An Enterprise Architecture-centred Approach towards

Eco-Industrial Networking: A Case Study

Ovidiu Noran

and Aurelia Noran

IIIS Centre for Enterprise Architecture Research and Management, Griffith University, Brisbane, Australia

Proteeum Pty Ltd, Brisbane, Australia

Keywords: Circular Economy, Industrial Ecology, Enterprise Architecture, Systems Theory, System of Systems,

Collaborative Networks, Green Virtual Enterprise, Eco-Industrial Networking.

Abstract: Circular Economy is one of the main avenues to tackle the ever-increasing effects of what is becoming the

most urgent challenge of our times: climate change. Previous work has advocated a multidisciplinary

approach towards the optimal enactment of Circular Economy through Eco-Industrial Networking, due to its

many-faceted and complex aspects. This paper aims to further the research in the area by examining the

practical application of the concepts proposed within a case study and drawing conclusions on applicability,

potential pitfalls and improvements, while at the same time advocating a more Enterprise Architecture (EA)-

centric stance due to its all-encompassing and integrating nature. Thus, a brief explanation of the theoretical

background is followed by the description of the scenario and the proposed EA-focused concepts’ application

in practice, including challenges and benefits of the chosen approach. Finally, a reflection is performed and

conclusions are drawn together with suggestions for future applications and development of the method.

1 INTRODUCTION

The increasingly ubiquitous impact of climate

change, biodiversity loss, waste, and pollution have

brought forward the need for urgent and meaningful

action (Manisalidis et al., 2020). Among the possible

avenues for tackling these problems, the circular

economy (CE) (Brennan et al., 2015) initiative

features prominently due to its potential to address the

root causes of the above-mentioned challenges, by

aiming to share, lease, reuse, repair, refurbish and

recycle existing materials and products for as long as

possible (European Parliament, 2015). As shown in

previous work (Halog et al., 2021; Romero & Noran,

2015), CE can be more effectively enacted in a more

structured collaborative environment by adopting the

Eco-Industrial Networking (EIN) approach (Yedla &

Park, 2017), which in turn builds on Environmental

Management concepts applied to proven

Collaborative Networking (CN) paradigms

(Camarinha-Matos et al., 2009). In order to deal with

complexity and emergent behaviour aspects that

typically manifest themselves in the EIN endeavour,

https://orcid.org/0000-0002-2135-8533

https://orcid.org/0000-0003-2865-886X

some authors (Haskins, 2006; Noran & Romero,

2014; Patala et al., 2014) have proposed adopting a

multidisciplinary stance that provides a holistic

approach considering all aspects relevant to the

specific EIN endeavour.

This paper describes a practical EIN case study

which has been tackled using the above-described

multidisciplinary paradigm, while advocating more

emphasis on using the Enterprise Architecture

artefacts as an encompassing concept for the other

knowledge areas involved. The application and

benefits of the approach are illustrated in practice,

while at the same time observing challenges

encountered and lessons learned towards the

improvement of the proposed methodology. The rest

of the paper is set out as follows: Section 2 briefly

describes the theoretical background outlining the

contributions of the various knowledge areas. Section

3 describes the case study itself including the setting,

building the necessary artefacts, modelling them

using the proposed constructs and contrasting with

the legacy approach, followed by reflecting on the

results, challenges and possible future development.

Noran, O. and Noran, A.

An Enterprise Architecture-centred Approach towards Eco-Industrial Networking: A Case Study.

DOI: 10.5220/0010939100003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 455-464

ISBN: 978-989-758-569-2; ISSN: 2184-4992

455

Finally, the paper closes with conclusions and

potential further work.

2 THEORETICAL

BACKGROUND

Circular Economy and its specific industrial

application, Industrial Ecology (IE), represents the

paradigm guiding the formation and operation of the

EIN. IE advocates the shift from a linear progression

of the resources through the system towards

eventually becoming waste, to an integrated circular

‘industrial eco-system’ optimising and redesigning

all relevant resource flows (beyond just physical, e.g.

also including information and knowledge) to up- and

down-cycle them. By becoming inputs for new

processes, such resources are fully utilised and thus

contribute to reducing the reliance on non-renewable

natural resources (International Society for Industrial

Ecology (IS4IE), 2013).

Several viewpoints are proposed in the analysis

and development of IE, as follows: 1) technical

(technically feasible stream exchanges); 2) economic

(economically sound exchanges); 3) political /

regulatory / legal (environmental laws and

regulations); 4) informational (need of data and

information for decision-making management) and 5)

organisational / institutional (readiness for

environmental collaboration).

The practical manifestation of Industrial Ecology,

namely Industrial Eco-systems (IES), typically

feature a plethora of autonomous entities that in turn

possess interrelated, interdependent and/or

interactive components within their ‘techno-sphere’

(man-made technological systems) and their

‘biosphere’ (natural ecosystems) (Romero & Molina,

2012); therefore, IES are in fact complex cyber-

physical systems (CPS).

For the reasons above, a complete EIN analysis

comprising all necessary aspects needs to also resort

to the Systems Engineering and Systems Theory.

Adopting this knowledge area will provide EIN

designers with the concepts of homeostasis,

adaptation and feedback loops which may manifest

themselves in a tendency towards an equilibrium state

and self-evolving behaviour in response to various

stimuli (as shown in Section 3 to also be applicable in

this particular case study).

Due to the complexity involved, an adequate

analysis of EIN needs to consider each participating

entity as a ‘System of Systems’ (SoS) (Haskins, 2007;

Mennenga et al., 2019), i.e. a collection of systems

that contribute their resources and capabilities to

create a more complex system, featuring more

functionality and performance compared to the mere

sum of the constituents. Thus, SoS are considered to

be ‘meta-systems’ composed of several other

independent complex systems varying in

“technology, context, operation and conceptual

frame” (Keating et al., 2003). Maier’s (1998) work is

adding to the above two more SoS features, namely

operational and managerial independence and

emergent behaviour. EIN scalability, potential

agenthood of network participants and stakeholder

conflict mitigation is also supported by the SoS

paradigm (DeLaurentis & Ayyalasomayajula, 2009)

The capability to promptly bid for and win large

grants and/or projects requires an adequate set of

competencies and resources; often, this is beyond the

capabilities of most companies taken in isolation.

Therefore, companies often form ’alliances’ taking

forms of Collaborative Networks (CNs) such as

Breeding Environments (BE) which are set up so as

to achieve the required preparedness to promptly

create Virtual Enterprises (VEs) able to bid for

projects as described above (Afsarmanesh &

Camarinha-Matos, 2006). Typically, CNs feature one

or more lead partners which orchestrate the BE and

VE efforts and one or more brokers whose role is to

identify and acquire new collaboration opportunities

and negotiate with potential participants (Camarinha-

Matos et al., 2005). If successful in bidding, the VEs

then build and manage projects which can also use

resources outside the CN.

Moving on to the next knowledge area, namely

Enterprise Architecture (EA), Gartner Research

(2012) sees it as representing a holistic change

management paradigm connecting management and

engineering best-practice, providing requirements,

principles and models that describe future state of the

enterprise. This paradigm includes humans,

processes, information and technology within the

enterprise, together with their internal and external

relationships; according to the definition above, it can

be considered that EA represents in fact the ‘ontology

of change’. Typically, the above viewpoints are

structured in EA frameworks (EAF), several of which

are currently in use. In order to ensure maximum

potential relevance and coverage to the problem at

hand, this paper adopts a framework that represents

and generalises several mainstream EAFs, namely the

Generalised Enterprise Reference Architecture and

methodology (GERAM), described in Annex A of the

ISO15704 Standard (ISO/IEC, 2005). This EAF

includes a reference architecture (GERA) which in

turn features a modelling framework (MF) integrating

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aspects deemed of the most importance for the EIN

problem at hand. Importantly, the MF includes the

concept of life cycle which is absent from the

majority of other approaches (see e.g. Clark et

al.(2015), Nwokeji et al.(2017) ,etc.) and can be used

as a background to all the other viewpoints, as shown

in Figure 1(vertical axis). The proposed modelling

framework provides guidance in relation to the

expressiveness of the modelling languages to be used

rather than enforcing specific choices.

Figure 1: Enterprise Architecture Modelling Framework.

As one can see from the figure, this construct not only

subsumes the viewpoints of the other disciplines

relevant to EIN, but it also integrates them in a

multidimensional framework. This is important as it

enables multi-pronged analyses in a cohesive manner

while also allowing to examine selected viewpoints

separately, as shown in Figure 2 and further in

Section 3. For the reasons above, it is hereby argued

that EA should be the prevailing knowledge area

involved in this case study analysis.

Thus, one may define the roles of the various

disciplines involved in this case study as follows: CE

is the defining paradigm, SoS and CN are the

structural concepts while EA is the integrated

overarching and multidimensional ontology of the

entire endeavour.

Note that the proposed MF abstracts from time,

although the concept does exist in the Reference

Architecture GERA in the form of life history. This

aspect will be used in Section 3.4.

Figure 2: Deriving the modelling constructs used to analyse

specific viewpoints in the case study.

3 THE CASE STUDY

3.1 Setting

A large company and developer aimed to diversify its

investments by creating an industrial precinct; in the

context of the current climate change challenges and

global competition, the decision was taken to adopt a

recycling-based strategy, so as to minimize waste and

pollution and realise savings on raw materials. The

overall strategy was to attract various participants to

the precinct and then get selected members to form a

recycling-based environment based on their potential

and willingness to connect their relevant inputs and

outputs. However, there was a need for guidance as

to how to select these partners in the initial and

continuing stages. Furthermore, there was a need to

define the kind of association these partners were to

enter and furthermore the various ways they could

connect in order to realise the circularity concept –

more specifically, the various configurations the

partner association would create.

In view of the above narrative, it became apparent

that the theoretical background presented in Section 2

could be used to provide guidance towards the set-up

and operation of the envisaged network.

3.2 Building the Breeding Environment

The above situation matches a combined CE and CN

paradigm, namely the closed-loop logistics and

integrating forward and reverse supply chains

described by various authors (Meade et al., 2007;

Romero & Molina, 2012; Srivastava, 2007). In the

Management

and Control

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An Enterprise Architecture-centred Approach towards Eco-Industrial Networking: A Case Study

457

particular case described, in view of the geographical

co-location of the participants, the specific type of

EIN envisaged was the symbiosis network depicted

by Patala et al.’s (2014) work.

From the point of view of the CN knowledge area,

the situation is relevant to the formation of a ‘Green’

Breeding Environment (GBE)(Romero & Molina,

2012), a pool of potential participants which are pre-

qualified (i.e. all the cooperation protocols and

contracts are pre-established and negotiated) and thus

achieve the necessary preparedness to promptly

participate in various joint ventures (‘Green’ Virtual

Enterprises, or GVEs) as required to bid for- and win

project opportunities and / or grants. The initial ‘lead

partner’ in this case was the large company and

developer; subsequently, other major participants

followed: a telecommunications company ensuring

the essential infrastructure (data centre and ‘lake’)

and a tertiary education institution contributing the

necessary research and innovation resources. The

selection of these lead partners has been a result of

applying the other knowledge areas towards

achieving suitable emergent properties for the partner

associations (SoS area) and of investigating the

relevant areas that needed to be addressed by

potential partners (using the EA Modelling

Framework viewpoints, namely Information and

Resource).

Further on, several other participants have been

identified and qualified as GBE members using the

same multi-disciplinary approach:

- Green energy producer

- Plant-based food manufacturer

- Waste handling facility

- Research facility

- Telecommunications company

- Agricultural equipment dealer

- Plastic pipe manufacturer

- Building products manufacturer

- Enviro-fibre packaging manufacturer

- Plastic decontamination/Protein Manufacturer

- Industrial / food grade gases manufacturer and

liquefier

- Hydrogen industrial gas manufacturer

The qualification process was based on several

criteria, such as shown below.

A first qualifying condition was the suitability

(initial preparedness) to participate in the Circular

Economy-based GBE, i.e. whether the aspiring

participants had any existing or potential inputs or

outputs that could connect with other GBE members.

This initial analysis has involved using the IE-

inspired technical and organisational domains (see

Section 2) encompassed and complemented by the

EA Function, Information, Resource and

Organisation (FIRO) MF viewpoints.

A further qualifying criterion for BE membership

from the point of view of SoS has been the

commitment to build up adequate connectivity

preparedness by developing and maintaining a

‘digital twin’ (Negri, 2017) for the potential input /

output connections that could develop between the

GBE members. The concept of digital twin has been

used before in EIN (Rojek et al., 2021); in this case

study, it was necessary in order to start quantifying

the inputs and outputs of the various exchange

streams participants (previously not performed as not

being deemed necessary). Measuring the amount of

matter, information and energy involved in the

streams was paramount to their proper setup and

operation (also involving trading) that underpinned

the entire Eco-Industrial Network paradigm.

Table 1: Specific CN situation of the case study.

To summarize, from the point of view of the CN

discipline, the situation looks as shown in Table 1:

EA MF viewpoints have been used to scope out

streams, e.g. using functional and informational

criteria to determine what exchange activities must be

modelled and what data is needed to describe relevant

properties of the exchanges, respectively.

3.3 Building the Virtual Enterprise/s

The lead partners within the GBE have decided to

allocate resources to a workgroup whose tasks are to

create input / output streams connecting GBE

members and also to apply for government and

private funding supporting EIN endeavours.

According to the CN body of knowledge, a suitable

materialisation of such a workgroup is a VE;

however, in this case study, this entity is expected to

have a lasting presence and take on other tasks which

are not assigned to it in the typical CN realm.

Initially, a representation such as the one shown

in Fig. 3 has been used. To start with, a diagram is

required for each stream to preserve readability. This

means that showing potential relations between

streams is very difficult and requires shuffling

between diagrams which is very awkward for

CN Concept Case Study Materialisation

Breeding Environment Qualified Network participants

Virtual Enterprise Lead partner, telco, education

Broker Green Virtual Enterprise

Streams Exchange connections

Reference model Interface templates

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Figure 3: Initial EIN Models including the BE, VE and the Streams.

stakeholders. In addition, as one can see from the

figure, many essential aspects, such as life cycle of

the BE participants, VE and the streams and the

potential relations between them cannot be

meaningfully represented.

According to the concepts described in the

theoretical background section and satisfactory

previous life cycle-based endeavours in the area of

environmental management (Lewis & Demmers,

1996; Noran, 2010), it is proposed in this paper to

represent the situation using modelling constructs

proposed in Section 2 and analyse the results.

Figure 4 shows the same situation using the EA

modelling construct derived as shown in Figure 2; as

can be seen, this construct allows a richer

representation taking into account the life cycles of

the participants and management/ production (or

service) aspects. Note that not all aspects need to be

represented for all participating entities; for example,

in Fig. 4 some entities are only represented through

their Production side of the Operation life cycle phase

(see for example potential BE members or CN

members) since only that area is relevant to the model

in question.

The arrows between entities represent the role

played by the originating entity in the life cycle/s of

the destination entity. Note that some arrows

originate from- and point to the same entity (e.g. in

the case of the VE and the streams S1 and S2 in fig.

Figure 4); this is to indicate the capacity of the entity

to change itself to a certain extent (adaptation) as

further explained. Various line types may also be used

as illustrated in the same figure, in order to better

represent possible scenarios or behaviours.

Should specific aspects be required to be

represented, various viewpoints can be selected from

the MF shown in Figure 1. For example, if the

contents of the streams need to be shown, the

Information viewpoint may be selected and an

appropriate language such as Entity Relationship- or

UML Class Diagrams can be used; if the flow of data

or sequence of activities within a stream need to be

represented for analysis and costing, the Function

viewpoint may be selected and a view created using a

language such as IDEF0 or UML Activity Diagram

may be used. While one can use such languages

independently of employing an (EA) MF, the

essential advantage when using such an artefact is

that all the viewpoints represented are linked via the

framework through a common meta-model and as

such, the consistency of the complete model can be

intrinsically maintained.

The following gives a brief explanation of certain

aspects of the dynamic business model of the case

study represented in Fig. 4.

Thus, one can see the role of governmental

agencies such as the Clean Energy Regulator

(CER)(Australian Government, 2021) in the early life

cycle phases (identification, concept) of the GBE and

the Streams, in order to abide by the rules and qualify

for potential funding. This is represented in the figure

by arrows containing the ‘CER’ designation

originating from the Government Operation phase

going to GBE and Streams’ Identification and

Concept life cycle phases. Note how CER is

represented in the figure by the Production side of its

Operation phase, since only that aspect is relevant to

the situation. The Government also influences the

GVE(s) created by the GBE by means of funding

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• Tele c o ms

• TertiaryEd

Hydrogen

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plastic

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Recycled

building

products

Organic

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protein

Industrial

gas

liquefier

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Fibre

waste

Fibre

waste

Fibre

waste

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grid

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Enviro‐

packa‐

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Wet

fibre

Dry

fibre

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waste

Organic

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Env.Pack‐

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An Enterprise Architecture-centred Approach towards Eco-Industrial Networking: A Case Study

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Figure 4: Possible business model of the EIN in an EA MF life cycle-based representation.

(‘GR’ in the figure).

A GVE is set up by the GBE for several reasons,

such as: a) to bid for government grants and other

types of funding for CE initiatives b) to set up the

interfaces displayed to BE members for stream

creation and operation and c) to act as a broker for the

CN, investigating the market for new potential

participants and bringing them in the GBE.

In the figure, these avenues of action are shown

as follows: a) an arrow going from the GVE to the

Government management (lobbying and applying for

funding), b) arrows going to the Identification

through Detailed Design life cycle phases of the

streams and c) arrow containing PBEM (Potential BE

Members) going from the GVE to the Identification

through to Detailed Design life cycle phases of the

GBE, respectively.

The GVE may also create and maintain a set of

Stream Reference models (SRM in fig. 4), which will

be used to store and reuse accumulated knowledge

from the creation and operation of previous streams

in view of accelerating the creation of new streams.

The reference models can take the form of e.g.

partially instantiated interfaces presented to GBE

members; they can also be used to prepare new

entrants for GBE membership. To be able to

investigate and handle new stream scenarios that do

not fit any existing knowledge that can be reused, and

to enable the continuous improvement of the existing

streams and the EIN in general, the GVE (mainly via

the resources of its tertiary education lead partner)

created an Innovation Centre (IC). In its operation,

the IC assists the GVE in finding solutions to new

problems and evolving the EIN to ensure its agility

and preparedness in the face of changing environment

conditions.

The same figure also shows how Collaborative

Network members (CNM) may contribute to the GBE

if they satisfy the Qualification Criteria (QC in the

figure) which, as described in Section 3.2, include

suitable preparedness to participate in Streams and

agreement to develop Digital Twins.

3.4 Setting up the Streams

The streams’ interfaces are designed by the GVE

according to various criteria, such as the Function,

Information, Resource and Organisation set of

viewpoints present in the EA MF (see fig. 1). As

mentioned, on entering the GBE, the CN participants

agree on creating Digital Twins which can be

subsequently used to quantify their participation in

streams. For example, if within a stream a participant

requires CO

of a certain concentration, composition

etc. delivered in a certain way one can use the

Information, Function and Resource viewpoints

: VE acting as Broker

GBE

Gvt

Legend:

Gvt: Government

CER: Clean Energy Regulator

CNM: Collaborative Network

Members

QC: Q ualification C riteria

GBE: Green Breeding Environment;

GVE: Green VE set up by GBE

PBEM: Potential BE Members

GR: Grant / Funding

S1, S2: Streams

SRM: Stream Reference Models

IC: Innovation Centre

: Operation Phase Shown –

Production only

Life cycle phases: Id: Identification; C=concept; R=requirements, PD=preliminary

design; DD=detailed design, I=implementation, Op=operation, =decommissioning;

Other aspects: P=Production / Service, M=management

PBEM

GVE

SRM

: use of Ref. Models

CNM

: Self-evolution

CER

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Figure 5: Relations between the streams and the VE.

respectively to model this aspect, while the

Organisation view may depict (if necessary) the

organisational changes required for this stream to

exist and operate. The automation viewpoint (shown

as ‘machine’ and ‘human’ in Fig. 1 and 2) may be

used to define any necessary human / machine / agent

aspect of the processes involved (Bichraoui et al.,

2013). Furthermore, the Management vs. Production

distinction present in the EA MF can also be used to

scope the Stream to the level of detail desired. As an

example, in Fig. 4 this distinction is used to represent

how the management of the stream enables its own

evolution (within limits shown as the circled life

cycle phases) as a complex system.

Figure 5 illustrates in more detail the manner in

which the chosen EA-based representation allows

depicting the relations between the streams and with

the managing GVE in the context of their life cycles.

This representation enables reasoning about the

alternative forms of such relations, possibly

uncovering emergent features that did not exist in the

participants, and about their effects which may be

positive (EIN enhancement) or needing to be avoided

(if hindering the EIN operation as a whole).

Fig. 5 shows how feedback from stream operation

analyses can flow back to and influence the GVE

(dashed arrows in the figure from stream operation to

GVE detailed design life cycle phase), which may

result e.g. in redesign or reconfiguration to optimise

the streams in question.

An example in regards to the previously-

mentioned self-evolution of the streams can be as

follows: in the process of stream operation there may

be discoveries made by the participants by way of

unforeseen optimisations and innovation which

illustrates emergent (not previously planned)

behaviour. This may result in autonomous changes up

to a certain level (in the figure, arrows from

Management side of Operation up to Detailed Design

and Implementation life cycle phases), beyond which

the GVE has to be involved in the redesign effort. For

example, in the particular case study, if there are

limited adjustments in how the food-grade gases are

produced and delivered (e.g. in the concentration /

composition requested by the customer) this can be

handled by the stream. Else, e.g. if there is a major

change (e.g. hydrogen is produced for example in a

‘blue’ rather than ‘green’ manner (Howarth &

Jacobson, 2021) due to e.g. economic reasons), that

may imply more significant changes, which need to

be handled by the GVE.

Last but not least, the streams can influence each

other; for example, in the specific case study if fibre

waste is collected from several outputs using shared

infrastructure, optimisations may be possible but also

bottlenecks may occur (see e.g. the dash-dot arrow

from Stream 1 to Stream 2 in Fig. 5). These situations

may be modelled showing the extent of the affected

life cycle phases (with other viewpoints added as

required) and suitable action can be designed and

assigned to streams’- or to GVE management,

depending on the magnitude of the issue.

3.5 A Time-based Representation

As mentioned in Section 2 and shown in Fig. 1 and 2,

the EA-based modelling constructs proposed

intentionally abstract from time so as to better focus

on the other aspects. However, in practice, at some

point it becomes necessary to determine the timing of

all the interactions (be it activities, information, etc.)

within the life cycle phases so as to enable adequate

management. For example, it is important to know

which- but also when the life cycle phases of the GVE

and streams are being influenced by other entities. In

addition, one may need to repeatedly go through some

: Stream feedback

Legend:

VE: Virtual Enterprise

S1, S2: Streams

Life cycle phases: Id: Identification; C=concept; R=requirements, PD=preliminary design; DD=detailed design,

I=implementation, Op=operation, D=decommissioning;

Other aspects: P=Production / Service, M=management

: Influence between

streams

: Self-evolution

An Enterprise Architecture-centred Approach towards Eco-Industrial Networking: A Case Study

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Figure 6: Parallel life histories of entities of interest.

life cycle phases of the participating entities – e.g. in

this case, during the re-design of the GBE, GVE, or

the streams. In order to cater for the management of

the project, one needs to also add the time dimension

to the previous representations.

As previously mentioned, GERA (see Section 2)

also contains a time-relevant construct called life

history. Its use is illustrated in Figure 6. for the

entities ‘of interest’ (involved in the EIN), where the

life cycle-based constructs defined in Section 2 and

used in Figure 4 and Figure 5 are now represented

orthogonal on a horizontal time dimension.

Using this type of figure, the stakeholders can

agree on the procedure and timing to set up the GBE,

GVE and streams and other important entities that

may be involved; for example, the Clean Energy

Regulator setting up a general framework for policies

and principles of GBE and GVE, or the Government

awarding various grants to the GVE in order to set up

the streams enabling the EIN operation.

Due to the time dimension presence, this

representation can uncover new precedence and

succession insights, such as for example the need for-

and the concept for specific Streams being possibly

defined before the creation of the VE that will design

and implement them (see arrow from Government /

CER to the upper life cycle phases of the Streams in

Figure 6). Note that the involvement of the CER in

the life cycle phases of the GBE and the potential

government support in the form of grants, initially

shown in Figure 4, is also represented here to ensure

consistency of the models.

4 REFLECTIONS ON THE

RESULTS

A significant problem in large and distributed projects

is the lack of a common understanding by the

stakeholders of the present and desired future states,

leading to conflicts and delays and eventual failure

(Davis, 2018); another is the real possibility of failing

to address every required aspect of the project, due

to complexity (Cristóbal et al., 2018). In this case, the

use of the EA artefacts in the representations has

enabled reasoning about the creation of the GBE,

GVE and streams in terms of the relevant aspects,

such as for example who designs, manages and

operates these structures, how do these entities

interact, what are other interfacing possibilities and so

on. Hence, the use of EA artefacts has allowed

Gov

’

t&C

ean

Energy

Regulator

...

Breeding

Environment

Virtual

Enterprise

LEGEND:mission fulfillment ofentity:

managementofentity:

operationalrelationship(interactionorsupport:

generativerelationship(conceive/design/create):

Influencerelationship:

CN

members

...

Streams

generalpolicies,

principles

negotiate

entry

create

design&

implement

stream

participatein

VEgovernance

IDtheneedfor,concept

andrequirements

ofstream

define

Grant/s

time

generalpolicies,

principles

participatein

Streamgovernance

give

feedback

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stakeholders to better visualise, internalise and agree

on a common perspective on the present (AS-IS) and

selected future (TO-BE) states.

The initial models used were limited in their

expressivity and cumbersome as they required the

simultaneous use of several diagrams. The use of EA

artefacts, shown to be able to subsume concepts

brought by other disciplines (CN, SoS, IE), has

allowed to model the streams in a more expressive

way and importantly, in the context of their lifecycles.

Thus, the proposed modelling approach was able

to represent important interactions and influences

between the participant entities occurring during their

entire life rather than just during operation.

The Management vs. Mission fulfillment

viewpoint has allowed to reveal other relevant aspects

in the models such as for example the ability of the

entities to adapt, in the case of the streams and the VE.

The use of the FIRO viewpoints has allowed to

model separately the flows of information and

material, while at the same time allowing to check

consistency between these flows and thus ensuring

model integration. This has supported the creation

and maintenance of meaningful and representative

digital twins, essential in life cycle management

(Macchi et al., 2018).

A potential challenge in the proposed approach

can be achieving the necessary competency in using

the modelling constructs and the fact that background

domain knowledge is still required. Along these lines,

one step further could be the use of a supporting

artefact that creates customised, directly applicable

stream setup operation and maintenance methods for

specific scenarios, i.e. a meta-methodology (Noran,

2004; Thomann, 1973). This would allow for the

needed guidance in applying the proposed EA

artefacts to the projects at hand.

5 CONCLUSIONS AND FURTHER

WORK

This paper has aimed to apply previous research

investigating the use of several knowledge areas in

EIN to a real-world case study, while at the same time

analysing the possibility and benefits of a more EA-

focused approach in EIN. Thus, various models of the

EIN have been created and assessed in contrast with

previous approaches. The conclusion has been that

EA-based artefacts have enabled a rich and integrated

representation of all the required aspects of the

relevant entities within the project, especially life

cycle, which has facilitated a common stakeholder

understanding of the present and future situations.

Thus, a major contribution of this paper is in the

assistance it provides to stakeholders by providing a

more coherent, life cycle-based and overarching view

of complex projects, enhancing agility and future-

proofing by its ability to seamlessly integrate present

and emerging modelling concepts.

Further work will seek more case studies focused

on various CE areas in order to evolve and detail the

approach presented and potentially testing the

proposed assisting meta-methodology artefact.

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