control •
Technology • •
… Process •
Monitoring and control •
Technology •
Process N Process •
Monitoring and control • •
Technology •
Area Opportunities
Process Area 1
Design and Engineering
•
Process Area 2
Production Management
•
Process Area 3
Quality Management
•
Process Area 4
Maintenance Management
•
Process Area 5
Logistics Management
Digital Backbone
OpportunitiesIdentification
Strenghs and WeaknessesIdentification
MaturityAssessment
A. De Carolis. Building Smart Manufacturing through Cyber-Physical Systems: a model to generate awareness and toguide manufacturing companies towards digitalisation. PhD thesis
Recommended