d1.5 fitman reference architecture - cordis...project id 604674 fitman – future internet...

Project ID 604674 FITMAN – Future Internet Technologies for MANufacturing

23/12/2013 Deliverable D1.5: FITMAN Reference Architecture

FITMAN Consortium Dissemination: Public 1/60

D1.5

FITMAN Reference Architecture

Document Owner: Domenico Rotondi (TXT)

Contributors: Jesus Benedicto (ATOS), Óscar Lázaro (INNOVALIA), Michele Sesana (TXT), Sergio

Gusmeroli (TXT)

Dissemination: Public

Contributing to: WP1

Date: 23/12/2013

Revision: 1.0




VERSION HISTORY

VERSION DATE NOTES AND COMMENTS

0.1 01/07/2013 TOC AND INITIAL DRAFT

0.2 19/07/2013 TOC REVISION AFTER FITMAN MEETING IN MADRID

0.3 29/07/2013 FIRST CONTRIBUTIONS ON FITMAN TRIALS

0.4 30/08/2013 NEW DRAFT

0.5 19/09/2013 PARTNERS CONTRIBUTIONS OF FITMAN TRIALS AND SECTION 2

0.6 27/10/2013 DOCUMENT REVISION AFTER FITMAN ATHENS MEETING

0.7 07/10/2013 DOCUMENT OBJECTIVES AND STRUCTURE REVISION

0.8 15/10/2013 FIRST COMPLETE DRAFT OF SECTION 3

0.9 29/11/2013 FIRST COMPLETE VERSION FOR INTERNAL REVIEW

1.0 23/12/2013 VERSION 1.0

DELIVERABLE PEER REVIEW SUMMARY

ID Comments Addressed ()

Answered (A)

1 Refer to other deliverables for trials IT

infrastructures and requirements Removed the section with the trials IT

requirements and infrastructures description

2 Add FITMAN cloud computing relevance

and Cloud Chapter GEs selection rationale Added Section 3.5.6

3




Table of Contents

EXECUTIVE SUMMARY ................................................................................................................................... 5

1. INTRODUCTION ....................................................................................................................................... 7

1.1. DELIVERABLE OBJECTIVES ................................................................................................................... 7 1.2. DELIVERABLE ORGANIZATION ............................................................................................................... 8

2. THE MANUFACTURING DOMAINS AND THE FITMAN TRIALS ................................................. 9

2.1. THE FITMAN MANUFACTURING DOMAINS ........................................................................................... 9 2.1.1. Manufacturing Challenges........................................................................................................... 9 2.1.2. Manufacturing Characteristics .................................................................................................. 12 2.1.3. Smart Factories ........................................................................................................................... 13 2.1.4. Digital Factories ......................................................................................................................... 14 2.1.5. Virtual Factories ......................................................................................................................... 15

2.2. THE FITMAN SMART FACTORY TRIALS ............................................................................................... 16 2.2.1. Automotive Supplier (Spain) ...................................................................................................... 16 2.2.2. White Goods OEM (Italy) ........................................................................................................... 17 2.2.3. Textile/Clothing (Italy) ............................................................................................................... 18 2.2.4. Aeronautics OEM (Italy) ............................................................................................................ 18

2.3. THE FITMAN DIGITAL FACTORY TRIALS ............................................................................................ 19 2.3.1. Automotive OEM (Germany) ..................................................................................................... 19 2.3.2. Aeronautics OEM (Italy) ............................................................................................................ 20 2.3.3. Construction Industry (Portugal) ............................................................................................... 21 2.3.4. Furniture (Spain)........................................................................................................................ 22

2.4. THE FITMAN VIRTUAL FACTORY TRIALS ............................................................................................ 22 2.4.1. Plastic Industry (France)............................................................................................................ 23 2.4.2. Manufacturing Resource Management (UK) ............................................................................ 23 2.4.3. LED Lighting (Germany) ........................................................................................................... 24 2.4.4. Machinery for Wood (France) ................................................................................................... 25

3. THE FITMAN REFERENCE ARCHITECTURE ................................................................................. 26

3.1. WHY A REFERENCE ARCHITECTURE .................................................................................................... 26 3.2. THE FITMAN SMART FACTORY REFERENCE ARCHITECTURE ............................................................... 30 3.3. THE FITMAN DIGITAL FACTORY REFERENCE ARCHITECTURE ............................................................ 35 3.4. THE FITMAN VIRTUAL FACTORY REFERENCE ARCHITECTURE ............................................................ 39 3.5. THE FITMAN MANUFACTURING DOMAINS ARCHITECTURES AND FI-WARE GES ................................ 44

3.5.1. The FITMAN Enablers .............................................................................................................. 44 3.5.2. The FITMAN Reference Architectures and GEs /SEs .............................................................. 44 3.5.3. The FITMAN Smart Factory GEs/SEs ...................................................................................... 45 3.5.4. The FITMAN Digital Factory GEs/SEs .................................................................................... 48 3.5.5. The FITMAN Virtual Factory GEs/SEs .................................................................................... 51 3.5.6. The FI-WARE Cloud Hosting GEs in FITMAN ....................................................................... 55

4. CONCLUSIONS AND NEXT STEPS ..................................................................................................... 57

5. REFERENCES ........................................................................................................................................... 58




Table of Figures

Fig. 3-1: Role of a Reference Architecture............................................................................. 27 Fig. 3-2: Derivation of specific architectures and implementations ....................................... 27

Fig. 3-3: Industrie 4.0 Reference Architecture for connecting the IoT with the IoS.............. 28 Fig. 3-4: FITMAN DoW draft Reference Architecture .......................................................... 29 Fig. 3-5: The FITMAN Smart Factory Reference Architecture ............................................. 31 Fig. 3-6: The FITMAN Smart Factory Gateway Layer Architecture..................................... 33 Fig. 3-7: The FITMAN Smart Factory Back End Layer Architecture ................................... 33

Fig. 3-8: The FITMAN Digital Factory Reference Architecture ........................................... 36 Fig. 3-9: The FITMAN Digital Factory PL Data Management Layer Architecture .............. 37 Fig. 3-10: The FITMAN Digital Factory PL Data Visualization & Manipulation Layer

Architecture ............................................................................................................................ 39 Fig. 3-11: The FITMAN Virtual Factory Overall Architecture ............................................. 41

Fig. 3-12: The FITMAN Virtual Factory Assets Management Layer Architecture ............... 42 Fig. 3-13: The FITMAN Virtual Factory Interoperability & Collaboration layer Architecture

................................................................................................................................................ 43 Fig. 3-14: The GEs / SEs graphical representations ............................................................... 45 Fig. 3-15: The FITMAN Smart Factory Reference Architecture and GEs / SEs ................... 47 Fig. 3-16: The FITMAN Digital Factory Reference Architecture and GEs / SEs ................. 50

Fig. 3-17: The FITMAN Virtual Factory Reference Architecture and GEs / SEs ................. 54




EXECUTIVE SUMMARY

The FITMAN D1.5 “FITMAN Reference Architecture” is a public document delivered in the

context of WP1 Task 1.5 that reports, as the title implies, the structure and rationale of the

FITMAN reference architecture, based on Future Internet technologies, for each of the three

manufacturing domains identified by the EFFRA (European Factory of the Future Research

Association) and addressed by the project:

Smart Factory that focuses on agile manufacturing, process automation control, and

tools for sustainable manufacturing;

Digital Factory in which ICT is focused on improving the design of production and

manufacturing systems, the product life cycle management;

Virtual Factory that addresses supply chain management, product-service linkage

and management of distributed manufacturing.

An architecture is meant to capture and identify the high-level structure of a system and

assure that the identified architectural elements and their relationships meet the needs and

objective of the system or domain to be modelled. The manufacturing domain is quite wide,

as is reflected in the FITMAN project, to be effectively modelled by one architecture, even

by three architectures one for each of the EFFRA manufacturing domains. Therefore, the

design of one single architecture for the whole Future Internet for Manufacturing domain is

not reasonable if it has to be effective.

A better approach is to design a Reference Model, or Reference Architecture, that provides a

more abstract description from which several concrete architectures for the specific real

systems can be actually drawn. A Reference Architecture focuses on capturing the main

architectural characteristics of a set of systems, providing indications and guidelines for the

design of actual architectures for a specific system.

A Reference Architecture is normally focused on providing:

a common lexicon or taxonomy that is tied to the application domain (i.e., automation

in the FITMAN case);

a common architectural vision that helps in focusing common elements and in

deriving synergic actual architectures;

modularization that helps in focusing subsequent refinement activities and in assuring

integration and, to some extent, interoperability.

Reference Architectures facilitate the design and management of product family architecting

and evolution, smoothing impacts due to market changes in contexts and needs.

This document therefore first tries to characterize the envisaged main evolutionary trends of

the manufacturing domain, in terms of envisaged key functionalities and technologies, taking

into account analysis and forecasts from authoritative documents such as the Industrial

Internet, the German Industrie 4.0 initiative, FInES (Future Internet Enterprise System),

EFFRA and ActionPlanT roadmaps and documents, as well as other documents available in

literature.

Afterwards it better characterizes the three EFFRA manufacturing domains, and provides a

quick overview of the FITMAN trials to provide to the reader evidence of how these trials

cover the three EFFRA manufacturing domains, as well as how to match and interpret the

key trends that are expected to characterize the future on manufacturing.

The document then describes the three Reference Architectures identifying their layering and

functional components and their relationships, as well as the rationale behind them. Each of




the three Reference Architectures is structured in two or three sub-layers, the lowest one

interfacing the data sources specific for the manufacturing domain at hand, and each sub-

layer is furthermore structured in modular components focused on specific functionalities.

Finally the three Reference Architectures are revisited taking into account the FI-WARE

Generic Enablers (GEs) and the FITMAN Specific Enablers (SEs) and how these enablers

are positioned in them and could fulfill the functionalities envisaged for the architectural

components.

Cloud computing technologies are expected to play a relevant role in the near future for

manufacturing, especially to support SMEs. Therefore the three Reference Architectures

must be read taking into account that the envisaged functional components will be, sooner or

later, provided by software elements deployed and running on cloud infrastructures.

Unfortunately, the cloud IaaS, PaaS and SaaS are simple service models that applies to any

application domain and therefore their functional elements cannot be identified as explicitly

contributing to one application domain’s specific architectures and to the FITMAN

Reference Architectures in particular.

Anyway, the final section that reviews the FITMAN Reference Architectures in the light of

the FI-WARE GEs and FITMAN SEs is completed with a survey of the selected GEs from

the FI-WARE Cloud Chapter to better highlight the contribution the FITMAN trials can

provide to assess the FI-PPP provided Future Internet technologies in the manufacturing

area.

A final consideration is required on the use of the FITMAN Reference Architectures, and of

their related GEs/SEs, by each of the FITMAN trials, or other manufacturing contexts

outside the FITMAN project.

As highlighted above, a Reference Architecture captures the main characteristics of a

specific system or domain and only provides a framework for the actual architecture of a

specific context (e.g., a FITMAN trial). A similar consideration is valid for the identified

GEs and SEs; the GEs and SEs aggregation and positioning in the Reference Architectures is

intended just to fulfil the envisaged functional requirements and therefore must be

considered just as indicative of the real implementation in a specific manufacturing context.

Therefore, in the respective T4.1-T5.1-T6.1 tasks, each FITMAN trial or manufacturing

context must draw its own specific architecture finalizing one or more of the three FITMAN

Reference Architectures and must pick up the mix of the identified GEs and SEs that best fit

its needs, requirements and constraints complementing the picture with Trial, or context,

Specific Components (TSCs).




1. INTRODUCTION

1.1. Deliverable Objectives

The FITMAN objective is to provide industry-led use case trials in the Smart, Digital and

Virtual Factories, as defined by the EFFRA roadmap for the Factory of the Future, based on

Future Internet technologies as made available within the EU FI-PPP programme (FI-WARE

GEs) or provided by the FITMAN project itself (FITMAN Specific Enablers – SEs, or Trial

Specific Components –TSCs).

Each trial will combine suitable sets of GEs and SEs, and will develop its own TSCs, to

develop and deploy its own service platform to provide services to the community envisaged

by each specific trial and assess, in real contexts, the suitability of the identified Future

Itnernet technologies.

This deliverable, as envisaged in the DoW, describes the FITMAN Reference Architecture

taking into account the following elements:

the draft FITMAN architecture s reported in the Description of Work (DoW);

the IT requirements collection and analysis performed in the start phase of the

FITMAN project and reported in the FITMAN D1.2 [48] deliverable;

the analysis performed and reported in the FITMAN D1.3 deliverable [47] regarding

the FI-WARE GEs and their functionalities.

This deliverable consolidates the above elements and structures a set of reference

architectures for each of the 11 FITMAN trials relating them to the three layers envisaged by

the FITMAN Reference Architecture and to the FI-WARE GEs, identifies commonalities

among them, and tries to sketch the FITMAN Reference Platforms for the three different

manufacturing areas (Smart, Digital and Virtual factory).

The following sections, in line with the FITMAN DoW, report and analyze the needs and

architectural choices according to the three manufacturing systems’ levels identified by the

EFFRA (European Factory of the Future Research Association) documents [1] where ICT is

having, and will have, a key role:

Smart Factories: characterized by “agile manufacturing and customisation involving

process automation control, planning, simulation and optimization technologies,

robotics, and tools for sustainable manufacturing”;

Virtual Factories: where is relevant the “value creation from global networked

operations involving global supply chain management, product-service linkage and

management of distributed manufacturing assets”;

Digital Factories: in which ICT is focused on supporting “a better understanding

and design of production and manufacturing systems for better product life cycle

management involving simulation, modelling and knowledge management from the

product conception level down to manufacturing, maintenance and

disassembly/recycling”.

These three EFFRA areas in the following are for brevity identified with the term

manufacturing domains.

The deliverable, therefore, provides details on the FITMAN reference architecture for each

of these manufacturing domains starting from the needs and architectural designs of each of

the project’s trials and details and rationalizes the mapping between each of the designed

Reference Architectures and the identified set of FI-WARE GEs and FITMAN SEs.




1.2. Deliverable Organization

The deliverable is structured in three sections (in addition to this section 1):

Section 2 is devoted to characterize the manufacturing domain and the key challenges

(see section 2.1.1) and elements (see section 2.1.2) that will characterize it in the near

future. After an overall analysis specific ones are provided for each of the three

EFFRA defined manufacturing domains (see sections 2.1.3 - 2.1.5). Section 2

completes the scenario providing a quick overview of the FITMAN 11 trials (see

sections 2.2 - 2.4) o that the reader has the elements to evaluate the identified

Reference Architectures and their potential both to assess the proposed Future

Internet technologies, as well as the potential as sources for solutions in the

manufacturing domains and addressed manufacturing needs;

Section 3 is the main section devoted to provide the rationale and structuring of the

three Reference Architectures, as well as of their relationships with the selected GEs

and SEs. More specifically, section 3.1 is devoted to provide the rationale behind the

Reference Architecture approach, while sections 3.2 - 3.4 presents and analyze the

Reference Architectures for the Smart, Digital and Virtual Factories, respectively.

Finally, section 3.5 describes how the FI-WARE GEs and FITMAN SEs contribute to

the implementation of each of the identified Reference Architectures, Section 3.5 is

structured into a set of subchapters to better analyze the mappings of each of the three

Reference Architectures with the FI-WARE GES and FITMAN SEs. A specific

subchapter (see section 3.5.6) is devoted to describe how the FI-WARE Cloud

Chapter GEs are expected to contribute to the implementation of the reference

architectures;

Section 4 reports the conclusions.

Care has been taken to provide references to documents and literature (see section 5) that

support the analysis, approaches and choices reported in this document so to provide to the

reader the possibility to directly check design approaches and to further deepen the topics

addressed in this document.




2. THE MANUFACTURING DOMAINS AND THE FITMAN TRIALS

2.1. The FITMAN Manufacturing Domains

2.1.1. Manufacturing Challenges

Manufacturing is a significant element for the European economy accounting for around

10% of all enterprises in the EU-27’s non-financial business economy in 2010, with figures

like: 2 million enterprises, 30 million employed people (around 22% of EU’27 employment),

and an annual value added around 1.600 billion Euro (around 27% of the EU’27 non-

financial business economy value added) (source: Eurostat).

The European manufacturing sector envisages a huge presence of SMEs that account for

around of 45% of the whole EU manufacturing added value, and around 59% of the

manufacturing employment (source: Eurostat).

These figures make evident how critical is for Europe this sector and the relevance of

promoting its innovation both to maintain and possibly expand its economic relevance, as

well as a way to promote innovation in other sectors (like ICT) that provide products and

services to the manufacturing sector. Indeed, manufacturing is an R&D&I intensive activity

with R&D investments that represent 66% of the European R&D private expenditure.

Many analysts state that the manufacturing sector is moving to a new phase. Indeed, the

General Electric document on Industrial Internet [1] states the twenty-first century will see

a 3rd

wave after the Industrial (1st wave) and Internet (2

nd wave) revolutions. This wave, the

authors call Industrial Internet, is characterized by “… the melding of the global industrial

system that was made possible as a result of the Industrial Revolution, with the open

computing and communication systems developed as part of the Internet Revolution, opens

up new frontiers to accelerate productivity, reduce inefficiency and waste, and enhance the

human work experience”.

A similar position is reported by the German Industrie 4.0 programme [3] that states “the

first three industrial revolutions came about as a result of mechanisation, electricity and IT.

Now, the introduction of the Internet of Things and Services into the manufacturing

environment is ushering in a fourth industrial revolution”.

Several elements are behind the evolution that is making machines and the Cyber Physical

Systems (CPS) in which they are integrated more intelligent. The most relevant elements are:

costs: instrumentation costs have declined dramatically, therefore making possible to

more economically equip industrial machines;

computing power: improvements in microprocessor chips make it possible to add

digital intelligence to machines;

data analytics: advances in software tools and analytic techniques provide the means

to actually manage and understand the massive quantities of data that intelligent

devices generate.

Additionally, this evolution is also promoting new approaches to production [3] “smart

products are uniquely identifiable, may be located at all times and know their own history,

current status and alternative routes to achieving their target state. The embedded

manufacturing systems are vertically networked with business processes within factories and

enterprises and horizontally connected to dispersed value networks that can be managed in

real time – from the moment an order is placed right through to outbound logistics”.

Moreover, as stated in FInES) position paper [4], “… Future Internet (FI) technologies and

infrastructures (including cloud and mobile computing, Internet of Things, Big Data

analytics, IPv6 and next generation networks and computational and storage architectures),




when embedded and integrated in vital and critical business processes in a transparent and

seamless way, are envisioned to constitute the most prominent drivers for enterprise business

innovation”.

All these analysis assign a central role to ICT technologies; therefore, the Europe 2020

Strategy underlines the role of technology as the key solution-provider for tackling the

challenges Europe has to face in the coming years and help in promoting innovative ideas,

new products and services to assure EU growth, high-skilled jobs, and address European and

global societal challenges.

To address the challenges envisaged by the Europe 2020 Strategy, the European

manufacturing sectors have to undergo the following structural transformations [5]:

Manufacturing the products of the future, addressing the ever changing needs of

society and offering the potential of opening new markets.

Economic sustainability of manufacturing, combining high-performance and quality

with cost-effective productivity, realising reconfigurable, adaptive and evolving

factories capable of small-scale production in an economically viable way, herewith

facing better and promptly the uncertain evolution of the market or the effect of

disruptive events.

Social sustainability of manufacturing, integrating human skills with technology.

Environmental sustainability of manufacturing, reducing resource consumption and

waste generation.

From an ICT point of view, the EFFRA roadmap envisages the following major challenges:

Collaboration: as a way to support:

o collaborative manufacturing where ICT has to support a constant feedback

loop among product designers, engineers, state-of-the-art production facilities

and customers;

o collaborative supply networks where ICT has to support OEMs to offer

value-added services (e.g., maintenance, upgrade) or even sell their “products

as a service”

o customer collaboration where ICT solutions must enable extraction of

customer and after-sales information from disparate sources (e.g., social

networks) and feed the manufacturing process to develop personalized or

highly customized products;

Connectivity to support seamless, bi-directional interactions with real-world objects

and environments (e.g., IoT) on a global scale, across different application domains

and stakeholders. The manufacturing Enterprise Information Systems (EIS) must be

opened adopting widely used standards and be able to inter-operate across multiple

organisations, as well as manufacturing systems and devices must become more

intelligent and have advanced self-configuration, self-monitoring, and self-healing

features to support the dynamics and the large and growing number of devices and

data of future manufacturing processes;

Mobility to provide workers, supervisors and managers with critical data at their

fingertips and foster the development of next-generation of mobility assisted

manufacturing applications (in fields like: manufacturing & logistics traceability,

real-time data analysis, and forecasting);

Intelligence to support the processing the huge amount of data originating as a result

of the above points. Significant progress beyond the state-of-the art will be required

in areas like: complex event processing, real-time data analysis and forecasting.




Similarly, the roadmap [6] designed by the ActionPlanT project within the EU Factory of the

Future PPP Action identifies the following elements are central for the European

manufacturing future:

three key ICT elements:

I. the need to “bridge the gap between process- and commercially-oriented

manufacturing operations by leveraging advances in the ICT world – notably

in collaboration, connectivity, mobility and intelligence”;

II. the need to use “ICT to integrate the human element – workers and customers

– to a greater degree in their day-to-day operations and businesses”;

III. the systematic use of ICT innovation as a fundamental success factor for

future European manufacturing operations in Europe, where “enterprises have

to be agile and swift when it comes to being innovative and applying

innovation in practice”;

five essential ambitions for ICT-enabled manufacturing:

I. On-demand: “… manufacturing 2.0 should accommodate changing demands

from a new customer base and deliver customised products on-demand. … it

is important that European manufacturers are able to deliver products to

customers quickly by collaborating with suppliers and subcontractors using

agile supply chains which are interoperable, collaborative and manageable”;

II. Optimal: “… the next generation of product lifecycle management solutions

should not only focus on designing the best products but consider the service

life of products with special emphasis on value added and after-sales

services”;

III. Innovative: “… introduction of collective innovation is one of the three key

growth factors together with human capital and infrastructures”;

IV. Green: “… Manufacturing 2.0 needs focused initiatives to reduce energy

footprints on shop floors and increase awareness of end-of-life (EoL) product

use”;

V. Human-centric: “… manufacturing 2.0 will evolve from being perceived as

production centred to human centred”.

As evident from the above analysis and future manufacturing scenario, the European

industries has to: on the one hand put workers at the forefront (improving knowledge

delivery mechanisms, supporting continuous skills enhancement, providing assistive tools

for aged workers, developing and deploying intuitive e-learning tools for all), and, on the

other hand,

Put customers in-the-loop (quality and sustainable products for customers, design thinking

and customizations, customer collaboration). To this end the ActionPlanT document

introduces the concept of Manufacturing Business Web (MBW) as a “melting pot where

disparate solutions for process- and commercially-oriented technologies converge”. This

concept will encourage technology developers and manufacturing service providers to build

new solutions with minimal time-to-market, while customers will significantly reduce their

investments to develop new services, and will have the possibility to compose and configure

manufacturing services in-situ.

The FITMAN project, which is not focused on R&D activities devoted to develop new

technologies for manufacturing, nevertheless has to move along the lines depicted above and,

roughly speaking, position itself within the Industrial Internet wave or as a forerunner of the

Industrie 4.0 4th

industrial revolution. Indeed FITMAN envisages as key elements:

the horizontal integration through value networks,

support for end-to-end engineering across the entire value chain,




vertically integrated and networked manufacturing systems.

2.1.2. Manufacturing Characteristics

The previous section shed some light on the challenges manufacturers and ICT solutions

providers have to face in the coming years.

As the IoT becomes widely deployed in smart factories, the volume and the level of detail of

the generated data will increase, and, at the same time, the business models will no longer

involve just one or a few companies, but will instead comprise highly dynamic networks of

companies and completely new value chains. Smart machines will autonomously generate

and transmit data that will inevitably cross company boundaries. A number of specific

dangers are associated with this new context, and new instruments will be required these

issues.

Information will also be shared across machines, individuals or companies to facilitate

intelligent collaboration and better decision making [1]: “This enables a broader group of

stakeholders to engage in asset maintenance, management and optimization. It also ensures

that local and remote individuals that have machine specific expertise are brought into the

fold at the right time. Intelligent information can also be fed back to the originating machine.

This not only includes data that was produced by the originating machine, but also external

data that can enhance the operation or maintenance of machines, fleets and larger systems”.

The scenarios and challenges highlighted in the previous sections must be taken into account

in the design of the FITMAN architecture and FI-WARE GEs evaluation. The following list

tries to structure the most relevant elements:

Safety: technological systems (machines, production facilities, products, etc.) should

not create danger to people, the environment or even to other machines. Safety [3]

“requires both operational safety and a high degree of reliability. … Operational

safety refers to the aspects of safety that are dependent on the correct operation of

the system or that are provided by the system itself. The elements required to deliver

operational safety include low fault rates, high fault tolerance (i.e. the ability to keep

operating correctly even when faults occur) and robustness (the ability to guarantee

basic functionality in the event of a fault). Reliability refers to the probability of a

(technological) system operating correctly for a given period of time in a given

environment”. This requirement therefore impact the ability, reliability, and reactivity

of the production systems as a whole, as well as on the smart objects and services

active in it;

Security: data and services need to be protected against misuse (e.g. unauthorised

access, modification or destruction). The manufacturing system has to provide

advanced security features to increase confidentiality (restrict access to data and

services to specific machines/human users), integrity (accuracy/completeness of data

and correct operation of services) and availability (system’s ability to perform a

function in a particular time);

Information protection: all manufacturing systems manage highly confidential

information being it information related to shop floor activities, product technical or

production processes details, etc. In a global market, intellectual property protection

is therefore key to the manufacturers’ survival due to its impacts not only on sales,

but also on corporate image and loss of know-how. This issue will increase its

importance in view of the much higher degree of cooperation between the different

partners in the value network. It is therefore necessary to assure the deployment of

highly flexible, scalable and efficient information protection features (e.g., access




control and data integrity), as well as highly secure and controllable ICT

environments to guarantee trust and transparency to protect critical business

knowhow;

Liability: the Industrial Internet, Industrie 4.0 4th

wave, Manufacturing 2.0 envisage

a substantial increase on the exchange of sensitive data between different companies,

with a corresponding increase of the risks that these data may be used and/or

disclosed illicitly, or that they may be hacked by third parties. In the immediate future

therefore, the issue of liability and responsibility becomes even more important and

specific measures have to be set up to manage correct attribution of liability and

precise documentary evidence in all manufacturing steps and system statuses;

Handling of personal data: the interaction between employees and the production

system will increase both to improve safety of people on the working place, as well

as to support the new production processes. The volume and level of detail regarding

the workers and employees will therefore increase too. This issue poses a threat [3]

“… to employees’ right to informational self-determination. … Current regulation

fails to adequately address these problems. Outsourced data processing models are

already encountering difficulties (e.g. in the realm of cloud computing), since local

data protection standards are generally not applicable in countries outside of

Europe, meaning that in practice it is impossible for client companies to comply with

their data protection responsibilities”;

Time constraints: production lines and cells, in particular, need communication

infrastructures with low delay and jitter (often able to assure real-time

communication), as well as event processing services able to quickly react to events;

Integrability: a manufacturing environment envisages many hardware (sensors,

actuators, conveyors, robots, etc.) and software components (PLCs, SCADA systems,

MES, ERP, etc.) normally provided by a wide set of suppliers or managed by

different subjects (e.g., suppliers, maintainers, retailers, etc.). The integrability issue

both impacts on the information representation, as well as on features or protocols to

be provided or supported (e.g., authentication based on LDAP, Kerberos, Microsoft

Active Directory);

Predictability: production is characterized by well-defined processes and procedures

that, often, have to be executed according to a given pace (e.g., takt time) and always

according to what envisaged in the design phase. This implies that elements like

continuity, scalability, responsiveness, etc. must be assured at all levels of the ICT

production stack, and that these elements are key elements of the contracts with

external subjects. On the ICT side this implies that ICT related contracts must

provide assurance for QoS, SLA, etc.

The following sections further characterize each on the manufacturing domains envisaged by

the FITMAN project.

2.1.3. Smart Factories

Smart Manufacturing (or Smart Factory), as stated, deals with the optimization of the

production processes (in terms of production costs reduction, efficient energy usage,

improvement in production reliability, production machines usage, etc.) via the monitoring

and management of the production process and of its components. Smart factories therefore

[3] “… are capable of managing complexity, are less prone to disruption and are able to

manufacture goods more efficiently.

In the smart factory, human beings, machines and resources communicate with each other as

naturally as in a social network”.




These contexts need technologies that supports [6]: seamless integration of disparate systems

and robots, real-time enforcement of engineering changes, quality, regulatory, requirements

in the frontline, advanced algorithms on large data sets (e.g., for analyses and forecasting on

productivity, throughput, downtime), event driven architecture, as well as standardized

interfaces for data exchange. Additionally smart factories need to provide, in an effective and

efficient way, to workers and plant managers a disparate set of information to support them

in their daily operations and decisions. Furthermore, managers would be able to [6] “… drill

down into any production area and observe throughput, use and consumption through

intuitive key performance indicators (KPIs) even when on the move”.

Smart factory therefore envisages the deployment and management of technologies at the

level of the shop floor, the supply chain optimization, robotics, automation, production

planning and optimisation. Additionally, it envisages smart devices, adaptive and fault

tolerant process automation, control and optimisation technologies and tools, plug&produce

connection of automation equipment, new metrology tools and methods for large-scale and

real-time handling and processing of manufacturing information.

2.1.4. Digital Factories

Digital factories address [1] “… the front-end stages of manufacturing, in particular early

concept modelling, simulation and evaluation, as well as the transformation of the

knowledge-time curve, thus ensuring greater acquisition of knowledge earlier so that better

informed manufacturing decisions can be taken. The handling of uncertainty is also a crucial

area”.

As such digital factory’s platforms must [3] “… connect people, objects and systems to each

other … and will possess the following features:

Flexibility provided by rapid and simple orchestration of services and applications,

including CPS-based software

Simple allocation and deployment of business processes along the lines of the App

Stores model

Comprehensive, secure and reliable backup of the entire business process

Safety, security and reliability for everything from sensors to user interfaces

Support for mobile end devices

Support for collaborative manufacturing, service, analysis and forecasting processes

in business networks.”

Additional aspects that digital factory’s ICT platforms have to provide are related to:

intelligent system maintenance and maintenance optimization across machines,

components and individual parts providing a line of sight on the status of these

devices and assuring required parts to be delivered at the right time to the correct

location. This includes also learning capabilities and predictive analytics to allow

preventive and predictive maintenance programs that [2] “… have the potential to lift

machine reliability rates to unprecedented levels”;

the collected data could be aggregated and analysed to obtain [2] “… a continuously

expanding, self-learning system that grows smarter over time”.

provide intelligent decision making gathering data from intelligent devices and

systems to [2] “… facilitate data-driven learning, which in turn enables a subset of

machine and network-level operational functions to be transferred from operators to

secure digital systems. This element of the Industrial Internet is essential to grapple

with the increasing complexity of interconnected machines, facilities, fleets and

networks”.




The Digital Factory domain, therefore, envisages the integration of digital methodologies

and tools in the field of production engineering spanning from modelling, simulation,

3D/Virtual Reality visualization, continuous data management, predictive and condition

based maintenance, KPI evaluation and on-demand provision of information (even on mobile

devices) for decision makers.

The aim of the Digital factory is to set up an efficient and comprehensive environment able

to support and optimize the design, modelling, simulation, and evaluation of products,

processes and systems before a new factory is built or any modification are made on existing

systems, as well as to in order to improve quality and reduce time. Additionally, this

manufacturing area covers also the life-cycle management of products, from the design

phase all the way through to production, maintenance, disassembly and recycling.

The Digital Factory domain, in short, has to face two main challenges to improve (in terms

of speed, quality, accuracy and effectiveness) the decision making process:

provide a consolidated and integrated access to product life-cycle information

managed by the various systems, tools and sources available in the factory;

provide a contextualised, personalised presentation of such information based on

advanced data analytics and (3D) visualisation capabilities also while on the move.

To this end, the FITMAN Digital Factory trials focus on particular needs of better

knowledge management and exploitation of product lifecycle information identified in a

variety of departments that range from production planning departments, maintenance,

design or teams working in the production field. All share the same interest to access to

technologies and platforms that allow them experience a more natural flow of information

and a better presentation of information for adapting their decisions and systems towards

more productive scenarios.

2.1.5. Virtual Factories

The Virtual Factory domain can be simply characterized in term of setting up and managing

collaborative supply networks based on [6]:

Great collaboration between OEMs and subcontracts through standardized

interfaces

Total visibility of production, inventory, and materials

Quick response in supply chain planning

New paradigms such as “production as a service” and “after-sales services”

The final, future objective is to [3] “… incorporate individual customer- and product specific

features into the design, configuration, ordering, planning, production, operation and

recycling phases. It will even be possible to incorporate last-minute requests for changes

immediately before or even during manufacturing and potentially also during operation”.

ICT technologies in this area have to support the development and management of inter-

company value chains and networks through horizontal integration, digital end-to-end

engineering across the entire value chain of both the product and the associated

manufacturing system, and the vertical integration of flexible and reconfigurable

manufacturing systems within businesses.




Key ICT elements in this domain are [1]: “(a) improved efficiency of (embedded) product

intelligence enabling advanced product-centric services (e.g. product authentication, IPR

security, ICT-facilitated diagnosis and repair/resetting, remote performance/energy

monitoring and logistics); (b) new business models and capabilities for improved

management of global networked operations”.

The Virtual Factory ICT domain therefore deals with the realization and management of

complex, end-to-end, collaborative production environments therefore including complex

and extended supply chain, collaborative design and production, M2M communication, etc.

In this domain ICT solutions are integrated end-to-end and across company boundaries, with

the aim to support the exchange and integration of data and physical assets, to provide clear

insight and exact, useful knowledge, while facilitating and supporting decision making and

creating value from global networked operations

2.2. The FITMAN Smart Factory Trials

In line with the characterization of the Smart Factory manufacturing domain, the related

FITMAN trials are essentially focused in reducing production costs, increasing production

capacity and increasing the usefulness of information, even if they could have some

characteristics pertaining to the other manufacturing domains as is usual in real contexts.

The objective of the these trials is to assess FI-WARE technologies in real or realistic Smart

Factory contexts in their ability to improve and speed up the production processes; therefore

supporting exploitation of FI-WARE and Future Internet technologies by a large and diverse

set of service ecosystems for improved production in the manufacturing context.

FITMAN Smart Factory trials will significantly improve the management of sensors and

services, facilitating a simpler deployment of smart objects in the factory shop-floor and

more intelligent production practices. The final objective is to demonstrate how different

business domains could plug highly diverse sensors, machines and process information over

a unified information interoperability framework.

In the following we provide a very short description of the FITMAN Smart Factory trials

with the main objective to provide to the reader a short overview of these trials. More

extensive information are provided in D1.1 [55].

2.2.1. Automotive Supplier (Spain)

FITMAN Partners

Manufacturer Partner(s): TRW Automotive

Manufacturer Nature: Large Enterprise

Technological Partner(s): Innovalia Association

The TRW, a Spanish large enterprise, automotive trial is focused on improving the health

and safety of workers in production workplaces combining FI-WARE and FITMAN specific

technologies to support risk prevention and management developing systems able to

continuously monitor workers on their workplaces and process in real-time the acquired

information in order to predict and prevent accidents and incidents, achieving the zero

accident factory.

The trial expects to see significant impacts on the following aspects:




improvement of workers safety and security: death and health injuries on the

workplace is still an issue in Europe. Most of them are generated by the workers

themselves. The trial expects to achieve a zero accident factory;

enhancement of the competiveness: the human-centred production model fostered

within this trial will allow getting a significant increase in the companies’

productivity by the reduction of accidents and incidents;

reduction of costs and increase of benefits in production: thanks to the decrease in the

number of accidents and incidents, it is expected a significant drop of costs related to

times off sick, losses in materials due to the stop in the line, the funding and rewards

due to secure and safety factory, substitute workers training, as well as increase in the

production and the enterprise reputation and image;

increase of effectiveness in the industrial processes: the assessment and control of the

factors that are causing accidents will improve the effectiveness of the production

processes, as well as these the training and skills of workers;

improvement in company image: the improvement of the working conditions will

have a positive effects on the workers, the factory and the enterprise perception.

2.2.2. White Goods OEM (Italy)

FITMAN Partners

Manufacturer Partner(s): Whirlpool Europe Srl


Technological Partner(s): Engineering Ingegneria Informatica SpA

The Whirlpool trial aims at assessing the improvement Future Internet technologies can

provide to production processes via more effective and fast decisions by workers. In

particular this trial expects to have significant improvements on these three aspects:

improvement of decision process at the shop floor level, in terms of faster reaction

time (normally within the production takt1 time; i.e. 40-60 sec), supporting data and

event based;

improvement of decision process of supervisors, characterized by medium reaction

time (usually within day/shift) and pattern driven;

improvement of decision process of managers, characterized by long time reaction (

usually weeks/month) and systemic driven.

The benefits Whirlpool expects from the deployment of Future Internet technologies are the

following:

improvement of the product quality: the deployed ICT technologies are focused on

improving the fault detection ability and the reaction time, elements that directly

impact on the quality deterioration state and on the reduction of costs of non-quality

(rework, scraps, warranty cost, services etc.);

productivity: the reduction in the detection, decision and reaction time will improve

the overall efficiency of the production system;

waste reduction: a better and more informed decision process will hopefully reduce

one or more of the Lean Principles’ seven manufacturing wastes2;

1 Takt Time: The available production time divided by customer demand (see

http://www.lean.org/Common/LexiconTerm.aspx?termid=337) 2 http://www.emsstrategies.com/dm090203article2.html




cost reduction: more effective decision process could lead to a direct decrease of

TCQ (Total Cost of Quality);

people engagement: a more effective integration of workers in the decision processes

is expected to improve people engagement with all the related benefits.

2.2.3. Textile/Clothing (Italy)

FITMAN Partners

Manufacturer Partner(s): Fratelli Piacenza SpA

Manufacturer Nature: SME

Technological Partner(s): Softeco

Even if a traditional sector, the Textile and Clothing (T&C) sector is one of the most

fragmented and challenging sectors in EU. Indeed, there are more than 70 production steps

(some very specialized), extremely wide variety of raw materials, very short product life

cycle (6 months), pro cyclic and seasonable fashion demand. Due to this peculiarities it is not

convenient (and hardly impossible) to organize a fully integrated company from raw

materials to finished product and the business ecosystem is based on a sub-supplying

organization, where some large subject (fabric, clothing producers and retailers) collect

orders, take care of designs and sales and carry on some central steps of production,

delegating all the other ones to, normally, smaller and more specialized suppliers. The T&C

sector is therefore centred on industrial clusters, where different subjects interact and share a

same common industrial culture.

The production cycles in this sector is normally characterised by periods of calm and periods

of peak demand not equally distributed or synchronised among the companies in the

ecosystem. Therefore, there may happen that at a given point in time a company is

overloaded by orders while another one is underexploited. The business ecosystem is starting

to create agreements between competitors to share production facilities to manage these

peaks of demand, improve the machineries utilization and reduce investment costs. This

sharing approach can be identified as “cloud manufacturing” if supported by specific ICT

technologies, including IoT ones, to trace the products and the machinery availability.

The objective of the Piacenza trial is to demonstrate and assess that Future Internet

technologies can actually foster the T&C “cloud manufacturing”. The trial will deploy

sensors (e.g., RFID tags and readers) in the shop floor to collect and monitor production and

process the collected data to improve the exploitation of the production machineries, labour

force and infrastructures.

2.2.4. Aeronautics OEM (Italy)

FITMAN Partners

Manufacturer Partner(s): AgustaWestland


Technological Partner(s): TXT e-solutions SpA

This actually is a trial contributing to two different manufacturing domains: the Smart

Factory and Digital Factory ones. In this section the focus is on the AgustaWestland Smart

Factory issues and objectives, while section 2.3.2 reports the Digital Factory trial’s elements.




The focus of the Smart Factory Agusta trial is on using Future Internet technologies to

improve the compliance of tools management to the FOD3 (Foreign Object Damage)

normative in the Helicopter Final Assembly Line and Service Stations.

Indeed, workers in these production areas receive specific training, are subject to specific

assembly/maintenance procedures, and use peculiar toolboxes to properly use tools so to

avoid forgetting some of them in helicopters and create risks for damages to vehicles or

injuries to people as mandated by the FOD international normative. The normative envisages

also dedicated inspections by certified people to assure the assembly or maintenance

procedures were correctly executed and the vehicle can be delivered.

The objective of the trial is to further support the technicians via the deployment on the shop

floor of new instruments (e.g., Snap-on smart toolbox4 by Tools Italia S.r.l.) as data source

and analyse in (near-) real time the collected data to support continuous improvement of

workers skills, assure the best level of flight safety assurance, as well as improve the training

material and activities.

2.3. The FITMAN Digital Factory Trials

In line with the elements highlighted in section 2.1, the FITMAN Digital Factory trials focus

on the management of enterprise knowledge and therefore aim at providing, or enhancing,

tools and services able to support engineers, managers, designers, etc. in managing the whole

life cycle of a product or of a production facility and to reduce time to market, speed up

project ramp-up and reduce costs. The information managed in Digital Factory contexts, and

in the related FITMAN trials, is very sensitive being, in essence, the company know how

and/or its ability to do. In these contexts, therefore, the requirement to have specific

mechanisms to protect access to this knowledge or avoid its misuse is higher than in other

manufacturing contexts.

The FITMAN Digital Factory trials are, therefore, focused on improving, thanks to the

deployment of suitable FI-WARE GEs and FITMAN SEs, the access, sharing and processing

of the enterprise knowledge across the enterprise teams and subjects, and, at the same time,

reduce the need to develop new applications or procedures and the “knowledge provision”

time (i.e., the time required to make accessible to a subject available knowledge – for

example stored in the design engineering data silo but needed by production engineers -, or

generating new knowledge from available/acquired data).

In the following we provide a very short description of the FITMAN Digital Factory trials

with the main objective to provide to the reader a short overview of these trials. More

extensive information are provided in D1.1 [55].

2.3.1. Automotive OEM (Germany)

FITMAN Partners

Manufacturer Partner(s): Volkswagen


Technological Partner(s): Fraunhofer IPK

3 http://en.wikipedia.org/wiki/Foreign_object_damage 4 http://digimag.rrd.com/CAT1000a/




The Volkswagen trial is focused on significantly improving estimates of in-house production

costs at early phases of the product development process. Indeed, in new car projects,

Volkswagen does a thorough analysis to ascertain if it is more convenient to adapt an

existing production line, or establish a new one. These estimates are based on information

grabbed from the Volkswagen digital factory system, tools and planning environment.

In particular, in Volkswagen the required information is stored in the Machine Repository

(MR) that references detailed production equipment information stored in a variety of

Planning Systems' databases (e.g. TeamCenter).

The trial focuses on the early phase “Product concept – product design”. The information

from the Machine Repository is used by the design engineering team to consider

technological standards in order to evaluate and minimize the investment costs.

2.3.2. Aeronautics OEM (Italy)

FITMAN Partners

Manufacturer Partner(s): AgustaWestland


Technological Partner(s): TXT e-solutions SpA

As anticipated in section 2.2.4, the Agusta trial actually contributes to the Smart Factory and

Digital Factory domains. In this section the focus is on the AgustaWestland Digital Factory

trial’s elements.

The trial deals with the helicopters’ manufacturing process and, in particular, with the last

stage (i.e., the final assembly) of this process that takes place in the Final Assembly Line

(FAL).

In the FAL area specialised workers assemble helicopter components produced in other

AgustaWestland plants or provided by external suppliers, and assembly the helicopter

following a process divided in several steps. Each of these step envisages the compilation of

specific documents that collect information specific to the assembly step, and to the

assembled components and performed activities. Each of these document is signed by the

persons involved in that step.

All these documents are finally collected, checked and delivered by the dedicated quality

departments. These documents contribute to the set-up of the “Helicopter logbook”,

containing all the information referring to the manufactured helicopter with details on all

components and subcomponents (with their serial identifiers) mounted on that helicopter.

The selection, and the processing, of the documents produced during the assembly process to

produce the “logbook” is a complex activity because these documents are spread across

different data silos (managed by different applications, using different data structures and

access methods). The objective of the trial is to improve this “logbook” production phase

thanks to technologies that easy the access and processing of information managed by

different sub-systems.




2.3.3. Construction Industry (Portugal)

FITMAN Partners

Manufacturer Partner(s): CONSULGAL


Technological Partner(s): UNINOVA Institute

The CONSULGAL construction trial is focused on the concrete certification process. In any

construction process the concrete handling and testing is subject to specific procedures

finalized to ensure that the design characteristics (e.g., structural resistance, durability,

structural safety, resistance to environmental conditions) set for this component, and for the

item in which it will be used, are satisfied by each load arriving at the work site.

The concrete testing procedure envisages the collection of several samples of concrete for

each truck load arriving at the work site and a set of tests (e.g., visual inspection at arrival

time to check the concrete consistency, compression resistance after 7 and 28 days).

In complex construction works (e.g., dam construction) the number of tests, and of the

related data, is significant (e.g., in the order of thousands).

The FITMAN construction trial is within the construction of the Baixo Sabor Dam

(Northeast Portugal). This dam is divided into different sections and concrete is applied to

each section separately, as planned by the Works Contractor.

It is evident that in such kind of construction, concrete noncompliance with the design

parameters may have tremendous consequence, including, for example, the risk to have to

demolish a noncompliant section and rebuild it, or to compromise the dam’s structural

resistance.

It is therefore critical to be able to properly store, quickly relate the concrete test results to

specific areas of the dam, as well as to quickly evaluate the impacts of one or more abnormal

results in the overall dam wall resistance.

Currently, all these data are recorded and circulated in paper and/or in electronic files,

analyzed using Microsoft Excel, and made available to the client on a monthly basis, while

typically on a weekly base to the supervisor.

The deployment of Future Internet technologies in this trial and context is expected to

provide the following benefits:

near real time availability to, and accessibility by, the stakeholders of the information

regarding to the project status and the concrete tests;

more efficient and effective decision processes;

automatic test results analyses comparing the test outcomes with the concrete

characteristics envisaged in the dam design;

possibility to visualize the concrete zones and related information (e.g., operation

schedule, concrete classes distribution, concrete stress values, full samples history);

paper load reduction;

documentation of access to the test conditions;

possibility to tag, for identification, the concrete samples to allow their tracking.




2.3.4. Furniture (Spain)

FITMAN Partners

Manufacturer Partner(s): AIDIMA


Technological Partner(s): Universitat Politècnica de València

The FITMAN furniture trial is focused on improving the responsiveness of furniture

manufacturers to customer needs and market trends through the collection and analysis of

information form data sources related to the furniture sector or, for end users (i.e., customers)

related information from the open Internet (i.e., social networks, blogs, etc.).

More specifically, the trial explores the following three areas:

furniture trends forecasting: the objective is to early detect and identify trends for

new products;

customers’ requirements management: the expectation is to identify customers latent,

collect opinions and suggestions to improve products, as well as perform sentiment

analysis;

collaborative work for product innovation: on this area there are two specific

objectives. The 1st one is to try to involve the customers in the design of new

products setting up un iterative process that collects needs and expectations, design

and proposes new products accordingly, and solicits and collects feedbacks to refine

the design. The 2nd

objective is to use Future Internet technologies to speed up the

involvement of different teams and professional skills in the product’s and production

design processes in order to both reduce the time-to-market, detect and avoid

complex or expensive manufacturing processes, and speed up the supply chain

activation.

The final objective of the trial is therefore to have products that better meet, and anticipate,

customers’ needs, provided on the market more quickly and with more efficient and effective

production processes.

2.4. The FITMAN Virtual Factory Trials

As for the previous manufacturing domains, the FITMAN Virtual Factory trials take into

account the requirements and issues elements highlighted in section 2.1 and, therefore, focus

on development and management of inter-company value chains and networks through

horizontal integration, digital end-to-end engineering across the entire value chain, and

integration of flexible and reconfigurable manufacturing systems within businesses.

The main objective of the Virtual Factory trials is to assess if and how Future Internet

technologies can affect the today’s value chains, which are relatively static and do not

provide a global manufacturing overview, to become more responsive and effective to satisfy

customer needs, as well as to meet manufacturers and citizen’s needs (e.g., more efficient

production processes, added value products, pollution reduction, waste reduction).

The provision of ICT tools and services able to improve the tangible and intangible assets of

enterprises and their value chains, and to support engineers, blue and white collars,

managers, designers, etc. in managing the whole life cycle of a product or of a production

facility is the operative approach the FITMAN project is pushing, and its Virtual Factory

trials will deploy and assess, to achieve the above objective.




As for the previous manufacturing domains, in the following we provide a very short

description of the FITMAN Virtual Factory trials that provides to the reader a short overview

of the validation contexts and addressed needs.

More extensive information are provided in D1.1 [55].

2.4.1. Plastic Industry (France)

FITMAN Partners

Manufacturer Partner(s): Applications Plastiques du Rhône (APR)


Technological Partner(s): University Lumiere of Lyon 2

The plastic industry FITMAN trial aims at assessing Future Internet technologies in a market

sector where value chains play a relevant role and the European manufacturers are under

pressure to maintain their market and leading positions.

This trial is leaded by APR, a competitive actor in plastic industry in France, that aims at

enhancing the relation with its customers, suppliers and producers.

The trial expected benefits and impacts can be summarized as follows:

improve the effectiveness of business collaboration assuring better reactivity to

requests for quotation and delivery, improving the procurement process of raw

materials, and increasing the confidence of APR customers concerning products

quality and its timely delivery;

improve the production processes in terms raw material procurement, quality of

information processing, reduction of processing times;

reduction of information integration costs to face the huge amount (more than 2500

business projects per year) and diversity of customers’ projects;

improve the quality and effectiveness of the investment strategies;

enhance customer satisfaction and develop or strengthen partnerships with the top 50

customers.

From an operative point of view this FITMAN trial aims at “maximizing information quality

as support for successful business collaboration” and at upgrading existing business

collaboration processes by proposing more integrated, service-enabled and fully automated

processes.

2.4.2. Manufacturing Resource Management (UK)

FITMAN Partners

Manufacturer Partner(s): Sematronix Limited


Technological Partner(s): Coventry University

The overall aim of this trial is to enhance the services offered by Sematronix to its clients

through the deployment of Future Internet technologies.

The Sematronix client base consists of clusters of SME (called members), who subscribe to

the clusters in order to access services which may provide support to individual members, or

to cooperating members that establish virtual organizations (VOs) to better compete with




larger enterprises. The services offered span from identification of new business

opportunities, tenders selection and SMEs consortium matching, manufacturing sensor

monitoring applications.

The current services present some weaknesses like:

a consistent volume of custom code that implies higher costs for service

development/customization and code maintenance, as well as to align services to new

technologies and platforms updates;

the selection and management of tender sources is mainly performed by human

operators, which limits the possibility to set up new client clusters (or makes this set

up costly);

member’s capability knowledge base management is not fully automated and

negatively impacts tender opportunities;

the selection and prioritisation of tender opportunities still requires human

intervention to achieve an acceptable precision level;

identification of consortia that better match tender opportunities suffers a similar

problem;

sensors integration is heavily based on customised software which means the need to

do ad hoc developments to support new protocols or technologies;

complex event processing is performed using a prototype CEP system that does not

provide advanced features for the definition and management of the required event

patterns.

Therefore the trial objective is to embed Future Internet technologies within the Sematronix

services to solve some of the above issues (e.g., reduce custom software development,

simplify the integration of new protocols and sensors, reduce the deployment costs for new

clusters, be able to perform more complex events and data analysis, increase the flexibility

and adaptability of the service platform) and increase the effectiveness of the support

Sematronix provides to members and their VOs.

2.4.3. LED Lighting (Germany)

FITMAN Partners

Manufacturer Partner(s): COMplus Automation GmbH


Technological Partner(s): Fraunhofer IPK Berlin, COMplus Automation

GmbH

Also this trial is based on a cluster of SMEs that produce LED based lighting systems for

private, industrial or public use. These enterprises already collaborate to share their

capabilities, jointly design new products (or satisfy customers’ orders), and perform the

production of required components using an IT service platform essentially developed within

the Supply Network Mapping and FACIT-SME (Facilitate IT-providing SMEs by Operation-

related Models and Methods) research projects.

The current solution presents the following issues:

difficulties in predicting the time span of collaboration on specific activities;

difficulties in efficiently and quickly adapt business plans to take into account new

requirements or technologies, as well as new possible applications;

difficulties in identify and access the right, most appropriate and most up to date

information;




difficulties in actually collect and share the exchanged information because most of

the communications are between individuals and via phone;

the knowledge is not effectively shared due to the availability in time of partners or

experts;

differences in IT landscapes among the networked enterprises and lack of common

standards;

lack of widespread sufficient skill in using the application services.

The trial’s objectives are to deploy Future Internet technologies and assess them in solving

the above issues and, specifically, improve the knowledge sharing among the networked

enterprises, accelerate and enhance the identification of the partnerships for specific

products, dramatically speed up the early phases of the engineering project, as well as to

more effectively and efficiently insure the compliance of the systems to requirements and

constrains since the early design phase and across all involved partners (supplier).

2.4.4. Machinery for Wood (France)

FITMAN Partners

Manufacturer Partner(s): GEOLOC System


Technological Partner(s): Université Bordeaux 1

The FITMAN machinery for wood trial deals with improving the collaboration among a set

of subjects (e.g., final customers, product engineers, suppliers) and SEGEM-Macbo, an SME

developing special machinery for the wood industry. This market is more and more based on

multi-actors partnerships to address customers’ needs.

As for other market segments, these actors have the need to reduce costs and engineering

delays (Time-to-market).

The trial will specifically focus the order entry and order tracking processes that involves

many actors (e.g., customers with their orders, suppliers that have to provide specific

components), and is currently managed using a set of non-integrated applications (e.g.,

Microsoft Excel, SAP BusinessObjects), communication technologies (e.g., fax, email, FTP)

and order formats (e.g., PDF or Word documents, Excel spreadsheets). This approach, as

evident, requires a lot of human interventions to process the exchanged information and to

manually insert it into the main applications (e.g., SAP BusinessObjects).

The deployment of Future Internet technologies in this trial is finalised to implement and

experiment an open platform providing collaborative services to support the above actors and

implement workflows to support the order entry and tracking processes integrating processes

cross-enterprises and internal processes and automate the flow of information across the

whole set of phases (commercial, design, manufacturing and installation).




3. THE FITMAN REFERENCE ARCHITECTURE

3.1. Why a Reference Architecture

As the size and complexity of a system increases, its design, in terms of overall system

structures and interactions, becomes the central problem. An architecture, therefore, is meant

to design the high-level structure of a system in order to assure that the identified

architectural components and their interactions are able to satisfy the needs and goals of the

system [30]. The design of an architecture is based on a thorough understanding of the

domain to be modelled and to its specific needs and goals.

Even if focused on the manufacturing domain, the FITMAN project actually addresses issues

relative to a wide set of manufacturing systems, as made evident from its trials. Therefore,

the design of a specific architecture is not reasonable due to the lack of needs and goals of a

specific system. In situation like this, the best approach is to design a Reference Model or

Reference Architecture that provides a more abstract description from which concrete

architectures for the specific realities (e.g., FITMAN trials) can be actually derived.

Reference architectures start to appear in contexts where the multiplicity reached a critical

mass triggering a need to facilitate system design and life-cycle support.

A Reference Architecture captures the main characteristics of the architecture of a set of

systems. Reference Architectures [31] “start to have value when the multi-* factor is large

enough. When creating a single system, we need engineering, design and architecting

competencies. However, when the scope increases and multiple product creations are

coupled, then Reference architectures are indicated. For small stand-alone developments

Reference Architectures are overkill”.

The purpose of a Reference Architecture, therefore, is to provide directions for the

development of actual architectures for these systems, or their new or extended versions.

A Reference Architecture is normally characterized by the following three elements:

a common lexicon and taxonomy that is tied to the application domain (e.g.

automation in the FITMAN case);

a common architectural vision that helps in focusing common elements and in

deriving synergic actual architectures;

modularization that helps in focusing subsequent refinement activities and in assuring

integration and, to some extent, interoperability.

Reference Architectures facilitate the design and management of product family architecting

and evolution, smoothing impacts due to market changes in contexts and needs.

As depicted in Fig. 3-1 (source [31]), a Reference Architecture is an elaboration of a mission,

vision and strategy within a specific application domain (as in FITMAN) or context that

takes into account the requirements expressed by the customers or reference markets, as well

as the technological opportunities.




Fig. 3-1: Role of a Reference Architecture

A Reference Architecture, therefore, provides a description that, on the one hand, is more

abstract and general than a concrete architecture designed for a specific context, but, on the

other hand, it provides a better understanding of the system constraints, evolvability and

reusability, and a specific guide to design concrete architectures that exploit synergies,

evolvability and integrability.

The process of deriving specific architectures and implementations from a Reference

Architecture is depicted in Fig. 3-2. The actual architecture for a specific context (e.g., a

FITMAN trial) is obtained refining the Reference Architecture taking into account the

specific needs and requirements, as well as the specific constraints and opportunities of the

context at hand [32].

Fig. 3-2: Derivation of specific architectures and implementations

TechnologyCustomers

Market

Mission

Vision

Strategy

existing architecturesnew or evolved

architectures

ReferenceArchitecture

Organization A

Organization B

Organization …

elaboration

guid

ance

Context specificconstraints & opportunities

ReferenceArchitecture

Context specificArchitecture

Context specificneeds & requirements

Transformation Transformation

Context specifictechnologies

Context deploymentconstraints

Context specificimplementation




The final step bringing to the specific system implementation takes into account the actual

implementation in the light of the technologies and deployment constraints of the context at

hand.

To complete the scenario, Fig. 3-3 reports a very high level view of the Reference

Architecture envisaged by the Industrie 4.0 Working Group [3] to interconnect the physical

(IoT – Internet of Things) and digital worlds (IoS - Internet of Services). As evident from the

figure, this high level Reference Architecture addresses different business domains (e.g.,

Industry, Energy, Mobility, etc.) and meets the innovations envisaged by the Industrie 4.0

analysis and programme.

Fig. 3-3: Industrie 4.0 Reference Architecture for connecting the IoT with the IoS

A similar approach is envisaged by the Industrial Internet document [2] where the envisaged

innovation categories that a Reference Architecture has to integrate to foster the development

of the Industrial Internet can be characterized as follows:

deployment and integration of sensors (i.e. smartness) into the design of new

industrial equipment (as well as retrofitting existing equipment);

new standards to enable deeper integration of data from similar assets from different

Original Equipment Manufacturers (OEM) or from different asset categories;

new platforms that enable building specific applications using a shared

framework/architecture;

new business practices that fully integrate machine information into decision-making.

The above scenario helps positioning the FITMAN approach and Reference Architecture. To

avoid being too abstract and general the FITMAN project actually designs three Reference

Architectures one for each of the manufacturing domains described in §2.1 (see pag. 9).

Even if a manufacturing context does not fit exclusively and exactly in one of the Smart,

Virtual and Digital Factory domains, nevertheless having different reference architectures

for them helps to better satisfy the requirements and characteristics of these manufacturing

domains, identify architectural components and interaction/integration patterns that more

specifically characterize them. This approach, therefore, provides more effective indications

Compiling & networking of functions, data & processesManagement of end devices and systems

Business Processes

Services Services

Business Processes

Services ServicesApp.

Solutions

Mobility Energy Industry Buildings

Town CompanyLevel

Internet-basedSystem

& service platforms

Internetof Things

Internetof services

Applications

Requirements

Model-based development

platforms

Connectivity(IPV6)

Knowledge of business areas

and applications

Access to marketsAnd customers




and guidelines for the design of specific architectures addressing actual operational contexts

(e.g., FITMAN trials) as discussed above and indicated in Fig. 3-2.

Before moving to the description of the FITMAN Reference Architectures for the three

automation domains, we have to review the draft Reference Architecture reported in the

project’ DoW (see Fig. 3-4). This picture tries to simply characterize next generation FI-

based enterprise ICT systems and applications as analysed and reported by the FInES Cluster

Architecture Task Force5 and the MSEE project

6. The DoW draft architecture highlights the

requirement to design and develop collaborative and interoperable enterprise systems for the

next generation manufacturing systems, and that these next-generation systems must be able

to address he needs of an ecosystem of enterprises rather than the specific requirements of a

single subject (manufacturing enterprise or OEM).

The three layers depicted in Fig. 3-4 essentially try to highlight both the different “contexts”

(e.g., single enterprise/factory premise, business ecosystem, and the extended-ecosystem7 –

identified as FI level in the picture) and the essential data, models, knowledge and

ontologies, integration and interoperability requirements the future manufacturing systems

must address.

Fig. 3-4: FITMAN DoW draft Reference Architecture

5 FInES Architectural Design Principles (ttp://www.fines-cluster.eu/fines/jm/FInES-Task-Forces/fines-architectural-design-

principles.html) 6 Manufacturing Service Ecosystem (http://www.msee-ip.eu) 7 This is the level that will characterize the Cyber-Physical Systems (CPS) as depicted in the German Industrie 4.0

programme [3] (“In the future, businesses will establish global networks that incorporate their machinery, warehousing

systems and production facilities in the shape of Cyber-Physical Systems”)

Cloud ManufacturingKnowledge

Future Internet Core PlatformGeneric Enablers

Applications/Services Ecosystems

Service Delivery Framework

Cloud Hosting and I/face to Network Devices

Internet of Things and Data/Context Mgmt

Security, Privacy and Trust

Business Ecosystem

Individual Factory/Enterprise

Future Internet Cloud

Interoperability of

Services/PlatformsAlignment of

Knowledge/Models

Human-centric Manuf. Models

IoT-based Intelligent Manufacturing

Ecosystem Skills& Knowledge

Ecosystem Manuf. Capacity

Domain Ontologiesand Models

Ecosystem Collaboration &Innovation Platform & Services

Product Life Cycle Management Platform

Service Life Cycle Management Platform

Innovation Life Cycle Management Platform

Collaborative Business Process / Project Mgmt

Production Planning and Scheduling

Smart Factory (In)Tangible Assets

Digital Factory (In)Tangible Assets

Virtual Factory (In)Tangible Assets

Enterprise [Mobile] ApplicationsPlatform & Services

Enterprise Resource Planning ERP System

Customer Relations Management CRM System

Supply Chain Management SCM System

Manufacturing Excution System MES

Automation, Control and SCADA System




The DoW draft reference architecture tries to highlight the main challenges business

activities, and manufacturing in particular, must address to maintain a coherent and

consistent alignment of models and data/information/knowledge across a wide set of

subjects, systems and boundaries, and at the same time assure their sharing and protection.

The blue bordered boxes and arrows in the picture represent respectively

data/information/knowledge repositories or sources and flows necessary to support business

and production processes, as well as to assure consistency and synchronisation among the

different “contexts”. The red boxes in the figure sketch the main services that characterise

each layer, while the red arrows highlight the main interaction patterns and issues.

The Smart, Digital and Virtual Factory reference architectures described in the following

take into accounts the points discussed in this section identifying the functional elements that

characterise the three manufacturing domains, as well as the strategies, requirements,

opportunities and constraints highlighted in the previous chapters.

3.2. The FITMAN Smart Factory Reference Architecture

The FITMAN Smart Factory reference architecture has to address the requirements

highlighted in the section 2.1.3 (on pag. 13) and, among the issues and challenges listed in

sections 2.1.1and 2.1.2, the ones that specifically affect this manufacturing domain. The

FITMAN Smart factory trials needs summarized in section 2.2 have, of course, to be fulfilled

by the FITMAN Smart Factory architecture even if each trial could not require and deploy

each of the envisaged functionalities.

The FITMAN Smart Factory architecture has therefore to envisage features that, on the one

hand, collects, adapts and dispatches events coming from a wide set of sources, and, on the

other hand, pre-processes, integrates, analyses and provides to users of the Smart Factory

functionalities customized views or hints to govern and improve the production processes.

Due to the need to manage a production facility (and the shop floor in particular) and react to

events occurring in this environment the FITMAN Smart Factory architecture is more an

Event Driven (EDA) architecture [8][9][10][11] than a Software Oriented (SOA) one.

The application of EDA-based solutions in manufacturing, although not consolidated and

systematic, has gained a considerable attraction in recent years [12][13]. Indeed, in the last

years many attempts [14][15][16] [17][18][19][20] have been made to specify, develop and

implement a SOA-based or EDA compliant distributed embedded control and automation

systems covering the major levels of the ISA´95 Enterprise Architecture.

The Smart Factory architecture has both an upstream flow of data and information, as well as

a downstream flow that is used to “govern” the factory production processes.

Fig. 3-5 depicts the overall FITMAN Smart Factory architecture. The picture highlights:

the users of the Smart Factory functionalities. As sketched in the figure the

functionalities are targeted to support: application services (e.g., legacy systems),

through which production processes are managed, and workers (both blue and white

collars, both to improve the workplaces and to offer them customized views of the

production facility and processes or to provide them the opportunity to act on these

elements). Application services and workers can be both within the production

facility or even outside (e.g., remote maintenance services managed by suppliers or

maintainers);




the sources of the data required to achieve the management objectives of the Smart

Factory systems. As highlighted in the picture the data can originate from the outside

of the production plant (e.g., events coming from suppliers, logistics operators, etc.

for example carrying information about the delivery of components for Just in Time

production),, as well as from within the production facility;

two middle layers that, as detailed below, provide functionalities to manage collected

events (or received control instructions), (pre-)process them, and provide added value

to the users of the Smart Factory system.

Fig. 3-5: The FITMAN Smart Factory Reference Architecture

Fig. 3-5 differentiates the sources of data/events grouping them in four different boxes:

External Events: are events, relevant for the production processes, coming from the

outside of the production facilities (e.g., warehouses, material-tracking events). The

structures and dispatching mechanisms of these events are quite variable;

Shop Floor (Sensors, …): under this category the picture collects events generated by

commodity sensors deployed within the shop floor (e.g., smoke sensors). This

category of sensors uses communication mechanisms and protocols, data structuring

not specific of the manufacturing environment (even if we have to expect in the near

future this category to use things like CoAP);

Shop Floor (RFID readers, …): this box represents sensors used to identify and track

materials, tools, goods and other elements within the production facilities. The

communication mechanisms, protocols and data structures are normally quite

standardized ([21][22][23] [24][25]) by subjects like ISO, GS1, EPCGlobal Inc;

Shop Floor (PLCs, OPC UA complaint devices, …): this last box represents other

manufacturing components deployed within production cells and lines that are used

to monitor and control production steps. Normally these components have more

computing power and more advanced communication capabilities and exposes some

kind of API. There are currently standardization efforts to define a common set of

External Events(suppliers, …)

Shop Floor(sensors, …)

Smart Factory Back-end Layer Events Collection & Preprocessing

(Publish/subscribe, security, Complex Event Processing, …)

Shop Floor(PLCs, OPC UA complaint devices, …)

Shop Floor(RFID readers, …)

Smart Factory Gateway Layer Device Management & Data Adaptation

(M2M, GSN, GS1 EPC ALE Service, Protocol conversion, …)

Smart Factory Workplace Layer(HMI, Safety, …)

Legacy Systems(ERP, SCADA; MES, …)




standards to support standardized APIs and in particular the OPC UA [26], promoted

by the OPC Foundation [27], and the US MTA MTConnect [28].

All these categories of events and data sources must be managed and related events

collected, translated into a common format, and made available through a common API to

avoid persist in having vertical, disjoint information silos.

Most of these sensing elements need also to receive commands (e.g., instructs an EPC Global

ALE Service to provide events related to specific RFID readers or RFID tags; activate a

specific procedure on a PLC). Therefore, a Smart Factory reference architecture has to try to

depict a common mechanism for the downstream flow of commands and control instructions.

Unfortunately, while for the upstream flow the standardization effort has achieved some of

its objectives8, a similar result is still in a very early stage for the downstream flow. For

example, the OPC UA standard on the one hand has defined a standardized API (based on

the SOA paradigm) to interface manufacturing devices, on the other hand, it has not yet

defined any standard that identifies common features and characteristics for manufacturing

device categories exposing an OPC UA interface9. Therefore, while interacting with an OPC

UA device can be performed in a quite uniform way, as well as acquiring some data from it,

controlling the same device is still strongly dependent on the device type and device

manufacturer.

The Fig. 3-6 details the FITMAN Smart Factory Gateway Layer. The objective of this layer

is to provide an interface as uniform as possible to the upper layers both for the collected

data, as well as for the downstream command flow. As depicted in the figure, this layer

envisages a Protocol Adapters component that, thanks to the use of a plug-in approach, can

interface different data source (see their description in the bullets above) and mask the

specific protocols and data exchange patterns these data source requires.

The Protocol Adapters component, therefore, provides a protocol-independent interface to a

potential wide and time evolving set of data source.

The Data Collection & Adaptation Sublayer component, instead, is in charge of both

managing the capture of the data from the deployed data sources, exposing the collected data

using a more uniform data structure format, as well as feeding the upper layer using a

uniform protocol. This component can therefore include functional elements that are

specifically tied to the deployed data sources.

Just as an example, acquiring data regarding RFID tagged goods or tools, implies the

deployment in the environment of RFID readers able to detect tagged goods in the

environment. These RFID readers, in our architecture, are part of the Protocol Adapters

component and have to support and manage the protocol and data exchange patterns

envisaged by the RIFD standards for the RFID reader to tag interaction. The RFID readers

expose a command and query interface, as specified by the GS1 EPC Global standards,

toward upper layers and, in the GS1 EPC Global architecture, specifically toward the EPC

Global ALE Service [22][23]. The ALE Service functionalities are part of the Data Collection

& Adaptation Sublayer component, so that this component can interface the RFID sources

via an interface that does not depend on the specific hardware and RFID radio protocols

used. At the same time the data collected from the RFID readers are fed to the upper layer in

our architecture using an interface that is independent from the specific source (usually via a

RESTful API).

8 For example the MTConnect standard defined by the US ATM (Association for Manufacturing Technology) is a read-

only standard that only defines the extraction of data from control devices 9 An example of a fruitful standardization to this end is the one performed defining standardized Management

Information Base (MIB) to be used in connection with the SNMP to supervise and manage network devices




The Device Management Sublayer component in Fig. 3-6 performs similar adaptation for the

downstream command flow.

Fig. 3-6: The FITMAN Smart Factory Gateway Layer Architecture

The Fig. 3-7 depicts the FITMAN Smart Factory Back-end Layer that is in charge managing

the collection and dispatching of the events coming from the FITMAN Smart Factory

Gateway Layer, perform some pre-processing and analysis and provide data and outcomes to

the legacy systems or to the applications supporting the end-users.

Fig. 3-7: The FITMAN Smart Factory Back End Layer Architecture

As depicted in the figure, this layer envisages the following functional components:

the Security Assessment Features component that provides features to anonymise

data and check its quality. This module provides a kind of off-line functionality to be

used to assess if the adopted anonymization algorithms actually achieve their

Smart Factory Gateway Layer

Data Collection & Adaptation Sublayer

Device Management Sublayer

Protocol Adapters

GS1EPC

GSN …OPC UA

Smart Factory Back-end Layer

Security Assessment

Features

Event Collection&

DispatchingFeatures

ConfigurationManagement

Features“Syntactical”CEP Features

“Dynamic” CEP&

Reasoner FeaturesObj. a

Obj.

Obj. …

Pat. X

Pat. Y

Pat. …




objectives and therefore make documentable and visible the compliance of the

management of business confidential or personal data to company policies or national

or European normatives. This component play a relevant role in all smart factory

contexts in which data regarding workers is collected and processed (for example to

improve the safety of workers as in the TRW trial), or contexts in which business

confidential data have to be shared, in a properly anonymised way, with external

subjects;

the Event Collection & Dispatching Features component is in charge of receiving

events from the lower layer and dispatch it to interested components. Having to

manage shop floor related events, which are characterized by their confidentiality

nature (these events are strictly related to the production, its organization, etc.) and by

their amount, timing and variability, this component has to provide advanced features

to control access to these events and to scale. This component is a key element of the

FITMAN EDA approach that promotes a unified management of events across all

levels of the automation pyramid based on the adoption of a publish/subscribe model

that pushes events to interested listeners as compared to the pull model of more

traditional SOA approaches. Push models have normally unidirectional,

asynchronous and fire-and-forget communication patterns, promoting the use of

highly decoupled systems in which the only relevant issues are related to well-

defined message semantics, as well as provide better scalability;

the “Syntactical” CEP Features component is the element in charge of performing

analysis of the event’s streams normally based on a pattern-search approach within

real-time event’ streams. The component is, therefore, able to identify anomalies in

the collected data and to generate new events (typically directed to the Event

Collection & Dispatching Features component to be pushed to interested

applications) or directly actions (normally toward the Configuration Management

Features component). This component is, therefore, one additional key element of

the FITMAN EDA approach and supports off-loading supervising and control logic

from traditional production supervision systems (e.g., SCADA and MES systems)

improving a more fast and less expensive adaptation10

of the production management

and control systems;

the “Dynamic” CEP & Reasoner Features component provides more advanced

analysis functionalities as compared to the previous component. As depicted in the

Fig. 3-7 this component has to provide events analysis capabilities that try to achieve

an objectives (e.g., reduce the energy consumption in producing goods) instead of

identify a pattern in the collected data. This component, therefore, needs more

complex reasoning capabilities to achieve the provided objectives, and normally

needs to process a wider set of data. As for the previous component the outcomes of

this component can be new events (normally to be dispatched via the Event

Collection & Dispatching Features component), or actions to be normally managed

via the Configuration Management Features component;

the Configuration Management Features component is in charge of supporting the

management of the configuration of devices and components in the lower layer. As

depicted in Fig. 3-7, this component can be fed by the upper components (e.g., the

legacy systems), as well as by the two CEPs. This component in charge of managing,

with the limitation and issues highlighted in the previous pages, the conversion of the

10 Indeed CEP engines provide functionalities to structure and deploy the pattern to be searched, as well as to design the

actions to be performed when a pattern is identified. Setting up new analysis, or modifying existing ones, requires simple to

properly structure the pattern to be searched and the related actions, testing and deploying them, as compared to changes of,

or integration to, the SCADA or MES systems software




downstream control flow, or actions received by the CEP components, into

operations and command toward the lower layer components.

A final element to be described is the Smart Factory Workplace Layer component that is in

charge of interacting with, and supporting, the workers (both blue and white collars) in their

daily activities within the production facilities. This component has to support next

generation, attractive human-machine interaction (Human-centred Manufacturing), through a

disparate set of devices (e.g., mobile devices, 3D visors, wearable devices, etc.) and

modalities (graphical rendering, Augmented Reality, Virtual Reality, etc.). It is expected that

this component will be heavily based on new HTML-centred standards (e.g., HTML5,

XML3D, Web Components, etc.), and approaches to improve usability across disparate

platforms, integrability, flexibility and data access on the move. The final objective of this

component is therefore to substantially increase the efficiency and workers’ safety as well as

a user-friendly, ergonomic and intuitive interaction between workers and machines.

3.3. The FITMAN Digital Factory Reference Architecture

As for the Smart Factory, the FITMAN Digital Factory reference architecture has to address

the specific requirements highlighted in the section 2.1.4 (on pag. 14), as well as the related

issues and challenges listed in sections 2.1.1and 2.1.2.

As stated in previous sections, a manufacturing context referring to, or implementing, the

FITMAN Digital Factory architecture, and specifically the FITMAN Digital Factory trials,

have to refine this reference architecture and can deploy and implement only a subset of the

envisaged functionalities. The Digital Factory domain needs functionalities to make a more

intelligent and informed decisions at the various levels (strategic, planning, operational, shop

floor) accessing and processing all the required information that is currently kept in isolated

silos, therefore making difficult to streamline the work based on a natural and controlled

flow of information. The main issue that the Digital Factory Reference Architecture has to

address is to envisage functional elements able to provide effective means to consolidate a

view on Product Life Cycle Information, so that more flexible and contextual access, query

and analysis can be performed on the distributed product information. This implies that

Digital Factory architecture should provide means to represent and expose data coming from

multiple sources, so that the information in legacy systems (planning tools, ERPs, CRM,

etc.) and also in open contexts like social networks can be exploited. To this end it is

worthwhile to highlight the recent explosion of data entering into the landscape of business

companies, with an entirely new class of data that was never envisioned before and by legacy

systems. Therefore, one of the objective of the FITMAN Digital Factory reference

architecture is to enable organization to incorporate these new data sources into their

business and analytics applications.

Moreover, industrial competitiveness depends not simply on the information availability, but

also on its quick and effective presentation of such information in the required format for

proper decision-making.

To this end, the FITMAN Digital Factory Reference Architecture reported in Fig. 3-8

envisages:

a set of data sources as provided by legacy systems or acquired from external sources

(e.g., Internet social networks). These data sources, as stated above, have direct

relations with the produced goods, marketing or brand strategies, trends discovery,

etc. In Fig. 3-8 legacy systems actually have a double role: as storage systems that

manage products related data, which have to be suitably accessed, processed and

integrated to fulfil the objectives of the Digital Factory architecture (see the bottom




data sources icons in the figure); as well as services (see the azure Legacy Systems

box in the figure) to be integrated within the overall functional architecture;

the users of the Digital Factory functionalities. As sketched in the figure the

functionalities are targeted to support end-users (blue and white collars, individually

or in teams) in their daily activities, both within and outside the manufacturing

company, and along the whole lifecycle of produced goods (e.g., since the product

design phase, till its disposal; production line design, operation, maintenance,

restructuring or disposal, etc.);

two middle layers that, as detailed below, provide functionalities to access the data

silos, aggregate and manipulate these data, process and render them using rich and

effective presentation and visualization features to support advanced business

processes.

Fig. 3-8: The FITMAN Digital Factory Reference Architecture

Fig. 3-9 details the functional components of the lowest layer envisaged by the FITMAN

Digital Factory Reference Architecture. As indicated in the picture, this layer envisages two

different pillars:

the Product Lifecycle & Metadata Management Sublayer: this component collects

functionalities to manage access to a disparate set of, normally structured, PLM

related data and metadata and has the objective of providing a consistent, integrated

and uniform representation and interface to them. This includes the capability to

perform syntactical and semantic transformation of the available data and metadata in

order to assure interoperability and the accessibility from a wide set of services and

systems;

the Unstructured & Social Data Analysis Sublayer: this component, instead, is

focused on providing access to information, usually unstructured, as available in

outside and open systems (e.g., social networks).

Product Lifecycle Data Visualisation & Manipulation Layer Product Lifecycle Data Visualisation, Presentation, Rendering

Product Lifecycle Data & Analysis Management LayerProduct Lifecycle Data Management, Access, Query, Analytics

Legacy Systems(ERP, LMS, PLM, PDM, CAD, …)

Product Data

Process Data

MKT Data

XXX Data Social Net

Data




As for the Smart Factory reference architecture, these Digital Factory sublayers envisage a

set of plug-ins at the lowest level that interface the different data sources. In particular, the

Product Lifecycle & Metadata Management Features component will take responsibility for

interfacing the different PLM data sources, while the Social Media Connectors component

will be in charge of feeding the Unstructured & Social Data Analysis Sublayer picking data

from the social networks or web sites.

Both components therefore mimic the approach envisaged by the Java Service Provider

Interface (SPI) [33] decoupling the upper components from the details of the accessed data

sources. This approach will be further enhanced by the Interoperability Services component

so that upper components get a uniform access interface and uniform data and metadata

formats. The final objective of these components is to support the adoption of standard

metadata for product data and knowledge representation, support interoperability among

systems and data format standards thanks to the support of different access protocol toward

the data sources, syntactical and semantically enriched data transformation, uniform browse

and query functionalities.

Fig. 3-9: The FITMAN Digital Factory PL Data Management Layer Architecture

In addition, on the Unstructured & Social Data Analysis Sublayer side, the FITMAN Digital

Factory reference architecture envisages two additional components. The first of these

components (see the Processing/Querying Engine in Fig. 3-9) supports the pre-processing

the acquired unstructured data, and the provision of a first set of query functionalities. The

second component (see the Trend & Sentiment Analysis Engine in Fig. 3-9) focuses on

performing specific analysis of the acquired data to support trend and sentiment analysis.

These kind of analysis are becoming particularly relevant [34] [35] in many manufacturing

contexts, and in some of the FITMAN trials.

Product Lifecycle Data & Analysis Management Layer

Product Lifecycle & Metadata Management Sublayer

Product Data

Process Data

MKT Data

XXX Data Social Net

Data

Product Lifecycle & MetadataManagement Features

Interoperability Services

Unstructured & Social Data Analysis Sublayer

Social Media Connectors

Twitter …FBRSS

Processing /Querying Engine

Trend & Sentiment Analysis Engine




Fig. 3-10 depicts the functional components envisaged by the FITMAN Digital Factory

architecture Product Lifecycle Data Visualisation & Manipulation Layer whose main

objective is to support the effective, efficient and customized access and rendering of the

required and available information to the white and blue-collars involved in the

manufacturing phases and processes.

As indicated in the figure this layer envisages a set of functional components that, on the one

hand, enhance the analysis and query features provided by the underlying components, on

the other hand, are able to support end-users in effectively using the available services and

efficiently selecting and accessing the information required and necessary for their activities.

To this end the envisaged functional components are:

the Unstructured Data Analysis Features component complements the features on the

underlying components to extract knowledge from structured and unstructured data,

even supporting the management of data analysis pipelines for data and knowledge

transformations and interpretation;

the Application Mashup Features component supports the integration and

composition of services, data sources and workflows to satisfy end-users needs.

Among its features this functional component has to provide support the DIY (Do-It-

Yourself) metaphor so that end-users with minimal skills can design and create new

services or workflows to satisfy their needs as they emerge;

the Unstructured & Social Data Analysis Features component complements and

completes the functionalities of the homonymous underlying component on issues

related to the design and management of reports, as well as to support data

visualization, usually, but not restricted, related to the trend and sentiment analysis;

the 3D Rendering Features component is focused on providing functionalities to

support web based 2D and 3D rendering and interaction to access and manipulate

data and digital objects (e.g., CAD models, technical sheets, assembly procedures,

etc.) as requested by end users. This component has to possibly support web related

2D and 3D standards, as well as the possibility for services in the FITMAN platform

to interact with the 2D and 3D environments and objects so to support more dynamic

and interactive interaction experiences;

finally the Advanced Data Visualization & Manipulation Features component has to

provide a uniform access to the services, data and objects and to support a possibly

wide set of rendering devices (including mobile and wearable ones), and

collaborative visualization so that end-users can use the features provided by this

component to perform both their personal, as well as joint activities.




Fig. 3-10: The FITMAN Digital Factory PL Data Visualization & Manipulation Layer Architecture

Even if not strictly required, it is expected that the standards adopted for the rendering and

interactions be aligned with the ones envisaged for the FITMAN Smart Factory reference

architecture. Therefore focused on the mentioned new HTML-centred standards (e.g.,

HTML5, XML3D, Web Components, etc.), both to improve usability across disparate

platforms, integrability and flexibility, as well as to support contexts and trials with needs

pertaining to the smart and digital manufacturing domains.

3.4. The FITMAN Virtual Factory Reference Architecture

As for the previous reference architectures, the FITMAN Virtual Factory architecture has to

address the specific requirements highlighted in the section 2.1.5 (pag. 15), as well as the

related issues and challenges listed in sections 2.1.1 and 2.1.2.

The Virtual Factory architecture, according to the analysis in § 2.1.52.1.4, has to envisage

ICT functional elements able to support collaborative supply networks, and, therefore,

essentially focused on assuring inter-company communication, integration, collaboration and

interoperability, as well as on managing tangible and intangible assets [36] [37]. As indicated

in Fig. 3-11, the main beneficiaries of the Virtual Factory functionalities are the legacy

systems through which the end-users will normally manage the business processes envisaged

by the Virtual Factory domain.

The FITMAN Virtual Factory Reference Architecture reported in Fig. 3-11 envisages:

a set of sources of information and data related to tangible and intangible assets that

span from supply chains (through which structured data like: orders, invoices,

packing lists, transportation statuses, etc. are exchanged), value networks (through

which knowledge and value is created and exchanged [36] [38] [39]), and, more

generally, the business ecosystems (through which suppliers, distributors,

competitors and customers compete and cooperate to produce goods, improve them

and create new ones [40] [41] [42]);

Legacy Systems(ERP, LMS, PLM, PDM, CAD, …)

Product Lifecycle Data Visualisation & Manipulation Layer

Unstructured & Social Data Analysis Features

Visualization & ReportCreator EngineUnstructured

Data Analysis Features

Application MashupFeatures

3D RenderingFeatures

Advanced Data Visualization & Manipulation Features




two middle layers that, as better described and detailed in the following, must, on the

one hand, manage discovery, classification and management of data pertaining to

tangible and intangible assets involved in virtual factory business processes, and, on

the other hand, support cooperative business process design and management to

assure cross-enterprise boundaries interoperability and collaboration;

on the top layer the figure reports enterprise legacy systems to stress the relevance of

the interoperability and integrability across enterprises. Indeed, end-users normally

interact and collaborate through business processes involving legacy systems within

and across enterprises.

As evident from the objectives of the virtual factory domain, standards play a key role in this

context, both to structure and exchange the exchanged information (e.g., standards like

OASIS UBL11

, RDF12

, OWL13

), to describe available services [43] [44], as well as to design

(e.g., BPMN14

, UML AD15

), exchange (e.g., XPDL16

, BPDM17

), or execute (e.g., BPEL18

)

business processes [45].

11 See the OASIS UBL Technical Committee web site for more information (https://www.oasis-

open.org/committees/tc_home.php?wg_abbrev=ubl) 12 See the W3C Resource Description Framework (RDF) status page for more information

(http://www.w3.org/standards/techs/rdf) 13 See the W3C Web Ontology Language (OWL) Working Group web site for more information

(http://www.w3.org/2007/OWL/wiki/OWL_Working_Group) 14 See the Object Management Group (OMG) web section on BPMN (http://www.omg.org/spec/BPMN/index.htm) . OMG

BPMN 2.0.1 has been published by ISO on November 2013 as ” SO/IEC 19510:2013 - Information technology - Object

Management Group Business Process Model and Notation” 15 For details on UML Activity Diagrams (UML AD) see the OMG document ”UML 2.0 Superstructure Specification”,

Mars 2011 (http://www.omg.org) 16 For references and specifications see the Workflow Management Coalition XDPL web site (http://www.xpdl.org/) 17 See the OMG web site for BPDM specification (http://www.omg.org/spec/BPDM/) 18 See the OASIS WSBPEL Technical Committee web site for more information (https://www.oasis-

open.org/committees/tc_home.php?wg_abbrev=wsbpel)




Fig. 3-11: The FITMAN Virtual Factory Overall Architecture

Fig. 3-12 details the FITMAN VF lowest sublayer that envisages the following functional

components:

the Manufacturing Assets Management Features component that is in charge of

providing services to identify, discover, tag and provide access to tangible and

intangible assets as available within, or provided by, the depicted sources of

information. More specifically this component has to provide features to syntactically

and semantically manage discovery, tagging and access to assets, as well as to

properly represents them;

the Assets & Services Repository component that is in charge to store data and

metadata about the managed assets. This component complements the previous one

in offering a uniform, consistent and dynamic access to tangible and intangible assets

required by the VF business processes;

the Marketplace Management Features component is in charge of supporting

offering and usage of application services deployed within a VF environment. This

component therefore provides services to store data and metadata, based on standards

like USDL19

or Linked-USDL20

, about available services (e.g., service registry and

directory), discover services, match demand for services (even using semantic

matching features provided by other components in this sublayer) against the

available ones, as well as support dynamic service composition;

the Semantic Analysis Features component instead is in charge of providing

specialized semantic features to properly classify tangible and intangible assets, as

well as available services (which can be considered as assets too), cluster them

according to application needs and support composition and dynamic discovery on

the basis of semantically defined attributes and constraints;

19 See the W3C USDL Incubator Group web site for more information (http://www.w3.org/2005/Incubator/usdl) 20 See the Linked-USDL web site for documentations and tools (http://www.linked-usdl.org)

Enterprise Interoperability and Collaboration Layer

Enterprise Tangible / Intangible Assets Management Layer

Legacy Systems(ERP, SCM, CRM, PLM, …)

Supply Chains

Value NetworksBusiness Ecosystems

ERP SCM

CRM

MRP

Company A

CRM

SCM

MRP

ERP

Company B

CRM

SCM MRP

ERP

Company …




the Supply Chains & Business Ecosystems Management component, finally, provides

functionalities to support the usage of assets in collaborative production capacity

planning and team building. On the capacity planning side this component can

support, for example, discovery on unplanned production capacity and sharing of

manufacturing equipment and production facilities. On the team building side,

instead, the component can support in identifying the most suitable combination of

people having the most appropriate skills for the activity to be performed according

to the activity plan and people availability.

Fig. 3-12: The FITMAN Virtual Factory Assets Management Layer Architecture

As described in the previous section the Enterprise Tangible / Intangible Assets Management

Layer embraces components that provide a uniform set of functionalities to manage (e.g.,

discover, search, classify, combine and access) tangible and intangible assets, where assets

can be both data (e.g., business or technical documents, people profiles), as well as services.

On top of the assets management sublayer the Enterprise Interoperability and Collaboration

Layer is in charge of enhancing the lowest layer functionalities and add new ones to support

cooperative business process design and management and cross-enterprise interoperability

and collaboration. The functional components in this sublayer, depicted in Fig. 3-13, can be

shortly characterized as follows:

the Identity Management component provides the authentication services within the

cross-enterprise environment, therefore assuring that only authenticated subjects

(e.g., people, services, devices) are involved in business processes;

the Semantic Application Support Features component is in charge of providing

features to semantically manage assets (i.e., data and services as described in the

previous sections), if necessary convert their format as requested by the application

service or the business process, provide advanced syntactical and semantic search

services and manage ontologies, taxonomies and document formats;

the Collaborative BP Management Features component provides services to design

and manage business processes as requested by the cross-enterprise environments.

Enterprise Tangible / Intangible Assets Management Layer

Supply Chains

Value NetworksBusiness Ecosystems

MarketplaceManagement Features

Supply Chains & Business Ecosystems Management

SemanticAnalysis Features

Assets & Services Repository

Manufacturing AssetsManagement Features




The managed business processes can be also semantically characterized so to achieve

the twofold objective of composing processes on the basis of semantically based

assets (i.e., both data and services) identification and integration, as well as of

semantically tagging the business processes themselves so to make them more easily

usable and reusable;

the Interoperability Services component, finally, complements the above components

in providing interoperability (e.g., dynamic and semantic data format transformation,

data and service adaptation) so that the managed assets and defined business

processes can actually be used by, and integrate, the enterprise systems (as sketched

in Fig. 3-13).

Fig. 3-13: The FITMAN Virtual Factory Interoperability & Collaboration layer Architecture

Even if the Virtual Factory Interoperability Services component has some commonality with

the Digital Factory similarly named component (see Fig. 3-9), the two have a different

objective: the Virtual Factory Interoperability Services component has the primary objective

to support interoperability among legacy systems, as well as the setup of cross-enterprise

collaborative processes, while the Digital Factory component is focused on providing a

uniform interface to data and metadata as grabbed from the different prodct data silos.

The Virtual Factory reference architecture completes the FITMAN reference architecture

description.

As stated in section 2.1 the smart, digital and virtual manufacturing domains are abstractions

that try to characterize a manufacturing environment taking into account their most relevant

aspects. An actual manufacturing environment will, of course, envisage and require a

specific mix of smart, digital and virtual features. As described in section 3.1 each

environment (and, specifically, the FITMAN trials) has to design its own architecture taking

Enterprise Interoperability and Collaboration Layer

Semantic ApplicationSupport Features

Collaborative BP Management

Features

ERP SCM

CRM

MRP

Company A

CRM

SCM

MRP

ERP

Company B

CRM

SCM MRP

ERP

Company …

IdentityManagement

Interoperability Services




into account the three reference architectures and its specific needs combining the features

described in sections 3.2, 3.3 and 3.4.

3.5. The FITMAN Manufacturing Domains Architectures and FI-WARE GEs

3.5.1. The FITMAN Enablers

This section, and the following ones, provides an overview on how the FI-WARE GEs and

other FITMAN components fit with the reference architectures described in the previous

sections.

According to the FI-WARE Vision and Goals [46] and to the architectural approach

analyzed in the previous sections, each of FITMAN trial architecture implementations will

combine a set of elements providing specific functionalities. These elements are classified as

follows:

FI-WARE Generic Enablers (GE): that are defined by FI-WARE as offering

“reusable and commonly shared functions serving a multiplicity of Usage Areas

across various sectors”. The GEs selected by FITMAN, and their selection

rationales, are deported in the D1.3 deliverable [47];

FITMAN Specific Enablers (SE): these are components providing functionalities

that, on the one hand, are specific to an application domain (and specifically to one or

more of the three manufacturing domains), but on the other hand are quite common

in the application domain. The FITMAN SEs requirements are detailed in the D1.2

deliverable [48];

FITMAN Trial Specific Components (TSC): these are elements that are required by

a specific context (e.g., a specific FITMAN trial) and therefore cannot be reused in

other contexts.

The following three sections are therefore devoted to describe which FI-WARE GEs and

FITMAN SEs contribute to the three reference architectures.

More or less GEs in all FI-WARE chapters [49] contribute to the FITMAN reference

architectures, with the notable exception of the FI-WARE Cloud Hosting chapter. Actually

this chapter “… offers Generic Enablers that comprise the foundation for designing a

modern cloud hosting infrastructure that can be used to develop, deploy and manage Future

Internet applications and services”21

. Therefore, the GEs in the cloud chapter do not directly

provide “application functionalities”, but are there to support the dynamic deployment and

management of applications and services, which is one of the key features envisaged for the

manufacturing environment [2][3][4][5][6].

To reflect the relevance of the Cloud Hosting GEs to deploy and provide FITMAN

compliant manufacturing systems a specific section (see section 3.5.6) has been added that

highlights the currently envisaged FI-WARE cloud GEs to be used to support FITMAN

manufacturing systems.

3.5.2. The FITMAN Reference Architectures and GEs /SEs

The following sections, as stated, will review the three manufacturing architectures mapping

the functional components in them to the selected FI-WARE GEs and envisaged FITMAN

21 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/Cloud_Hosting_Architecture




SEs. The sections will therefore both describe the identified elements, as well as graphically

propose the mappings.

The pictures in the following sections are based on the ones used in sections 3.2 - 3.4 and

envisages three additional elements (see Fig. 3-14 for the colours’ meanings):

FI-WARE GE: the identified GE22

provides all or part of the functionalities

envisaged for the FITMAN architecture functional component;

FITMAN SE: a specific functional element has been identified as able to provide

totally or partially the functionalities of the architectural components and the

FITMAN partners are able to provide it23

;

FITMAN Open Call SE: the envisaged functional element will be acquired through

the Open Call process. The call will provide details on the expected functionalities

and positioning within the specific reference architecture.

Fig. 3-14: The GEs / SEs graphical representations

3.5.3. The FITMAN Smart Factory GEs/SEs

The Fig. 3-15 sketches the envisaged GEs and SEs for the FITMAN Smart Factory

Reference Architecture. Starting from the top the identified enablers are:

SF Open Call Topic II SE (Workers, Actuators, AmI, Smart Spaces): this SE is part of

the FITMAN Open Call for Smart Factory and, specifically, is specified as the 2nd

topic of the Smart Factory section in the call. The functionalities envisaged for this

SE are to support “SF next generation, attractive human‐machine interaction

(Human‐centered Manufacturing), including devices and software components for an

advanced automation of the shopfloor efficiency and safety as well as a user‐friendly,

ergonomic and intuitive interaction between workers and machines, including data

access on the move”. As indicated it is expected that the requested SE will cover all

the functionalities envisaged for the Smart Factory Workplace Layer architectural

component;

SF Open Call Topic I SE (Dyn. CEP, Monit. & Diagnosis): also this SE is part of the

FITMAN Open Call for Smart Factory (it is the 1st topic for the Smart Factory). The

requested SE has to support “SF monitoring and diagnosis (Advanced Intelligent

Manufacturing), including dynamic and re‐configurable filtering and processing of

real world events, coming from sensor networks embedded in machinery and

22 The FI-WARE GE is normally a FI-WARE release I GE. FI-WARE Release II and III have also been tentatively

identified based on their description as available on the WI-FWARE web site 23 For example reusing, enhancing or customizing outcomes of previous projects

Legend

...…

FITMANOpen Call SE

……

FI-WARE GE

...…

FITMAN SE




workplaces, shopfloor smart objects and tools, in‐bound logistics of tagged products

and materials, manual operations and workers wellbeing monitoring and control”.

As for the previous point it is expected that the requested SE will cover all the

functionalities envisaged by the “Dynamic” CEP & Reasoner Features architectural

component;

Config. Manag. GE: is part of the FI-WARE IoT Backend and is in charge of IoT

agents’ context management24

. This GE contributes to the provision of the

functionalities envisaged by the FITMAN Smart Factory Configuration Management

Features architectural component;

Re. II Backend Dev. Manag. GE: is also part of the FI-WARE IoT Backend system

and is in charge of the management of the kind of remote assets envisaged by the FI-

WARE IoT Backend system25

. FITMAN plans to use the FI-WARE Release II

version of this GE. Coupled with the previous GE this one completes the

functionalities envisaged for the FITMAN Configuration Management Features

architectural component. Additionally, this FI-WARE GE contributes to implement

the Device Management Sublayer architectural component as depicted in Fig. 3-15;

Re. II DB Anonym. GE: this is the FI-WARE DB Anonymizer (release II) GE26

that

covers all the functionalities envisaged for the FITMAN Security Assessment

Features architectural component;

SEI_2 Secure Events Man. SE: this is a FITMAN specific enabler that provides the

features27

envisaged by the Event Collection & Dispatching Features architectural

component. This SE is based on outcomes of the FP7 IoT@Work project28

and is

specifically designed to manage secure and scalable events collection and dispatching

for shop floor environments, as well as to assure strict access control to events and

scalability which, as argued in section 3.2, are critical features in smart factory

context;

IoT Broker GE: this GE provides a lightweight and scalable middleware component29

that helps in decoupling applications from underlying IoT devices easing their

management and translating their data into a common format and using a common

access approach. This GE will be mainly used in cooperation with the Protocol

Adapter GE to manage the device types envisaged by that GE. This GE contributes to

the provision of the features envisaged by the FITMAN Data Collection &

Adaptation Sublayer architectural component;

Protocol Adapter GE: this GE, as described in its FI-WARE specification30

, is in

particular devoted to support ZigBee devices and, therefore, contributes to the

implementation of the Protocol Adapters FITMAN architectural component

providing specific plug-ins for the GE supported devices;

SEI_1 Data Collection SE: this too is a FITMAN specific enabler31

that contributes

both to the implementation of the FITMAN Data Collection & Adaptation Sublayer

architectural component, as well as to the Protocol Adapters component. Actually

24 http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.IoT.Backend.ConfMan 25 http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.IoT.Backend.DeviceManagement 26 http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Security.Optional_Security_Enablers.DBAn

onymizer 27 http://catalogue.fitman.atosresearch.eu/enablers/secure-event-management 28 https://www.iot-at-work.eu/ 29 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.IoT.Backend.IoTBroker 30 http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.IoT.Gateway.ProtocolAdapter 31 http://catalogue.fitman.atosresearch.eu/enablers/shopfloor-data-collection




this SE is a collection of functional elements, as described on the FITMAN

catalogue. A first set of elements deal with the management of sensor networks and

enhances the related outcomes of the GSN project32

. A second set of elements is

focused on supporting RFID technologies in the shop floor and is based on the GS1 /

EPCglobal specifications and, specifically, on the Fosstrack33

implementation of

some of the EPCglobal specifications. This FITMAN SE, in particular, enhances and

integrates the Fosstrack implementation of the EPCglobal ALE Server [50], which

contributes to the realization of the FITMAN Data Collection & Adaptation Sublayer

architectural component, and the EPCglobal Low Level Reader Protocol (LLRP) [51]

to integrate standard-compliant RFID readers therefore contributing to complete the

FITMAN Protocol Adapters architectural component.

Fig. 3-15: The FITMAN Smart Factory Reference Architecture and GEs / SEs

32 http://sourceforge.net/apps/trac/gsn/ 33 See the Fosstrack project web page for further information: https://code.google.com/p/fosstrak/




As evident from the above description, the FI-WARE Internet of Things Services

Enablement chapter plays the role as the main chapter in the FITMAN Smart Factory

Architecture, due to the need of (near) real-time monitoring of the shop floor, device (e.g.,

sensors) management, and events collection and dispatching.

Additionally, the relevant role assigned to CEP (Complex Event Processor) engine’s enablers

will definitely allow getting an improvement in the manufacturing processes helping in

quickly (re-)adapt them or (re-)configure manufacturing machines to take into account the

actual production context and needs.

3.5.4. The FITMAN Digital Factory GEs/SEs

As for the previous chapter, Fig. 3-16 depicts the mapping between the FITMAN Digital

Factory reference architecture and the identified GEs and SEs. The following paragraphs

shortly described the identified elements:

DF Open Call Topic II SE (Product Data Visual. & 3D Rendering): this SE is part of

the FITMAN Open Call for Digital Factory and, specifically, is specified as the 2nd

topic of the Digital Factory call asking for features to support “collaborative product

data 3D visualisation (Collaborative and Mobile PLM), based on a collaborative

multi‐task project management environment and including devices and software

components for a web enabled rendering and interaction with 2D‐3D complex

manufacturing objects, e.g. CAD solids, points of clouds, large unstructured data‐sets including real‐time data repositories”. As indicated in Fig. 3-16 this SE is

expected to cover all the functionalities envisaged for the Advanced Data

Visualization & Manipulation Features architectural component;

Re. III XML 3D Web GE: this GE is a FI-WARE Release III GE providing

functionalities to create HTML based 2D and 3D interactive environments34

that can

therefore be accessible using simple web browser. This GE, which will be actually

available in FI-WARE Release III, has been inserted due to its relevance for the

Digital Factory as highlighted in sections 2.1.4and 3.3;

Application Mashup GE: this GE provides functionalities that make possible to

integrate heterogeneous data sources, UI widgets and services according to the user

defined application logic and create new applications. This GE fulfils the

functionalities envisaged for the FITMAN Application Mashup Features architectural

component;

Re. III Unstruct. Data Anal. GE: this too is a FI-WARE Release III GE35

to be soon

available to support processing of high volumes of unstructured data coming from the

Internet, therefore fulfilling the features envisaged for the FITMAN Unstructured

Data Analysis Features architectural component. This GE contributes also to provide

features envisaged by the FITMAN Product Lifecycle & Metadata Management

Sublayer (and specifically its Interoperability Services sub-component) and

Unstructured & Social Data Analysis Sublayer (specifically its Processing /Querying

Engine sub-component) FITMAN architectural components;

SEI_3 Unstructured and Social Data Analytics SE: this FITMAN SE36

, which

provides the features envisaged by the FITMAN Unstructured & Social Data

Analysis Features and, partially, by the Unstructured & Social Data Analysis

Sublayer architectural components, aims at extracting unstructured knowledge from

selected social media systems and web resources and at providing insights to user-

34 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/Materializing_Advanced_User_Interfaces_in_FI-WARE 35 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FI-

WARE_Data/Context_Management#Unstructured_data_analysis 36 http://catalogue.fitman.atosresearch.eu/enablers/unstructured-and-social-data-analytics)




generated content. As evident from Fig. 3-16 this SE is the main element of the

FITMAN Digital Factory reference architecture for the unstructured and social data

analysis and, as reported in the SE description on the FITMAN on-line catalogue, it is

based on the integration and customization of well-known and powerful open source

components to perform such kind of analysis;

Data Pub/Sub Broker GE: this is the FI-WARE publish/subscribe enabler37

that

supports collecting data (called context information in the FI-WARE specification)

and made them available to application consumers. The rationale behind the selection

of this GE within this FITMAN manufacturing domain is in its suitability for the

FITMAN Product Lifecycle & Metadata Management Sublayer architectural

components and its integrability with the other FI-WARE GEs selected for this

architectural component;

Mediator GE: this FI-WARE GE is a functional element38

able to provide

interoperability among different communication protocols and among different data

models, as such it will support conversion of data among different formats and

acquired from different sources;

DF Open Call Topic I SE (Prod. Lifecycle & Metadata Mgmt): this SE is the main

element of the Product Lifecycle & Metadata Management Features architectural

component and is in charge to provide a uniform access to PLM data sources, as well

as to enrich these data with meta-information to help upper layers in integrating and

properly processing the mediated data. This SE is part of the FITMAN Open Call for

Digital Factory and, specifically, is specified as the 1st topic of the Digital Factory

call asking for features to support “product data and knowledge in product life cycle

(standard‐based access to PLM and product‐item data), including definition and

adoption of standard metadata systems for Product Data and Knowledge

representation, semantic interoperability transformation services from heterogeneous

systems and/or available or de‐facto standards …”.

37 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Data.PubSub 38 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Apps.Mediator




Fig. 3-16: The FITMAN Digital Factory Reference Architecture and GEs / SEs

As evident from the above description, the main issue in the Digital Factory domain is to

have ICT systems able to support in taking more intelligent and informed decisions at the

various levels (strategic, planning, and operational). The challenge is mainly related to the

fact that information is kept in information silos and it is difficult to streamline work based

on a natural and controlled flow of information. The selection of the above GEs and SEs aim

at demonstrating how Future Internet technologies, and the FI-WARE platform in particular,

can contribute to solve, or alleviate, such situation.

The above selection of GEs/SEs provides an effective means to consolidate a view on

Product Life Cycle Information, so that more flexible and contextual access, query and

analysis can be performed on the distributed product information. Moreover, industrial

competitiveness depends not simply on the information availability but actuality on the quick

presentation of such information in the required format for effective decision making, as

envisaged by the GEs/SEs supporting the implementation of business intelligence and rich

information presentation and visualisation applications.




The main FI-WARE chapters contributing to the Digital Factory domain are the

Applications/Services Ecosystem and Delivery Framework and the Data/Context

Management chapters.

The first one plays a key role in overcoming the closures (i.e., data silos, vertical and

difficult-to-integrate applications) that currently characterize the manufacturing domain. The

possibility to opening the applications, integrate them, transparently access data, etc. that is

envisaged by this FI-WARE chapter improves the opportunities for software companies to

access reliable data and quickly deliver new products, which is further improved by the

possibility to use different APIs to mix the data and present it in a single solution without the

user knowing the application is made out of many other services.

A critical aspect for the factories of the future is to provide an ecosystem that relies in data

for better decision making process, as supported by this FI-WARE chapter.

The Data/Context Management chapter, instead, helps in managing the amount of

information that needs to be exchanged across the factories with the involvement of different

entities, and in orchestrating the actions and transfers in order to keep the information easily

available for any data consumer service, without requiring that information consumers be

aware of the specifics of the producer such as the locations or the transfer protocol.

3.5.5. The FITMAN Virtual Factory GEs/SEs

As for the previous sections, the Fig. 3-17 sketches the envisaged GEs and SEs for the

FITMAN Virtual Factory Reference Architecture. Starting from the top, the identified

enablers are:

VF Open Call Topic II SE (Seman. Data Interop.): this SE is part of the FITMAN

Open Call for Virtual Factory and, specifically, is specified as the 2nd

topic of the

Virtual Factory section in the call. The functionalities envisaged for this SE are to

support “… semantic interoperability (Product‐Service Manufacturing Ecosystem),

including platforms and software components for dynamic, semantic data formats

transformations (e.g. unified interoperability form by means of a common model

schema), in the view to achieve ERP (and other Enterprise Systems) compatibility in

the supply chain”. This SE is therefore a key element for the provision of the

functionalities envisaged for the FITMAN Virtual Factory Interoperability Services

architectural component to support interoperability among legacy systems, and across

enterprises, and the design and deployment of cross-enterprise collaborative

processes;

Mediator GE: this is the same FI-WARE GE39

included in the Digital Factory

reference architecture (see Fig. 3-16) but with a slightly different objective. In the

Virtual Factory reference architecture, indeed, it will not be finalised to simply

provide mediation services among different data structures (and data access

mechanisms) as envisaged within the Digital Factory case, but as a key element to

support integration and interoperability among enterprise applications (e.g., ERPs,

CRMs, etc.) for cross-enterprise processes. In the Virtual Factory scenario, therefore,

the GE’s ESB (Enterprise Service Bus) and the Enterprise Integration Patterns [52]

[53] features will be fully exploited;

SEI_8 Data Interop. Platform Services SE: this FITMAN specific enabler40

completes the functionalities envisaged for the Virtual Factory Interoperability

Services architectural component. It specifically provides features for the

39 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Apps.Mediator 40 http://catalogue.fitman.atosresearch.eu/enablers/data-interoperability-platform-services/documentation




management of Data Interoperability services (including services to support end-

users in designing and configuring them). This SE is based on outcomes of the FP7

COIN IP project41

and, specifically, it extends the COIN Integrated Platform;

Semantic App. Support GE: this FI-WARE GE42

is one of the elements providing the

services required by the FITMAN Semantic Application Support Features

architectural component and, specifically, it will provide the RDF/OWL aware

repository, ontology management features and ontology management;

Light Semantic Composition GE: this FI-WARE GE43

will provide support for

creating and managing business processing where semantics supports identification,

selection and integration of services and control elements in the business processes.

Indeed, the actual definition of a business process is normally not an easy activity,

requiring the contribution of different skills and knowledge of the available

application services (both in terms of their specific functionalities, as well as in terms

of their interfaces, supported data, etc.) This GE, therefore, aims at simplifying these

activities, and the type and number of required skills, thanks to capability to use

knowledge about application services formalized according to ad hoc ontologies (for

example giving the possibility to identify and select application services on the basis

of their provided, ontologically specified, properties, and not on more complex

service descriptions like WSDL);

SEI_6 Metadata, Ontol. Semantic Matching SE: the FITMAN Metadata and

Ontologies Semantic Matching SE44

completes the set of features envisaged for the

Virtual Factory Semantic Application Support Features architectural component

providing features to semi-automatically matching different OWL ontologies, or

XML schema definitions, addressing similar contexts of knowledge (e.g., XML

schemas for business documents defined in different standards like OASIS UBL45

or

UN/CEFACT CCL46

). This FITMAN SE provides both functionalities for nosiness

process designers to manage maps among different, even similar, ontologies or

document schemas, as well as run-time features to convert knowledge or information

from one formalization to a different one;

SEI_7 Collaboration Platf., BP Management SE: the FITMAN Collaborative

Business Process Management SE47

provides the functionalities envisaged for the

Virtual Factory Collaborative BP Management Features architectural component

and, specifically, the ones required to design, execute and monitor semantically

enhanced BPMN 2.0 business processes. This SE is actually an extension of the FI-

WARE Light Semantic Composition GE;

Identity Mgmt GE: this FI-WARE GE48

has to provide the subjects (e.g., users and

services) authentication functionalities that are critical for cross-enterprise contexts

which are at the core of the Virtual Factory domain;

Re. II Store & Registry GEs: this couple of GEs, from the FI-WARE Release II

delivery, have to jointly support the provision of the FITMAN Virtual Factory

Marketplace Management Features architectural component’s features. The FI-

41 http://www.coin-ip.eu/ 42 http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.Data.SemanticSupport 43 https://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Apps.LightSemanticComposition 44 http://catalogue.fitman.atosresearch.eu/enablers/metadata-and-ontologies-semantic-matching/documentation 45 OASIS Universal Business Language (UBL). See https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=ubl 46 UN/CEFACT) Core Components Library (CCL) http://www.unece.org/cefact/codesfortrade/unccl/ccl_index.html 47 http://catalogue.fitman.atosresearch.eu/enablers/collaborative-business-process-management/documentation 48 https://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Security.IdentityManagement




WARE Store GE49

provides services for managing offerings and sales of services

(e.g., publication of service offerings, payments, access to purchased services, etc.).

The Registry GE50

, on the other hand, provides directory features to store information

used for the maintenance, administration, deployment and retrieval of services.

Together these two GEs provide support to manage offered and requested services

and on their availability, location, software elements, etc.;

Marketplace GE: this GE51

completes the FITMAN Virtual Factory Marketplace

Management Features architectural component providing a uniform interface to

discover offered application and services that match consumer demands so to help in

navigating through the multitude of apps and services that is envisaged on the Future

Internet;

VF Open Call Topic I SE (TA/TI Semant. Cluster.): this SE is part of the FITMAN

Open Call for Virtual Factory and, specifically, is specified as the 1st topic of the

Virtual Factory section in the call. This SE aims at covering the lack of

functionalities to support semantically based assets discovery, to extend business‐oriented service description languages (e.g., USDL and LinkedUSDL), to cluster and

generate new assets from unstructured and semi‐structured enterprise resources (e.g.,

CVs, products catalogues, etc.) and their dynamic composition;

SEI_5 Supply Chain & Business Ecos. Apps SE: this FITMAN SE52

provides features

to exploit tangible (e.g., machinery capacity) and intangible (e.g., people skills)

services allowing to combine a set of communicating gadgets and support end-users

in improving cross-enterprise r value-networks collaboration in their Production

Capacity Planning or team building activities;

Repository GE: this FI-WARE GE53

is the main provider of the storage descriptions

for services and assets. The information managed by this GE is mainly structured

using USDL and LinkedUSDL specifications so to take into account all aspects of the

managed elements (e.g., API description, business conditions, etc.);

SEI_4 Collaborative Assets Management SE: this FITMAN SE54

provides support for

the collaborative management of assets (Asset-as-a-Service - AAS). It is targeted to

the business users having no, or very low, IT expertise. The elements (i.e., assets)

managed via this SE, represent any item of economic value for an enterprise like

resources (e.g., machinery, buildings, vehicles, etc.) and capabilities (e.g.,

knowledge, competencies, relationships, etc.) that is functional to achieve the goals

of the Enterprise.

49 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.Apps.Store 50 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.Apps.Registry 51 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.Apps.Marketplace 52 http://catalogue.fitman.atosresearch.eu/enablers/supply-chain-business-ecosystem-apps/documentation 53 http://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.Apps.Repository 54 http://catalogue.fitman.atosresearch.eu/enablers/collaborative-asset-management/documentation




Fig. 3-17: The FITMAN Virtual Factory Reference Architecture and GEs / SEs

In summary, the above briefly described Virtual Factory GEs/SEs provide features that can

be categorised as follows:

Support to enterprise interoperability and collaboration through the provision of

advanced services for setting up and execute cross-enterprise business processes.

Services that, thanks to the data and knowledge transformation features can easily

use, and reuse, the required data across the enterprise boundaries;

Support to the transparent management of enterprise tangible and intangible assets as

required to support the cross-enterprise cooperation managed via the business process

highlighted in the previous bullet.




Due to the wide need to integrate data sources, enterprise systems (e.g., SCM, CRM, ERP,

etc.) and people the Virtual Factory reference architecture instantiations (e.g., FITMAN

Virtual Factory trials) are envisaged as requiring many Trial Specific Components (TSC) as

compared to other factory domains.

3.5.6. The FI-WARE Cloud Hosting GEs in FITMAN

As already shortly reported in section 3.5.1, although the FI-WARE Cloud Chapter does not

directly contribute “application functionalities” to, and therefore have an explicit positioning

within, the FITMAN Reference Architecture, nevertheless it plays a significant role in

meeting requirements of flexibility, dynamic deployment and management of applications

and services as envisaged for the near manufacturing future environments [2][3][4][5][6].

In the following we report the rationale behind the selection of the FI-WARE Cloud Chapter

GEs, as well as the expected role they will play in the deployment of FITMAN compliant

manufacturing solutions.

As highlighted in section 2.1, there are contrasting requirements affecting the deployment of

cloud computing based solutions in manufacturing. Indeed, on the one hand, there is the need

to deploy solutions able to scale, be provided on-demand, support cross-enterprises contexts,

etc.; on the other hand, there is the need of assuring manufacturing solutions provide

information protection, liability, time constraints, etc.. The former set of needs of course

plays in favour of cloud based deployments, while the latter raises some concerns on the

usage of currently available cloud computing public services (e.g., Amazon AWS, Microsoft

Azure). In the near future we expect to have cloud and connectivity services that provides

SLAs and contractual terms that meet the manufacturing constraints and, therefore, an

increasing usage of public or community cloud services [54].

To overcome the above issues, most of the FITMAN trials that plan to deploy their FITMAN

compliant manufacturing systems on a cloud platform will actually use on premises cloud

infrastructures. This by no means affects the value of the FITMAN FI-WARE GEs cloud

evaluation; on the contrary this approach can help both in fulfilling the constraints mentioned

above, as well as in assessing in real contexts the cloud technologies, therefore fostering

manufacturers awareness about cloud services and their more conscious evaluation of third

parties contracts and SLAs for cloud services.

The selected FI-WARE Cloud Chapter GEs have the objective to provide specialised, added

value services as compared to the IaaS services cloud platforms like OpenStack55

, which is

the foundation of the FI-WARE cloud computing infrastructure, provide.

The selected GEs are the following:

Cloud.DCRM - IaaS Data Center Resource Management GE: The DCRM GE56

is

one of the most fundamental added value elements of the FI-WARE cloud computing

facilities being devoted to improve the management of the Virtual Machines (VMs),

and their resources, lifecycle. This DCRM GE extends the OpenStack baseline

functionalities;

Cloud.SM - IaaS Service Management: the IaaS SM GE57

is a key enabler for the

lifecycle management of virtual infrastructures required by applications, for example

scaling vertically or horizontally the virtual servers allocated to an application

according to a pre-defined set of rules. The flexibility provided by the IaaS SM GE

enhance the usability and flexibility of FI-WARE compliant platforms, improving the

usage of resources, reducing deployment/reconfiguration time, automating the

55 http://www.openstack.org 56 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Cloud.DCRM 57 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.ArchitectureDescription.Cloud.SM




management of failure tasks and supporting the integration of public and private

cloud infrastructures;

Cloud.ObjectStorage - Object Storage: the CDMI GE58

exposes a CDMI59

RESTful

API to manage object storage (e.g., binary or textual objects, hierarchical containers,

etc.) therefore supporting both infrastructure GEs and applications to organize and

manage their persistent data;

Cloud.SelfServiceInterfaces - Cloud Portal: this GE60

enables end-users and platform

administrators to manage their services and resources. This GE offers both a

graphical dashboard, accessible via a web portal that is part of the GE, as well as a

command line interface through which users can manage and supervise their services

and reosurces deployed on the cloud infrastructure.

As evident from the above paragraphs the FITMAN selection of FI-WARE Cloud Chapter’s

GEs is focused on enablers that can easy the on premise deployment and management of

cloud based solutions, as well as on FI-PPP provided infrastructures.

One of the expected benefits of the usage of the Cloud Chapter GEs in the FITMNA trials is

to gain actual experience on the performances, usability and suitability of cloud solutions in

manufacturing so to foster the deployment of cloud based solutions.

58 https://forge.fi-ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Cloud.ObjectStorage 59 http://www.snia.org/cdmi 60 https://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FIWARE.OpenSpecification.Cloud.SelfServiceInterfaces




4. CONCLUSIONS AND NEXT STEPS

The previous sections have tried to characterize the evolution of the manufacturing sector

from highlighting the main envisaged challenges (see section 2.1.1) and issues (see section

2.1.2). Afterwards we tried to better characterize different manufacturing domains (i.e., the

Smart, Digital and Virtual Factory ones) as envisaged by the EFFRA organization [5], and

we provided a quick summary of the FITMAN trials so to better characterize the specific

contexts the FITMAN architecture has to address.

Sections 3.1-3.4 provide the rationale behind the choice to define reference architectures for

the three manufacturing domains and a detailed description of each of reference architecture.

Finally, section 3.5 analyzes how the selected FI-WARE GEs and FITMAN SEs contribute

to, and position within, the three reference architectures.

The FITMAN plan does not envisage new versions of this deliverable, being the rationale

behind the design of the three reference architectures well founded and in line with the

manufactured trends as reported in section 2.1. Therefore, even if the selection of FI-WARE

GEs could be revised, or the FITMAN SEs could be refined, the overall design will

hopefully not be affected. Changes in GEs selection or SEs specifications will be addressed

and reported in other FITMAN deliverables.




5. REFERENCES

[1] EFFRA Ad-hoc Industrial Advisory Group, “Factories of The Future PPP

Strategic Multi-Annual Roadmap”, March 2010

[2] P. C. Evans and M. Annunziata, “Industrial Internet: Pushing the Boundaries of

Minds and Machines”, November 2012

[3] “Securing the future of German manufacturing industry - Recommendations for

implementing the strategic initiative INDUSTRIE 4.0 - Final report of the

Industrie 4.0 Working Group”, April 2013

[4] O. Lazaro et. al., “Embarking on New Orientations Towards Horizon 2020”,

Future Internet Enterprise System (FInES) position paper, June 2013

[5] EFFRA, “Factories of the Future PPP FoF 2020 Roadmap – Consultation

Document”, July 2012

[6] M. Taisch, A. Majumdar, “ICT for Manufacturing - The ActionPlanT Roadmap

for Manufacturing 2.0”, July 2012

[7] Y. Monden, “Toyota Production System: An Integrated Approach to Just-In-

Time”, Productivity Press. 2011 (ISBN 1-4398-2097-X)

[8] Z. A. Banaszak, B. H. Krogh, “Deadlock avoidance in flexible manufacturing

systems with concurrently competing process flows”, IEEE Trans. on Robotics

and Automation, vol. 6, no. 6, pp. 724-734, December 1990

[9] J. Park, S. A. Reveliotis, D. A. Bodner, L. F. Mcginnis, “A distributed, event-

driven control architecture for flexibly automated manufacturing systems”,

International Journal of Computer Integrated Manufacturing, vol. 15, no. 2, 2002

[10] G. Hohpe, “Programming Without a Call Stack – Event-driven Architectures”,

2006. Available: http://eaipatterns.com/docs/EDA.pdf

[11] M. Grauer, S. Karadgi, D. Metz, W. Schäfer, “Online Monitoring and Control of

Enterprise Processes in Manufacturing Based on an Event-Driven Architecture”,

in “Business Process Management Workshops”, pp 671-682, Springer Berlin

Heidelberg, 2011 (ISBN 978-3-642-20510-1)

[12] L. Monostori, J. Váncza, S. R. T. Kumara, “Agent-Based Systems for

Manufacturing”, CIRP Annals – Manufacturing Technology, vol. 55, pp. 697-720,

2006

[13] V. Marik, J. Lazansky, “Industrial applications of agent technologies”, Control

Engineering Practice, Special Issue on Manufacturing Plant Control: Challenges

and Issues, vol. 15, pp. 1364-1380, 2007

[14] I. M. Delamer, J. L. M. Lastra, “Service-Oriented Architecture for Distributed

Publish/Subscribe Middleware in Electronics Production”, IEEE Transactions on

Industrial Informatics, vol.2, no.4, pp.281-294, Nov. 2006

[15] C. Popescu, “Approach to Incremental Modelling of Web Services Orchestration -

An Application to Deadlock-free Scheduling in Automated Systems”, Tampere

University of Technology, Publication 832, 2009

[16] A. W. Colombo, “SOCRADES: Steps Towards the Factory of the Future”,

Projects Magazine EU, edition 12, pp. 20-23. British Publishers Inc. 2009

[17] J. Kaiser, C. Mitidieri, C. Brudna, C. E. Pereira, “COSMIC: A middleware for

event-based interaction”, in Proc. 9th IEEE International Conference on Emerging

Technologies and Factory Automation (ETFA 2003), vol. 2, pp. 669-676, Sept.

2003

[18] T. Kirkham, D. Savio, H. Smit, R. Harrison, R. P. Monfared, P.

Phaithoonbuathong, "SOA middleware and automation: Services, applications and

architectures", in Proc. 6th IEEE International Conference on Industrial

Informatics (INDIN 2008), pp. 1419-1424, July 2008




[19] M. Marinov, N. Magaletti, T. Pavlov, F. Gaus, D. Rotondi, P. Vitliemov, S.

Ivanova, “An Approach to Designing Distributed Knowledge-based Software

Platform for Injection Mould Industry”, WSEAS TRANSACTIONS on

INFORMATION SCIENCE and APPLICATIONS, issue 11, vol. 7, Nov. 2010

[20] See also the EU FP7 FoFdation project (http://www.fofdation-project.eu) that is

designing a data integration standard that allows individual entities and their

associated devices to share data in a common format

[21] GS1 EPC Global, “EPCglobal Tag Data Standards Version 1.3.1”, March 2006

[22] GS1 EPC Global, “The Application Level Events (ALE) Specification, Version

1.1 Part I: Core Specification”, February 2008

[23] GS1 EPC Global, “The Application Level Events (ALE) Specification, Version

1.1.1 Part I: Core Specification”, March 2009

[24] ISO, “ISO/IEC 15961:2004: Information technology - Radio frequency

identification (RFID) for item management -- Data protocol: application interface”

and its 2013 revision

[25] ISO, “ISO/IEC 15962:2004: Information technology - Radio frequency

identification (RFID) for item management -- Data protocol: data encoding rules

and logical memory functions”, and its 2013 revision

[26] OPC Foundation, “OPC Unified Architecture Specification Part 1: Overview and

Concepts Release 1.02”, July 2012

[27] http://www.opcfoundation.org/Default.aspx/01_about/01_history.asp?MID=About

OPC

[28] The Association For Manufacturing Technology, “MTConnect Standard Part 1 -

Overview and Protocol - Version 1.2.0 – Final”, February 2012

[29] K. Walzer, J. Rode, D. Wunsch, M. Groch, “Eventdriven manufacturing: Unified

management of primitive and complex events for manufacturing monitoring and

control”, IEEE International Workshop on Factory Communication Systems,

WFCS 2008, May 2008, pp. 383-391

[30] D. Garlan, M. Shaw, “An Introduction to Software Architecture”, Advances in

Software Engineering and Knowledge Engineering, Volume I, edited by V.

Ambriola and G. Tortora, World Scientific Publishing Company, New Jersey,

1993

[31] G. Muller, "A Reference Architecture Primer", March 2013

(http://www.gaudisite.nl/ReferenceArchitecturePrimerPaper.pdf)

[32] A. Bassi, M. Bauer, M. Fiedler, T. Kramp, R. Kranenburg, S. Lange, S. Meissner

(Eds.), “Designing IoT solutions with the IoT Architectural Reference Model”,

Springer Heidelberg New York Dordrecht London, 2013 (DOI 10.1007/978-3-

642-40403-0)

[33] R. Seacord, L. Wrage, "Replaceable Components and the Service Provider

Interface" (CMU/SEI-2002-TN-009). Pittsburgh, PA: Software Engineering

Institute, Carnegie Mellon University, 2002

[34] A. Kumar, T. M. Sebastian, “Sentiment analysis. A perspective on its past present

and future”, International Journal of Intelligent Systems and Applications, 4

(10):1-14, 2012

[35] S. Morrison, F. Crane, “Building the Service Brand by Creating and Managing an

Emotional Brand Experience”, Journal of Brand Management, 14 (5): 410-421,

2007

[36] V. Allee, “The Future of Knowledge: Increasing Prosperity through Value

Networks”, Butterworth-Heinemann 2003

[37] V. Allee, “Value Network Analysis and value conversion of tangible and

intangible assets”, Journal of Intellectual Capital, Volume 9, No. 1, pp. 5-24, 2008




[38] V. Allee, O. Schwabe, “Value Networks and the true nature of collaboration”,

Published by ValueNet Works and Verna Allee Associates, ISBN# 978-615-

43765-1, 2011

[39] C. B. Stabell, Ø. D. Fjeldstad, “Configuring value for competitive advantage: on

chains, shops, and networks”, Strategic Mgmt. J., 19: 413–437, 1998

[40] R. Adner, “The Wide Lens: A New Strategy for Innovation”, Penguin Group,

ISBN 978-1-59184-460-0, 2012

[41] M. Iansiti, R. Levien, “Strategy as ecology”, Harvard Business Review, pag. 68-

78, 2004

[42] S. Muegge, “Platforms, Communities, and Business Ecosystems: Lessons Learned

about Technology Entrepreneurship in an Interconnected World”, Technology

Innovation Management Review, 2013

[43] A. Barros, D. Oberle (Eds.), “Handbook of Service Description - USDL and Its

Methods”, Springer, 2012, XXV

[44] D. Oberlea, A. Barrosb, U. Kylauc, S. Heinzld, “A unified description language

for human to automated services”, Elsevier Information Systems, Volume 38,

Issue 1, March 2013, Pages 155–181

[45] R. K. L. Ko, S. S. G. Lee, E. Wah Lee, “Business Process Management (BPM)

Standards: A Survey”, in Business Process Management Journal, Emerald Group

Publishing Limited. Volume 15 Issue 5, 2009 (ISSN 1463-7154)

[46] See the “Overall FI-WARE Vision” document (http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/Overall_FI-WARE_Vision)

[47] FITMAN Consortium, “D1.3 - FI-WARE Generic Enablers Final Selection for

FITMAN”, July 2013

[48] FITMAN Consortium, “D1.2 - FITMAN Trials IT Requirements – Version 1.0”,

September 2013

[49] See the “FI-WARE Architecture” document (http://forge.fi-

ware.eu/plugins/mediawiki/wiki/fiware/index.php/FI-WARE_Architecture)

[50] EPCglobal Inc., “The Application Level Events (ALE) Specification, Version 1.1

Part I: Core Specification”, February 2008

[51] EPCglobal Inc., “Low Level Reader Protocol (LLRP) Version 1.1”, October 2010

[52] G. Hohpe, B. Woolf, “Enterprise Integration Patterns: Designing, Building, and

Deploying Messaging Solutions”, AddisonWesley, 2003

[53] O. Etzion, P. Niblett, “Event Processing in Action”, Manning Publications Co.,

2011

[54] P. Mell, T. Grance, “The NIST Definition of Cloud Computing”, NIST Special

Publication 800-145, September 2011

[55] FITMAN Consortium, “D1.1 - FITMAN Use Case Scenarios and Business

Requirements”, August 2013

d1.5 fitman reference architecture - cordis...project id 604674 fitman – future internet...

Documents