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sCorPiuS European Roadmap for Cyber-‐Physical Systems in Manufacturing

Deliverable D2.1 - Gap Analysis on Research and Innovation and Vision

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636906 29/02/16

sCorPiuS-project.eu 1/32 Dissemination: Public

Gap Analysis on

Research and Innovation

and

Vision

White Paper

Document Owner: Holonix

Contributors: FPM, SUPSI

Dissemination: Public

Contributing to: WP3 Roadmap for next generation of Cyber-Physical Systems

Date: 29/02/16

Revision: 1.0

sCorPiuS European Roadmap for Cyber-Physical Systems in Manufacturing


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Project ID:

636906 29/02/16


Table of Content

Executive Summary ...................................................................................................................................................... 3

1. Relevance of Manufacturing in Europe ........................................................................................................ 4

2. CPS in Manufacturing .......................................................................................................................................... 6 2.1. The need for CPS .......................................................................................................................................................... 6 2.2. What is a CPS?................................................................................................................................................................ 6 2.3. CPS as a digital twin .................................................................................................................................................... 7 2.4. CPS for European Manufacturing ........................................................................................................................... 7

3. Gap Analysis ........................................................................................................................................................... 8 3.1. Methodology .................................................................................................................................................................. 8 3.2. Gap Analysis of 6 Clusters ...................................................................................................................................... 10

3.2.1. Cluster 1: New data-driven services and business models ..............................................................................10 3.2.2. Cluster 2: Data-based improved products ...............................................................................................................10 3.2.3. Cluster 3: Closed-loop manufacturing .......................................................................................................................11 3.2.4. Cluster 4: Cyberized plant/ “Plug & Produce” ........................................................................................................12 3.2.5. Cluster 5: Next step production efficiency ...............................................................................................................13 3.2.6. Cluster 6: Digital Ergonomics........................................................................................................................................14

3.3. Additional findings from the Gap Analysis Exercise .................................................................................... 14 3.4. Gap Analysis Summary ........................................................................................................................................... 15

4. Vision ..................................................................................................................................................................... 19

5. Conclusions and Outlooks .............................................................................................................................. 23

6. Annex ..................................................................................................................................................................... 24 6.1. Cluster 1: New data-driven services and business models ........................................................................ 24 6.2. Cluster 2: Data-based improved products ....................................................................................................... 25 6.3. Cluster 3: Closed-loop manufacturing ............................................................................................................... 26 6.4. Cluster 4: Cyberized plant/ “Plug & Produce .................................................................................................. 27 6.5. Cluster 5: Next step production efficiency ....................................................................................................... 28 6.6. Cluster 6: Digital Ergonomics ............................................................................................................................... 30

References .................................................................................................................................................................... 32



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Executive Summary

Manufacturing is a key asset for Europe; considering its effect on services, it is a backbone for research,

innovation, productivity, job creation and exports. De-Industrialization hit Europe, but both the world economic

situation and EU initiatives will make it possible to reverse the trend.

Cyber-Physical Systems will play a key role in this opportunity of re-industrialization of EU, especially

considering that EU has 30% of the world production of embedded systems, especially in high added value

sectors such automotive, aerospace and healthcare. These technologies are leading to the creation of smart and

virtual factories, as well as enhancing both the vertical and horizontal integration of supply and vale chains.

Yet several challenges need to be addressed in order to spread the adoption of CPS both from the technical and

business perspectives. Privacy, security, dependability, genitive abilities, human interaction, ubiquity,

standardisation, robust connectivity and governance are aspects that require improvements and real-world

experimentation. New business models and manufacturing related applications needs also research and

improvements; sCorPiuS focuses mainly on the latter, eliciting the needs of the manufacturing to adopt CPS

and embrace their potential. Six main gap clusters have been find and highlighted:

Cluster 1: New data-driven services and business models

Cluster 2: Data-based improved products

Cluster 3: Closed-loop manufacturing

Cluster 4: Cyberized plant/ “Plug & Produce”

Cluster 5: Next step production efficiency

Cluster 6: Digital Ergonomics

Additionally specific types of Obstacle not addressable in the sCorPiuS roadmap via Research Initiatives or

Innovation Actions has been found. These obstacles are related with macro-dynamics (social, economic or

technological) or with legal and standardization compliance. Both have been analysed, but are outside sCorPiuS

roadmap focus, not being addressable from research suggestions and recommendations.

Overtaking these gaps and obstacles will enable EU enterprises to achieve the potential that an integrated vision

of CPS usage through the whole product and factory/asset lifecycle will enable. In fact a well managed closed

loop product/factory data management will enable manufacturing enterprise IT users to have access to the data

available within all the different represented systems, achieving therefore an in-depth knowledge that today is

not available. Currently, manufacturing data are segmented, detailed and planned for a single scope, stored

within the legacy systems, thus preventing the “digital continuity” that would let use them in the optimal way,

independently from where and how they have been collected. CPS will instead be able to provide all the needed

information from the physical world while the Cyber-Physical-Collaboration environment will enable an

efficient analysis, management, sharing and usage of the data and the knowledge elaborated from them and

from the experience of involved people.



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1. Relevance of Manufacturing in Europe

Industry plays a central role in the economy of the European Union, accounting for 15% of value added

(compared to 12% in the US)1. It serves as a key driver of research, innovation, productivity, job creation and

export. Industry generates 80% of the EU's innovations and 75% of its exports. Including its effect on services,

industry could be considered the social and economic engine of Europe. In her opening speech at the

“Manufacture 2015 Conference”, Francine Closener, Secretary of State for the Economy, stressed that the

manufacturing industry "has been" and "continues to be the backbone of the European economy", through

its contribution to economic growth, employment and innovation. Yet European industry has lost many

manufacturing jobs over the last decade, and is facing tougher competition from emerging markets. The ghost

of "deindustrialization" currently haunting European governments and the European Commission is galvanizing

them into action. In order "to reverse the trend of de-industrialization and investment leakage", and "maintain a

high standard of living in Europe", businesses must "seize the opportunities of the global market".

They must further integrate into global value chains and strengthen their position as frontrunners in product and

process innovation. The importance of the manufacturing sector is reflected in the priorities of the European

Union, which underlines the need to stimulate investment to support growth and employment, to revitalize the

single market by focusing on the digital dimension, placing European competitiveness in a global and

transparent framework, promoting sustainable development and finally, strengthening the presence of the EU

on the world stage. A particular attention is payed to SMEs, which experience greater difficulties in investing

in innovation, in exporting and integrating the global value chains. It is essential to remove obstacles to cross-

border investment and to improve access to funding for SMEs and large businesses in the capital markets of all

EU Member States. It is innovation that drives the competitiveness of the manufacturing industry, in particular

through data gathering, data management and data analytics, which allows us, according to understand and

optimize our processes and to enhance productivity.

In the table below, the Manufacturing value added (as % of GDP) is reported for each European country from

2010 to 2014.

Manufacturing, value added (% of GDP)

Country Name / Year 2010 2011 2012 2013 2014

Austria 18,67 18,76 18,93 18,51 18,44

Belgium 14,72 14,25 14,08 13,98 13,76

Croatia 14,15 14,38 14,47 14,12 14,50

Cyprus 5,77 5,47 5,07 4,90 5,00

Czech Republic 23,44 24,46 24,79 24,93 26,58

Denmark 12,64 12,76 12,96 13,72 13,85

Finland 19,52 18,86 16,85 16,88 16,74

France 11,25 11,37 11,32 11,31 11,19

Germany 22,18 22,89 22,77 22,57 22,62

Greece 8,19 8,89 9,09 9,61 9,36

Ireland 22,21 23,77 21,53 20,37 19,72

Italy 15,80 15,77 15,37 15,31 15,42

1 This publication refers to EU 28 and uses the European Commission's definition of "industry" as "manufacturing", excluding mining, construction

and energy.



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Lithuania 18,77 20,37 20,67 19,44 19,27

Luxembourg 5,85 5,65 5,57 5,05 4,88

Netherlands 11,80 12,07 11,83 11,78 12,08

Norway 8,08 7,55 7,37 7,49 7,84

Poland 17,48 18,07 17,95 18,00 18,38

Portugal 13,15 12,94 13,00 13,13 13,27

Slovak Republic 20,79 21,09 20,88 20,25 20,91

Slovenia 20,16 20,96 21,63 22,45 23,11

Spain 13,27 13,46 13,06 13,08 13,24

Sweden 18,58 18,26 17,18 16,79 16,35

United Kingdom 10,31 10,31 10,28 10,75 10,61

European Union 15,29 15,51 15,29 15,26 15,27

OECD members 15,02 15,12 15,05 14,93 14,96 Table 1 Manufacturing, value added (%GDP)2

On average, considering all the European countries, this percentage ranges from 15,29 to 15,52.

Figure 1 Manufacturing, value added (% of GDP) in Europe

2 Data from database: World Development Indicators

14,6

14,7

14,8

14,9

15

15,1

15,2

15,3

15,4

15,5

15,6

2010 2011 2012 2013

Manufacturing, value added (% of GDP)

European Union OECD members



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2. CPS in Manufacturing

2.1. The need for CPS In the last decades, the manufacturing ecosystem witnessed an unprecedented evolution of disruptive

technologies forging new opportunities for European companies to cope the ever-growing market pressure.

Moreover, the race to create value for the customers has been hindered

by several issues that both small and large companies have been facing,

such as: shorter product life cycles, rapid time-to-market, product

complexity, cost pressure, increased international competition, etc. To

tackle these challenges manufacturing firms and research institutes

developed in parallel new information and communication technologies

(ICT) and manufacturing automation systems. The convergence

between the virtual and the physical world in the form of cyber-physical

systems brought forth the opportunity for the fourth industrial

revolution. The term cyber-physical system was coined in the US in

2006 (Lee, 2006) from the acknowledged importance of the tight

integration between computing systems and the physical world in manufacturing. The CPS concept gained vast

popularity in the last years, according to the web of science search engine exists 2’146 CPS-related papers and

nearly 2’000 of them were published after 2010.

2.2. What is a CPS? Several experts already tried to provide definitions for CPS. Thus, in the literature it is possible to find several

similar interpretations of CPS rather than a standard definition. Most of them state the scope of CPS and describe

how the interaction between the virtual and the physical world is made (e.g. enabling technologies). Instead of

providing a modified interpretation in regards with the definition of CPS, in the table below are reported four

definitions achieved from the efforts made in existing studies related to the state of the art of CPS.

Yu et al, 2015

“Cyber-Physical systems are an integration of embedded systems and the physical

environment with global networks. In other words, the physical resources in CPS

are globally networked and accessible from the outside. Based on this underlying

architecture and together with the capabilities of sensing, computing and

communicating, products can actively change the state of the physical

environment.”

Monostori, 2014

“Cyber-Physical Systems (CPS) are systems of collaborating computational entities

which are in intensive connection with the surrounding physical world and its on-

going processes, providing and using, at the same time, data-accessing and data-

processing services available on the internet.”

Wang et al, 2015

“Embedded computers and networks monitor and control the physical processes,

usually with feedback loops where physical processes affect computations and vice

versa. In other words, CPS use computations and communication deeply embedded

in and interacting with physical processes so as to add new capabilities to physical

systems. A CPS may range from minuscule (a pace maker) to large scale (a national

power grid).”

Hu et al 2015

“The ultimate purpose of using cyber infrastructure (including sensing, computing

and communication hardware/software) is to intelligently monitor (from physical

to cyber) and control (from cyber to physical) our physical world. A system with a

tight coupling of cyber and physical objects is called cyber–physical system (CPS).” Table 2: definitions of CPS in the literature

Computer &

mi

c

roprocessor CNC

Computer g

r

aphi cs CAD

Databases CIM

Machine l

e

arni ng IMS

Internet Concurrent e

n

gi neering

Wireless c

o

mmuni cat ion High tr acki ng sys t ems

Embedded s

y

stems Product-Service sy stems

Semantic w

e

b Production on t ol ogi es

... ...

Physical w

o

r ldVirtual w or ld



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2.3. CPS as a digital twin In order to interact with the surrounding environment (i.e. vertical

and horizontal integration) CPS are equipped with embedded

computing power capable to retrieve and elaborate real-time

information from sensors, information systems, manufacturing

resources, products, customers, etc. Consequently, exploiting this big

amount of data in combination with new techniques such as cloud

and high performance computing, innovative companies are now able

to virtualize by means of 3D modelling software the physical objects

of the factory. In manufacturing, this practice leads to several

benefits, for example it allows to perform complex tasks such as assembly and maintenance in a simplified way

by adding needed information into the field of view. For instance, Boeing, BMW and Volkswagen are

incorporating these new technologies to improve their assembly processes3.

2.4. CPS for European Manufacturing CPS have the potential to impact massively the European economy and society. Europe accounts for 30% of

world production of embedded systems specifically in the automotive, aerospace and healthcare sectors. For

this reason, Europe is focusing on capitalizing this market through several financial supports (e.g. FP7, Horizon

2020, ARTEMIS, I4MS). However, providers of this technology have to create new business models in order

to make CPS be adopted both in small and large companies. In doing so, several challenges need to be addressed

in order to spread the adoption of CPS: privacy, security, dependability, genitive abilities, human interaction,

ubiquity, standardization, robust connectivity and governance. In the manufacturing context, CPS exploit

advancements achieved in computing systems on modelling in combination with the big amount of data

produced from the surrounding environment through low power sensors and actuators. These technologies led

to the creation of smart factories (where the machines are able to reconfigure themselves in line with external

conditions and thanks to their embedded computing power) and virtual factories (able to orchestrate the factory’s

resources and the information across the entire value chain through IoT, cloud computing and smart products

paradigms). In the last years, CPS have been extensively adopted in aerospace, electric, transportation,

healthcare, and housing industries to support both vertical and horizontal integration of IT systems. However,

since CPS are implemented in heterogeneous environments, companies need new architectures able to

seamlessly integrate several heterogeneous automation software conceived in diverse domains (e.g. control,

diagnostic, modelling, process rendering, human machine interfaces, etc.) of the factories. The standard IEC

61499 is currently adopted as a component-based modelling approach able to deal both with software and

hardware systems. Accordingly, the fundamental requirements for introducing CPS in industry have been

specified in the literature as it follows:

Adaptable to heterogeneous environments: integration with cutting-edge information systems, smart-

devices and the existing environment (from old PLCs to smart object embedded in computing power).

Capable of working in distributed networks: they should gather, transfer and store in a reliable manner

all the information provided by smart sensors and actuators through the use of the IoT.

Based on a modular open architecture: the interoperability has to be ensured across different platforms

provided by several vendors along the value chain.

Incorporate human interfaces (HW & SW based): integration of user-friendly and reliable service to

make decision makers aware about the real time situation of the factory.

Fault tolerant: given by the encapsulation of models to activate prediction control loop and correctness

of automation systems.

3 http://www.popsci.com/scitech/article/2009-09/bmw-developing-augmented-reality-help-mechanics



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3. Gap Analysis

Information collected during WP1 “State of the art of Cyber-Physical Systems in Manufacturing “, both from

experiences from other programs and projects and the direct collecting of knowledge during the Knowledge

Capture Events and Interviews, provided the basis for the identification of the missing elements to actually

release the full adoption of CPS in EU Manufacturing industry.

In 1.5 Methodology it is described the methodology utilized to elicit, from information collected and already

reported in D1.1 - State of the Art of Cyber-Physical Systems, the key gaps to address in the proposed action

described in the Roadmap (D3.1, D3.2 and D3.3).

3.1. Methodology This section concentrates on the analysis and identifications of existing gaps. These can be considered as the

missing link between the strategic breakthroughs CPS adoption may bring into Manufacturing industry and the

major obstacles to their achievement. The identification of the gaps is the result of the sequence of steps listed

below: 1. Consolidation and validation of output from the SotA analysis, KCEs and Guru interviews4;

2. Identification and definition of clusters collecting the main characteristics of breakthroughs and obstacles;

3. Identification, for each of the previously identified cluster, of some sub-clusters gathering breakthroughs

and obstacles elements (see Figure 3). The idea is to have a set of homogeneous groups of specific identified

breakthrough to match against specific subset of obstacles. In the crossing we identify the gap to overcome

in order to remove the obstacle and achieve the envisaged result.

Figure 2 Identification of sub-clusters process

4 Details on this analysis can be found in the “State of the Art on Cyber-Physical Systems” deliverable.



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Figure 3 Cluster Matrix for the Gap identification - example

Crossing breakthrough and obstacle sub-clusters and selecting the most significant intersections will point out

the researched gaps that would be addressed in T3.1 Research roadmap with specific research initiatives

constituting the overall research roadmap (see 5 Conclusions and Outlooks). This first exercise was run in the project team to focus on a limited number of investigation areas (Gaps) as they

emerged from the results achieved during the Knowledge Capture and Validation Events. We utilize the number

of references (mentions) both for Breakthrough and Obstacle belonging to the Sub Cluster to define a priority

ranking.

Of course we acknowledge the possibility other important gap are present, but we want now to focus on the

higher ranked ones. During the validation phases we will verify if these areas are exhaustive or we need to

investigate other important areas

As stated in the previous section, each cluster has been analyzed and reviewed in order to understand if it was

possible to identify more specific breakthrough and obstacle sub-clusters which could describe more in detail

that cluster characteristics.

The idea is to match breakthrough and obstacle sub-clusters in a matrix, to select the most significant crosses,

and so to identify the gaps.

Please refer to 6 Annex for the detailed description and discussion of the clustering and ranking exercise.

In the following sections, the results of this analysis are reported. Starting from the definition given in D1.1

“State of the Art on Cyber-Physical Systems”, each cluster has been further sound out and breakthrough and

obstacle sub-clusters have been identified and briefly described (details are reported in Annex). Finally, the

resulting matrix has been fulfilled, the gaps addressing some open areas that need to be further explored during

the next project activities have been identified and described.

In our analysis, we did not consider specific classes of obstacles that we do not think are addressed in sCorPiuS

roadmap, so we do not associate Gaps to this obstacles.

Cluster x

GAP 1

GAP 2

GAP 1



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Please refer to 3.3 Additional findings from the Gap Analysis Exercise for a detailed discussion about these

classes of Obstacles and how these type of information will be discussed as recommendations in the sCorPiuS

roadmap.

These obstacle are reported for completeness of the results coming from the Data Collecting Phase, but are in

light blue.

3.2. Gap Analysis of 6 Clusters

3.2.1. Cluster 1: New data-driven services and business models In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “New data-driven

services and business models” was defined as follow:

“It relates the company as a whole, regarding top managerial decisions. CPS, with its digital world, opens a wide

range of new business possibilities. Not only to already configured enterprises, e.g. giving the possibility of being

closer to customers and offering high value services, but the appearance of new business models exploiting the

capabilities of this new technology”

From here, it is possible to identify some specific characteristics of the breakthroughs and obstacles collected

in this cluster which allow the identification of 6 main breakthrough and 7 main obstacle sub-clusters. Tables

including their detailed definitions are provided in session 7.1.

The following matrix shows the results of the analysis. In particular, crossing the identified breakthrough sub-

clusters with each obstacle sub-cluster, one main gap arises. This is found at the intersection between “Digital

business ecosystems for sensing product/services” and “Uncertain ROI - benefits and costs not clear and

missing KPI”. This means that CPS could generate new business opportunities driven by product sense systems

and service-oriented business models, but their costs and benefits are currently not well evaluated and therefore

the return on the investment (ROI) is still unknown and difficult to predict.

This obstacle mainly limits both the achievement of technical and technological innovative systems and

potential results from new business opportunities. Obstacles

CL1 New data-driven services

and business models

CPS and related infrastructure costs

Cultural issues - "Not me first" and readiness of people

CPS embedded complexity

Liability/ Laws

Macro-economic limits - EU as follower in advanced ICT and R&D results go to market

Need of guidelines & risk of disillusion

Uncertain ROI - benefits and costs not clear and missing KPI

Bre

akth

rou

ghts

Active information generation and big data generation

Customer driven responsive business models

Digital business ecosystems for sensing product/services X

Distributed collaboration - horizontal and vertical integration

Predictive management

Socio-economic changes

Table 3 CL 1 New data-driven services and business models - Gaps

3.2.2. Cluster 2: Data-based improved products In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Data-based

improved products” was defined as follow:



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“it concerns the breakthroughs coming from the digitalization of the product. The product behaves as an intelligent

component inside and beyond the factory, sharing information at different levels, enabling a better understanding

and configuration of the processes and services, and bringing a high value added to its usage”.




The results of the analysis are represented in Table 4. Here, it is possible to identify 2 main gaps that need to be

further explored. The first one arises from the intersection between “Data-driven products use/maintenance

(MoL)” and “Communication problems - smart products vs old factories”. Embedding CPS in final products

could simplify communication and information exchange among products and between factory and products.

In this way, companies could have visibility on the way their products are used, give customers guidance and

help in case of malfunctioning and also inform customers when preventive maintenance is needed. However,

the main problem limiting the achievement of these advantages is related with the average “age” of the factories.

Embedded products are relatively easy to produce; old factories are not so easy to modernize. Let a product

communicate with others and with the factory is not enough; factories that are able to understand are the real

missing part.

The second gap comes from the match between “Improve quality and added value of the product” and

“Complexity vs usability”. In this case, CPS ability to improve quality and value added of the products is limited

by their intrinsic increasing complexity.

Obstacles

CL 2 Data-based improved products Complexity vs usability Communication problems - smart products vs old factories

Bre

akth

rou

gh

Data-driven products use/maintenance (MoL) X

Data-driving processes

Facts and Feedback based Design and Production

Improve quality and added value of the product X

Table 4 CL 2 Data-based improved products - Gaps

3.2.3. Cluster 3: Closed-loop manufacturing In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Closed-loop

manufacturing” was defined as follow:

“it corresponds to an “expansion” of the limits of the company. It takes into account other stakeholders in the value

network, such as suppliers and customers, integrating their feedback into the production”.




Through the crossed analysis of breakthrough and obstacle sub-clusters, two main gaps emerge (see Table 5).

CPS could facilitate communication within and beyond the factory, allowing people to take decisions in a

decentralized way. However, data security, privacy and safety problems have an high impact on the achievement

of these benefits. In fact, they have been selected as primary cause of the missing implementation of

decentralized decision systems. The second gap shows that integration and interoperability issues affect the

potential benefits of horizontal and vertical integration. In other words, a rapid integration enabled by ubiquitous

communication and integration infrastructure enabling the closed-loop manufacturing cannot be obtained



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mainly due to difficulty on realizing interoperable systems. Other problems related to this topic are the value

chain technological fragmentation and the data security, privacy and safety.

Although further specific gaps have not been advised, it is also relevant to observe that “Flexible, resilient and

agile value chain” breakthrough sub-clusters need to be clearer and deeper considered for future studies as it

could be impacted by many obstacles. For example, “Integration and interoperability issues” and “VC

technological fragmentation” obstacles could negatively impact the achievement of highly flexible and self-

organizing value chains.

Obstacles

CL 3 Closed-loop manufacturing

Integration and interoperability issues

Need of standards

Security, privacy and safety

Stakeholders readiness

VC technological fragmentation

Bre

akth

rou

ghs

Autonomous decentralized decisions X

Customer in the loop

Flexible, resilient and agile value chain

Horizontal and vertical integration X

Real time control from data availability

Table 5 CL 3 Closed-loop manufacturing - Gaps

3.2.4. Cluster 4: Cyberized plant/ “Plug & Produce” In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Cyberized plant/

“Plug & Produce”” was defined as follow:

“it embraces the benefits that the adoption of CPS has in the plant. It permits a more holistic insight of the production

processes, a better information management, and facilitates tasks performance by mean of digital support”




From the analysis of the breakthroughs and obstacles included in Cluster 4 “Cyberized plant / Plug & Produce”,

a key topic that need further studies in order to be reached is related with the benefits from the plant flexibility

such as the ability to enable the information sharing between devices and the self-reconfiguration station. The

Cyber-physical flexibility enables the rapid intervention at shop floor level by means of augmented reality

solutions, and also allow to combine local and global levels in a natural manner. These opportunities are hard

to be reached mainly because difficulties in terms of time, effort and costs of transforming the current production

systems and limits related to the uncertain performance reliability and availability of the systems occur.

The same obstacles also affect the prediction and forecasting of the plant behavior and its related benefits

without being a specific gap.

Another area that emerged as requiring research correlated to the fact that from one side it is required an high

configurability of the system and the process, but in doing that we change the characteristic (layout, working

conditions, operators activity) of the environment, that could create potential risk to the security for the workers

and for the system itself.



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Obstacles

CL 4 Cyberized plant/ “Plug & Produce”

Difficulties in transforming current plant into CPS

Legacy integration + Extreme Performances Requirements

Missing of standardization and certification rules

Safety and security limits

Uncertain performance reliability

Bre

akth

rou

ghs

Cyber-physical flexibility X X

Full transparency

Monitoring & control

Prediction and forecasting

Real time information at right place

Reconfigurability X

Self-X (recovery, learning, analysis,…)

Traceability

Table 6 CL 4 Cyberized plant/ “Plug and Produce” - Gaps

3.2.5. Cluster 5: Next step production efficiency In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Next step

production efficiency” was defined as follow:

“it encompasses breakthroughs that enable a better utilization of management assets translated into a more

efficient production”




The following table shows the results of the analysis conducted for Cluster 5 Next step production efficiency.

In particular, the “Legacy and old technologies CPS migration” obstacle represents the main limit to the CPS

ability to improve energy efficiency and to optimize plant operations. This is because it makes difficult to find

solutions for an efficient and cheap integration of CPS in brownfield plants.

Obstacles

CL 5 Next step production efficiency

Education & cultural gaps inside & outside the plant

Lack of standard and interfaces

Legacy and old technologies CPS migration

Technological / Technical barriers

Bre

akth

rou

ghs

Allow small lot sizes

Cyber-physical production efficiency

Improve energy efficiency X

Plant operations optimization X

Faster engineering process

Flexible Equipment, Agile Processes

Improve logistics processes (reduce failures, inventory, …)

Predictive problem solving

Real and digital fusion

Self-learning zero defect manufacturing

Table 7 CL 5 Next step production efficiency - Gaps



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3.2.6. Cluster 6: Digital Ergonomics In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Digital

Ergonomics” was defined as follow:

“it corresponds to breakthroughs that make easier the integration of people with the new CPS environment.

Although the processes are getting more complex with CPS, e.g. more data is analyzed, people have to access them

in an easy and understandable way”




The identified gaps concerning this Cluster are reported in Table 8. The first one sees the advantages for

workers, which are facilitated in doing their job and take decisions thanks to the human automation co-working,

limited by problems related to their safety and by the lack of new regulations in terms of security; while the

second concerns the system over-functionalities that could limit the reduction of management complexity.

Obstacles

CL 6 Digital Ergonomics

Complexity (over functionalities)

Costs of digital ergonomics

CPS acceptance

Data security

Human safety and related aged regulations

Lack of skilled and educated workforce

Legacy Systems

Underestimate CPS possibilities

Bre

akth

rou

ghs

Enhanced humans sensing and intelligence X

Factory in my pocket

Reduce management complexity X

Table 8 CL 6 Digital Ergonomics - Gaps

3.3. Additional findings from the Gap Analysis Exercise Carrying out the analysis of Obstacles associated to Breakthrough executing the Gap Analysis Exercise, we

identified specific types of Obstacle not addressable in the sCorPiuS roadmap, via Research Initiatives or

Innovation Actions. That is due to the fact that these obstacles are related with macro-dynamics (social,

economic or technological) that sCorPiuS Roadmap could not expect to address. Another type of obstacles

that emerged require legal and standardization compliance where we think the Roadmap exercise will not

address in term of Research suggestions, but in terms of recommendations for monitoring or support actions.

Here following the above mentioned categories:

Cultural, Educational and Perception

To this category belong the following type of information we collected in our State of Art: “Cultural issues -

"Not me first" and readiness of people”, “Stakeholders readiness and attitude”, “Education & cultural gaps

inside & outside the plant”, “Underestimation and low trust of CPS possibilities”, “CPS acceptance”.

This call can be addressed according two main streams:

Education: There is the need to fully involve School and University in an significant effort to spread

awareness of CPS adoption concepts in manufacturing, promote them as motivation for young people to

approach the manufacturing reality.



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Consensus Generation: In this area it is necessary to bring CPS concepts outside the niche bounders of people

of the specific sector and to spread the possible advantages in adoption (e.g. support to impaired or elder

people) and to smooth down fears of adoption (e.g. issue related to security or privacy). With this respect it is

very interesting to see how it is evolving the general sentiment of people with respect to “self-driving” cars; in

very limited time people accept the fact they are no longer driving their own car, but it is an automated system

on their behalf)

Overestimation of costs

From the feedbacks we collected there is the perception that CPS technologies are “expensive” (both in term

of infrastructure and implementation) and that is independent from the expected advantages. We have

feedback like “CPS and related infrastructure cost” “Costs of digital ergonomics are high”.

With this respect we identified in the gap analysis exercise focused tasks to identify clear measurement of

benefits coming from implementation of CPS systems. On the other side we will recommend in our Roadmap

to consider (especially for SME) the opportunity for supporting modernization investments.

Law, Regulations, Technology Enablers and EU Macro Economic Factors

C-level people and Business and Operation managers are sensitive to aspects that are beyond the technical and

process aspects, like Liability of the utilization of CPS, both as a provider and as a utilizer and respect of

various laws and legislations. This bring up the point to carefully consider a review in some area of the

Legislation to address changing conditions and players.

Another aspect to consider is related with the almost complete dependency of Europe from the US based

technology suppliers and information “Giant”. Such situation, in conjunction with the average small size of

many European industry could create an unbalanced relationship that could impact attitude to invest in new

technologies like CPS.

Standards and certifications

In CPS arena many technologies are involved, interoperability is a key requirement emerged and addressed in

the Gap Analysis, but even the same technologies are not in some case mature enough to have defined a clear

de-facto standard able to preserve investment and allow a viable lifetime to implementation. The definition or

fostering of existing standards is definitely a point that need to be addressed.

For industrial manufactures, the increasing complexity of CPS-ized products, the evolution of the possible

implication and consequences of their utilization (e.g. privacy or responsibility) and considering that the same

product is commercialized in many countries , it brings up the issue of product certifications for each possible

market. That could represent a major effort to undertake (especially for SME with a limited production

volumes).

3.4. Gap Analysis Summary From the analysis carried out in the previous chapter from 3.2.1 to 3.2.6 we can summarize Table 9 - Summary

of Gap Analysis exercise. This table reports structure of potential gaps to investigate and to address with specific

Research Initiatives composing the sCorPiuS roadmap.



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Table 9 - Summary of Gap Analysis exercise

Cluster Breakthrough Gaps

CL 1 New data-driven services and

business models

Digital business

ecosystems for sensing

product/services

1. Uncertain ROI

Benefits and costs not clear

Missing KPI

CL 2 Data-based improved products

Data-driven products

use/maintenance (MoL) 2. Communication problems - smart

products vs old factories

Improve quality and

added value of product 3. Complexity vs usability

CL 3 Closed-loop manufacturing

Autonomous

decentralized decisions 4. Data Security and privacy

Horizontal and vertical

integration 5. Integration and interoperability

CL 4 Cyberized plant / “Plug &

Produce"

Cyber-physical

flexibility 6. Difficulties in transforming

current plant into CPS based

systems

Cyber-physical

flexibility 7. Uncertain performance

reliability

Reconfigurability 8. Safety and security limits

CL 5 Next step production efficiency

Improve energy

efficiency 9. Legacy and old technologies CPS

migration Plant operations

optimization

CL 6 Digital Ergonomics

Enhanced humans

sensing and intelligence 10. Workers role and related aged

regulations

Reduce management

complexity 11. Management Complexity (over

functionalities)

Here following it is listed a short description of 11 Gaps:

1. Uncertain ROI - Benefits and costs not clear- Missing KPI

CPS introduce a new paradigm in the way production and business are going to be managed. New

business models are envisaged, new players enter the stage and potential big investments (both in

technology, but also in organization and change management) are expected. On the other side shorter

and shorter pay-back is requested. In this context it is crucial to provide decision makers of tools for

forecasting and monitoring benefits and risks. The challenge is the definition of simple and reactive

tools and methodologies, supported by a strong technology able to provide these information.

2. Communication & Coexistence - smart products vs old factories

Products are evolving very fast, they embed high level technology, they are designed to be

integrated in a complex ecosystem (made of smart customers asking for complex high performance

services). The problem arises from the fact that these products are manufactured in “classical”

environment, not designed for supporting this kind of product. The question is how to evolve the



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production environment to support the fast evolution of the complex and poly morphed (hard

components, software, services, eco-sustainability, etc.) products.

3. Complexity vs usability

Factory environment is most often focus on the production process. Tools to produce can support

sophisticated “production” technologies (e.g. electronic, nanotechnologies, lasers, etc.). CPS

enabling technologies and CPS related process (see 2.2) add a new level of complexity, not always

perceived as part of the “core business”. From that comes the need to a seamless, easy-to-use CPS

environment to be deployed in transparent way in the factory and along the Value Chain.

4. Data Security and Privacy

CPS is dealing with big amount of data. Some of them are related to products, some to process,

some to people (worker and customer). A complete new scale of magnitude of complexity arises

from this landscape to ensure that data are available, protected and reserved.

5. Integration and interoperability

Value chains along the process and supply chain flow and product design conceptualization, design

engineering, involve a number of players and components (technologies, applications and data

semantic). New data interconnection paradigms (as NPC-UA) are addressing this aspects, but CPS

diffusion is challenging this approach well beyond the classical production and design arena,

involving customers, social bodies and for a long time (since product idea to product disposal or

recycling). IT is necessary to interpreter the Integration and interoperability along a much broader

range in term of geographical spread, but also as time horizon.

6. Difficulties in transforming current plant into CPS based systems

As mentioned above smart products are going to be produced in existing facilities. What are the

criteria to define an evolutionary path for these infrastructure without impacting the process

performances and efficiency ? IT is required both a technological, but even methodological

approach to such challenge.

7. Uncertain performance reliability

CPS approach is very pervasive and it promises a huge increase in potential performance of the

systems, but at the same time they raise the question about the reliability of the overall systems and

the implication of a failure or a degradation of their functions.

8. Safety and security limits

CPS are expecting to be a pervasive technology on the shop floor, where more and more functions

and operations are going to be delegated to autonomous systems able to take decisions. It is crucial

to identify clear collaboration pattern among human operator and automated systems to avoid

possible problem for safety.

9. Legacy and old technologies migration to CPS

While it is an open question how to migrate the overall production structure towards new

paradigms (see bullet 6), we have also to consider the specific technology already present on the

shopfloor (e.g. automated lines, robots, power management, etc.). Also at the level of the basic

monitoring, actuation and cooperation level a dramatic change of the technologies or at least their

encapsulation is required to support CPS approach.

10. Workers role and related aged regulations

With CPS the role of the human being and especially the role of the worker is changing. Not just

the safety and security aspects need to be considered (see bullet 8), but also the operational co-



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existence in the physical space of the workspace. The human factor can contribute to make more

flexible the CPS system or support it in case of problems, but how they can interact, with which

interfaces? On the other end, the social and demographic megatrend are challenging the profile of

the classical “blue collar”, both from the age perspective and for the knowledge of the workers.

11. Management Complexity (over functionalities)

Issue to address at shop-floor level from the adoption of CPS are also present at management and

decision taking level. Potentially huge amount of information are available to take informed

decision, it is important that these information are made available when needed to whom is actually

concerned and with the appropriate context to avoid wrong decisions. The risk is that the

complexity of the viewable context is actually depleting the quality of the available information to

support the right choice.

The following “Table 10 Key aspects to address as recommendations” contains the result of the analysis of

findings not directly addressable with Research activities specified in the sCorPiuS roadmap as described in 3.3

Additional findings from the Gap Analysis Exercise.

Table 10 Key aspects to address as recommendations

Cultural, Educational and Perception

Overestimation of costs

Law, Regulations, Technology Enablers and EU Macro Economic Factors

Standards and certifications

As stated above we will consider these aspects to be addressed in a set of recommendations to accompany the

roadmap. We envisaged mainly two sets of recommendations, one more oriented to address the soft and

consensus aspects, the second more oriented to the Regulations, Standards and Enabling Technologies key

features expected to endorse CPS implementation



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4. Vision

The sCorPiuS team, to define the vision, started taking inspiration from previous works; acknowledging how

these have changed our way of seeing the manufacturing sector in general and how ICT is applied more

specifically.

Relation with previous roadmaps

A first key point we take into consideration is that the manufacturing firms have to take into account both the

product-lifecycle and the assets/factory lifecycle. This concept isn’t new for IT in manufacturing, but is still

under implementation in several realities and explains in a clear and easily understandable way that a

manufacturing company has to keep focus both on products, maximizing the delivery of value to customers

through a proper design, development, engineering and production as well as how the production process itself

will run into years.

A second important reference is the ISA95 automation pyramid, also in use since years, which explains

graphically the hierarchy of the different enterprise systems.

These two important aspects of ICT for manufacturing have been merged into a single synergic vision by

Pathfinder, which declines the FOF roadmap for the simulation and introduces an important shift within the

automation domain, changing the traditional automation pyramid, introducing the relation with CPS as in the

following picture.

Figure 4: Pathfinder roadmap vision and scheme; peculiar importance has the automation pyramid in the bottom layer, which represents

how the CPS restructures the traditional layers of the automation pyramid (Pathfinder roadmap whitepaper)

Within any analysis of ICT in manufacturing Industrie 4.0 has to play an important role, due to its impact and

relevant developments, as well as for its role as “inspiration” for other counties and initiatives. One of the key

aspects of Industrie 4.0 are the Cyber-Physical Systems. RAMI, The Reference Architectural Model

Industrie 4.0 provides an interesting view point of Cyber Physical Systems. In fact it maps the relations between

“Life Cycle & Value Streams”, (based on IEC62890), the enterprise IT and control systems hierarchies

(IEC62264) and machines structured properties. The “RAMI” architecture in fact shows how a product has a

lifecycle both as “type” and as a physical “instance”; the latter is integrated in the different enterprise logical

layers and has relations with the IT hierarchical layers. Traditionally the more advanced functions were

delegated only to systems higher in the hierarchy; “RAMI” shows how now this vision is encompassed.



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Figure 5: RAMI – Industry 4.0 Reference Architecture (ZVEI - Industrie 4.0 whitepaper)

Finally the Industrial Internet Consortium reference architecture, gives another view on the different layers and

IT aspects, depicting how different stakeholders concerns on the system of interest should be taken into account

and structured in a well defined stack.

Figure 6: IIC Reference Architecture

sCorPiuS vision

sCorPiuS vision integrates the pervious referenced architectures and visions, enlarging the RAMI, specifying

that both the factory/assets as well as the products have a lifecycle which has to be managed. The IT hierarchies

of the CPS architecture are also taken into account; sCorPiuS uses for the factory/asset-lifecycle the IEC62264

as RAMI; for the product-lifecycle the layered approach an IoT-Architecture (inspired by IIC and other IoT-

related standards) derived approach has been considered due to the different kind of environment and ecosystem

which will have to use the CPS. On top of the CPS layers, considering the enterprise approach, the different

legacy systems whose CPS will have to interact with have been also considered. In fact SCORPIUS

acknowledges that CPS intrinsic existence defies the concept of rigid hierarchical levels, being each CPS

capable of complex functions across all layers and thus adopts an updated version of the pyramid representation,

where the field level features CPS capable of articulated functions (thus in contact with all the pyramid layers)



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while still a hierarchical structure is preserved. Single CPS are hierarchically arranged in groups of CPS, to

deliver specific functions. CPS elements, as long as they comply with the interaction standard, may be supplied

by any automation provider.

The usual automation pyramid (ISA95) is therefore tilted as in Pathfinder, but also extended to include all the

IT systems whose are used outside the usual domain of automation, but have to be considered in an extended

lifecycle perspective. The sCorPiuS Collaboration Pyramid therefore includes the CPS-Automation Pyramid,

which derives from ISA95, but also Product Lifecycle Management Tools, covering the design and engineering

phases and the CRM as well as IoT solutions, which will enable to develop and improve services, usage and

reuse/recycle of the products.

This vision therefore links together all the tools on the market (PLM, PLC, SCADA, MES, ERP) and under

development (CPS and IoT) whose data and information are relevant to create a product-service centric closed

loop collaboration.

On the product axes perspective both the virtual product (Design and Engineering) and the physical one are

taken into account. On the factory lifecycle axes both the physical and digital one are considered. This creates

a unique data and information flow that will first of all link all stakeholders in the manufacturing enterprise

domain.

Within this vision, the manufacturing enterprise IT users can have access, depending on their roles and needs,

to the data available within all the different represented systems, achieving therefore an in-depth knowledge

that today is not available. Currently, manufacturing data are segmented, detailed and planned for a single scope,

stored within the legacy systems, thus preventing the “digital continuity” that would let use them in the optimal

way, independently from where and how they have been collected. CPS will instead be able to provide all the

needed information from the physical world while the Cyber-Physical-Collaboration environment will enable

an efficient analysis, management, sharing and usage of the data and the knowledge elaborated from them and

from the experience of involved people.



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Figure 7: sCorPiuS – Preliminary concept architecture



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5. Conclusions and Outlooks

This whitepaper presented and motivated the importance of Cyber-Physical Systems for EU manufacturing,

their current status, the main clusters of gaps which can be addressed by EU research and Obstacles, which

needs not only research, but also standardization, the introduction of new laws and norms to be overcome. The

overall vision of the usage of CPS in manufacturing will take some years to be implemented, but will

revolutionize the current concepts of factory automation, defying the traditional hierarchical levels, being each

CPS capable of complex and cross-layer functions. This will enable a better usage of data and information, more

autonomous systems, able to self arrange and interact with other as well as legacy systems, with the emergence

of new possibilities to create efficiency and the development of new business models.

This document is therefore a first outlook of this potential future. All the analysis in this preliminary

roadmapping white paper have been developed with the scope of supporting the European Commission and

EFFRA to make the factory of the future enabled by CPS. Concepts, ideas and input were provided from experts

collected during workshops and meetings.

This whitepaper will be extended and verified with the sCorPiuS community as well as discussed with IoT,

Industrial Internet, Industrie 4.0 and CPS experts. The vision will be used therefore to evaluate if all the gaps

have been found and from the emerged gaps research priorities and research topics will be derived.

We would be therefore grateful to all the readers who will provide feedback and input for improvements and

further development of the concepts and ideas presented in the document, through the next sCorPiuS foreseen

meetings, the open consultations and the discussion panels.

In the following Figure 8: Conceptual flow of sCorPiuS activities current Vision (draft), will be validated in

public event with participation of experts and stakeholder as mentioned above to come to the final Vision (D2.2).

On the other hand the final Vision will constitute one of the key elements for validating the Draft Roadmap

(D3.1).

Figure 8: Conceptual flow of sCorPiuS activities



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6. Annex

6.1. Cluster 1: New data-driven services and business models In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “New data-driven

services and business models” was defined as follow:

“it relates the company as a whole, regarding top managerial decisions. CPS, with its digital world, opens a wide

range of new business possibilities. Not only to already configured enterprises, e.g. giving the possibility of being

closer to customers and offering high value services, but the appearance of new business models exploiting the

capabilities of this new technology”


in this cluster which allow the identification of 6 main breakthrough and 7 main obstacle sub-clusters.

CL 1: New data-driven services and business models Description

Bre

akth

roughs

Active information generation and big data generation It concerns with the opportunity to facilitate

big data analysis and to have active

information availability

Customer driven responsive business models It considers new business models enabled by

CPS ability to optimize and realign

production flows following market needs

Digital business ecosystems for sensing product/services It includes new business opportunities driven

by product sense systems and service-oriented

business models, which allow better control

of the ecosystem

Distributed collaboration - horizontal and vertical

integration

It concerns with the CPS ability to enhance

companies cooperation through ecosystems of

partners for service execution

Predictive management It considers new management opportunities

coming from the ability to predict the future

factory behavior

Socio-economic changes It includes socio-economic breakthroughs

related to the possibility to make production

return from less developed countries to EU Table 11 CL 1 Breakthrough sub-clusters description

CL 1: New data-driven services and business models

Obstacles

CPS and

related

infrastructur

e costs

Cultural issues

- "Not me first"

and readiness

of people

CPS

embedded

complexity

Liability/Laws Macro-

economical

limits - EU as

follower in

advanced ICT

and R&D

Need of

guidelines &

risk of

disillusion

valley

Uncertain

ROI -

benefits

and costs

not clear

and



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results go to

market

missing

KPI

It considers

costs due to

the

integration of

CPS into

factories

Cultural issues

related to the

fact that most of

the companies

would like to

see their

competitors

before adopting

this technology

It considers

the growing

complexity to

be addressed

due to the

management

of

decentralised

systems

It includes

obstacles

caused by

missing

standards and

laws and too

slow,

confused and

restrictive

regulations

It considers

obstacles due to

the fact that big

players of Cloud

storage are US

and not EU

based

companies

It considers

the need to

come to a

common

understanding

and sharing

consensus

because

results may

take time to

arrive (avoid

the risk of

disillusion

valley)

It lists a

series of

obstacles

related to

the need of

having

clear and

short term

ROI

evaluation

Table 12 CL 1 Obstacle sub-clusters description

6.2. Cluster 2: Data-based improved products In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Data-based

improved products” was defined as follow:

“it concerns the breakthroughs coming from the digitalization of the product. The product behaves as an

intelligent component inside and beyond the factory, sharing information at different levels, enabling a better

understanding and configuration of the processes and services, and bringing a high value added to its usage”.



CL 2: Data-based improved products Description

Bre

akth

rou

gh

s

Data-driven products use/maintenance (MoL) It considers the products beyond the factory and

the information they could drive to the factory

when utilized

Data-driving processes It includes benefits from the data gathering and

translation in knowledge. This could give better

understanding and more complete data-driving

processes.

Facts and Feedback based Design and

Production

It lists benefits from the early integration of

customer feedback and know-how into the

production control systems

Improve quality and added value of the product It lists benefits considering that CPS not only

enhance the production process but gives a value

added to the product (increases its

characteristics) Table 13 CL 2 Breakthrough sub-clusters description



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CL 2: Data-based improved products

Obstacles

Complexity vs usability Communication problems - smart products vs old

factories

It considers the trade-off between complexity and

usability as a limit for CPS implementation

It includes communication problems between smart new

products and old factories


6.3. Cluster 3: Closed-loop manufacturing In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Closed-loop

manufacturing” was defined as follow:

“it corresponds to an “expansion” of the limits of the company. It takes into account other stakeholders in the

value network, such as suppliers and customers, integrating their feedback into the production”.



CL 3: Closed-loop manufacturing Description

Bre

akth

roughs

Autonomous decentralized decisions It considers benefits connected with the opportunity to take

decisions at different levels in autonomous way

Customer in the loop It includes improved product design through data and

customers feedback and from product usage. It also considers

the positive impact on post-production phases.

Flexible, resilient and agile value chain It includes all the typical benefits of highly flexible and self-

organizing value chains

Horizontal and vertical integration It considers improved performance from intra-factory and

inter-factories process integration

Real time control from data availability It is related with data availability benefits such as real time

monitoring of the value chain, ubiquitous sensing and better

understanding of the ecosystem Table 15 CL 3 Breakthrough sub-clusters description



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CL 3: Closed-loop manufacturing

Obstacles

Integration and

interoperability issues

Need of standards Security, privacy and

safety

Stakeholders

readiness

Value chain

technological

fragmentation

It includes

communication and

collaboration issues

between organizations,

humans and systems

across the entire value

chain

It includes the main

problems to let

different systems

communicate at

different levels (from

sensors to database)

It concerns with the

security and privacy

of data issues

It considers the

people readiness to

share data along the

value network

problems

Also called

“boomerang

effect”: it considers

the fact that if in

the middle of the

chain a company

doesn't adopt CPS,

the whole value

chain won't take

benefits from CPS

implementation Table 16 CL 3 Obstacle sub-clusters description

6.4. Cluster 4: Cyberized plant/ “Plug & Produce In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Cyberized plant/

“Plug & Produce”” was defined as follow:

“it embraces the benefits that the adoption of CPS has in the plant. It permits a more holistic insight of the

production processes, a better information management, and facilitates tasks performance by mean of digital

support”



CL 4: Cyberized plant/ “Plug &

Produce”

Description

Bre

akth

rou

gh

s

Cyber-physical flexibility It considers benefits from plant flexibility such as the ability

to enable the information sharing between devices and the

self-reconfiguration station

Full transparency It contains all the benefits driven by the full data availability

Monitoring & control Monitoring and production control: this sub-cluster includes

benefits that give a complete and continuously updated

representation of what is happening in the shop-floor in real

time

Prediction and forecasting It includes breakthroughs related with the prediction and

forecasting of the plant behavior



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Real time information at right place It enables more comprehensive products and production, due

to the fact that information can be made available faster than

before and that everyone can reach easily the information he

needs

Reconfigurability It contains benefits related to the CPS adoption as they allow

the auto-reconfirmation of the system thanks to their ability to

auto-reconfiguring themselves

Self-X (recovery, learning, analysis,…) It considers breakthroughs related to the ability of self-

learning, recovery and analysis of machineries which are able

to learn from the outside environment in an independent and

autonomous way

Traceability It sees the benefits that the traceability of the product and the

transparency of the production can have for a manufacturing

company Table 17 CL 4 Breakthrough sub-clusters description

CL 4: Cyberized plant/ “Plug & Produce”

Obstacles

Difficulties in transforming current plant into CPS

Legacy integration + Extreme Performances Requirements

Missing of standardization and certification rules

Safety and security limits

Uncertain performance reliability

It considers the difficulties in terms of time, effort and costs of transforming the current production systems

It includes the limits of integrating CPS with the current systems, the need of bridge technologies and the heterogeneity of hardware and software

It states that standardization and certification rules are still missing

It considers the limitations originated from data safety and security

It includes limits related to the reliability and availability of the systems


6.5. Cluster 5: Next step production efficiency In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Next step

production efficiency” was defined as follow:

“it encompasses breakthroughs that enable a better utilization of management assets translated into a more

efficient production”





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CL 5: Next step production efficiency Description

Bre

akth

rou

gh

s

Allow small lot sizes It identifies the benefits of addressing small

volume lots in efficient way and at

predicted quality

Cyber-physical production efficiency It considers the machinery optimization

through information gathered at equipment

and product level

Faster engineering process It includes the benefits related with the

reduction of test time, superfluous test and

design phase

Flexible Equipment, Agile Processes It is related with benefits from flexible and

agile process chains over several product

lines and equipment

Improve logistics processes (reduce failures, inventory, …) It includes benefits like the increase of

undirected productivity by automation

ability to reduce failure rate, reduce

inventory levels and reduce re-planning

time

Predictive problem solving It considers breakthroughs related to the

fact that CPS could help in predict

problems, anticipating them also at

machinery level

Real and digital fusion It refers to the close work between

manufacturing reality and engineering data

Self-learning zero defect manufacturing Faster and better learning from mistakes

and process parameters would facilitate

zero defect manufacturing objectives

Improve energy efficiency The reduction of energy and media

consumption is made possible by intelligent

energy/media management Table 19 CL 5 Breakthrough sub-clusters description

CL 5: Next step production efficiency

Obstacles

Education & cultural gaps inside & outside the plant

Lack of standard and interfaces

Legacy and old technologies CPS migration

Liquid systems Tecnological / Technical barriers

It considers problems related to skills requirement in operators which have to deal with CPS and the necessity to create

It states that standards and interfaces are still missing

It includes difficulties in finding solutions for an efficient & cheap integration of CPS in brownfield plants

It contains limitations in identifying the boundaries of the system



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AD-HOC training models


6.6. Cluster 6: Digital Ergonomics In chapter 3.1.1 of the “State of the Art on Cyber-Physical Systems” deliverable, the cluster “Digital

Ergonomics” was defined as follow:

“it corresponds to breakthroughs that make easier the integration of people with the new CPS environment.

Although the processes are getting more complex with CPS, e.g. more data is analysed, people have to access them

in an easy and understandable way”



CL 6: Digital Ergonomics Description

Bre

akth

roughs

Enhanced humans sensing and intelligence It includes advantages for workers which are

facilitated in doing their job and take decisions thanks

to the human automation co-working

Factory in my pocket It considers the benefits related to the opportunity to

access and control plant data and use it to take

decisions everywhere and in real time

Reduce management complexity It includes breakthroughs related to the complexity

reduction. Among others, these smart systems consent

to give the right information to the right person at the

right moment which implicate a significant reduction

in management complexity

Table 21 CL 6 Breakthrough sub-clusters description

CL 6: Digital Ergonomics

Obstacles

Complexity (over functionalities)

Control limits - balancing workers and

Costs of digital ergonomics

CPS acceptance

Data security

Human safety and related aged

Lack of skilled and educated workforce

Legacy Systems

Underestimate CPS possibilities



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technologies

regulations

It considers difficulties related to the fact that CPS over-functionalities could improve the utilization level of complexity

It includes the need of CPS acceptance in the working environment problems due to the fact that not everybody understand what CPS is

Data security and privacy

It includes limits elicited by the lack of human safety and security

It contains obstacles to CPS implementation caused by the lack of skilled and educate workforce

It includes difficulties in finding solutions for an efficient and cheap integration of CPS in brownfield plants

It refers to the unclear knowledge of CPS possibilities



White Paper

Project ID:

636906 29/02/16


References

Lee, E. A. (2006). Cyber-physical systems—are computing foundations adequate? NSF Workshop on Cyber-

Physical Systems: Research Motivation, Techniques and Roadmap, Austin, Texas.

Wan, L., Törngren, M., Onori, M., (2015). Current status and advancement of cyber-physical systems in

manufacturing. Journal of Manufacturing Systems.

Yu, C., Xu, X., Lu, Y., (2015). Computer Integrated Manufacturing, Cyber-Physical Systems and Cloud

Manufacturing – Concepts and relationships. Manufacturing letters.

Hu, F., Lu, Y., Vasilakos, A.V, Hao, Q., Ma, R., Patil, Y., Zhang, T., Lu, J., Li, X., Xiong, N.N., (2016).

Robust Cyber-Physical Systems: Concept, models and implementation. Future Generation Computer Systems.

Monostori, L., (2014). Cyber-Physical production systems: Roots, expectations and R&D challenges. Procedia

CIRP 17.

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