=Paper=
{{Paper
|id=Vol-2574/short3
|storemode=property
|title=Digital Enterprise Modelling — Opportunities and Challenges (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2574/short3.pdf
|volume=Vol-2574
|authors=Henderik Proper
|dblpUrl=https://dblp.org/rec/conf/vmbo/Proper20a
}}
==Digital Enterprise Modelling — Opportunities and Challenges (short paper)==
https://ceur-ws.org/Vol-2574/short3.pdf
Digital Enterprise Modelling –
Opportunities and Challenges
Henderik A. Proper1,2
1 Luxembourg Institute of Science and Technology (LIST), Belval, Luxembourg
2 University of Luxembourg, Luxembourg
E.Proper@acm.org
Abstract. Our society is transitioning from the industrial age to the digital age.
As a consequence, enterprises need to transform almost continuously, while in-
creasingly becoming “digital enterprises”.
During such transformations, coordination among the stakeholders involved is
key. Enterprise models, including value models and business ontologies, are tra-
ditionally regarded as an effective way to enable such (informed) coordination.
At the same time, the digital age also provides ample new challenges to enter-
prise modelling. Conversely, however, the digital age also provides technological
innovations that can support the activities involved in enterprise modelling.
The primary objective of this paper is to (further) raise the discussion related to
these challenges.
1 Introduction
Initially, IT enabled enterprises to automate their information processing activities.
Soon after, IT also started to be used to steer and control machinery. This enabled us to
amplify our human abilities not only in a cognitive sense, but also in a physical sense,
resulting in the automation of manual processes, e.g. using computer integrated manu-
facturing and robotics.
The on-going miniaturisation of hardware, the integration of IT and communication
technologies, the networking of IT on a global scale (i.e. the Internet), the advent of
mobile computing, and the introduction of different networked sensors / actuators, also
enabled us to amplify our communication / dialoguing capabilities as well as (remote)
sensing / actuating capabilities.
Recent developments in AI, where traditional symbolical approaches (e.g. logic and
rule-based approaches) have been complemented with statistical approaches. The latter
have especially been made possible by the availability of large amounts of (training)
data. Combined, these AI approaches have now enabled us to not just amplify our abil-
ities but even to completely take over (and improve on) human roles and activities.
Our society has now, indeed, transitioned from the industrial age to the digital age,
where IT has established itself as being an integral part of an enterprise’s primary pro-
cesses, and has quite often become an integral part of their business models as well.
Companies such as Amazon, AirBnB, Uber, Netflix, Spotify, Bitcoin, etcetera, provide
clear examples of the latter.
33
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
As a result of these trends, modern day enterprises are confronted with several chal-
lenges. These challenges impact the design of these enterprises, from the definitions
of products and services offered to their clients, via the business processes that deliver
these products and services, and the information systems that support these processes,
to the underlying IT infrastructure. These trends drive enterprises to transform continu-
ously, towards digital enterprises. From an entrepreneurial perspective, this offers many
new possibilities to optimise existing processes and services, while also offering ample
opportunities for new product and services.
As discussed in [24], coordination among the stakeholders involved is key dur-
ing such transformations. Enterprise models, and ultimately enterprise modelling lan-
guages and associated frameworks, are generally regarded as an effective way to enable
such (informed) coordination. At the same time, the digital age also provides ample
new challenges to enterprise modelling, and value modelling and business ontologies in
particular. Conversely, the digital age also provides technological innovations that can
actually support the activities involved in enterprise modelling.
The primary objective of this paper is to (further) raise the discussion related to
these challenges. In line with this, the paper covers three more specific aims. The first
aim (addressed in section 2) is to reflect on the role of enterprise modelling towards
the coordination of enterprise transformations in general. The second aim (addressed
in section 3) is to explore the challenges, which digital transformations pose to enter-
prise modelling. The third, and final, aim (addressed in section 4) is to reflect on how
enterprise modelling itself may benefit from the new digital technologies.
2 The Role of Enterprise Models
Scholars across different forms of domain modelling (including systems modelling,
knowledge modelling, information modelling, enterprise modelling, and software mod-
elling) have provided definitions of the concept of model.3 Most of these definitions are
based on the well-known semiotic triangle [19] (see figure 1).
The semiotic triangle expresses how a person attributes meaning (thought or refer-
ence) to the combination of a symbol and a referent, where the former is some language
utterance, and the latter is something that the person can refer to. The referent can be
anything, e.g. something in the physical world (tree, car, bike, atom, document, picture,
etc) or something in the social world (marriage, mortgage, trust, value, etc). Next to
that, it can be something in an existing world, or in a desired / imagined world.
The semiotic triangle is often used as a base to theorise about meaning in the context
of language [18, 28]. Based on this linguistic background, the semiotic triangle has also
been used, directly or indirectly, by several scholars3 to reason about the foundations of
(information) systems modelling.
In line with the semiotic triangle, we [2] define a model as “an artefact that is
acknowledged by an observer as representing some domain for a particular purpose”,
where observer refers to the (group of) actor(s) involved in the creation and use of the
model, and domain can be any part or aspect of the past / existing / desired world.
3 This paper certainly does not aim to provide a literature review on the concept of model. In
earlier work, as reported in e.g. [2], we did aim to provide such an overview.
34
Fig. 1. Ogden and Richard’s semiotic triangle [19]
During any enterprise transformation, coordination among the key stakeholders and
the projects / activities that drive the transformations is key [24]. Enterprise models are
generally considered as an effective way to support such coordination, as such models
can zoom in on, or relate, different aspects of an enterprise, including its structures,
purpose, value proposition, value propositions, business processes, stakeholder goals,
information systems, underlying IT infrastructures, physical infrastructure, etc. Many,
many, languages and frameworks have indeed been suggested as a way to create and
capture a different enterprise models.
In general, enterprise models can be created for different overall purposes:
1. Understand – Understand the working of the current affairs of an enterprise and /
or its environment.
2. Assess – Assess (a part / aspect of) the current affairs of an enterprise in relation to
a e.g. benchmark or a reference model.
3. Diagnose – Diagnose the causes of an identified problem in the current affairs of
an enterprise and / or its environment.
4. Design – Express different design alternatives, and analyse properties of the (de-
sired) future affairs of the enterprise.
5. Realise – Guidance, specification, or explanation during the realisation of the de-
sired affairs of an enterprise.
6. Operate – Guidance, specification, or explanation for the socio-cyber-physical ac-
tors involved in the day-to-day operations of an enterprise.
7. Regulate – Externally formulated regulation on the operational behaviour of (an)
enterprise(s).
Depending on additional factors, such as the abilities of the actors involved in the
creation and utilisation of the model, the intended usage of the model, the need for
understanding / agreement / commitment to the model from different stakeholders, etc,
these overall purposes can be refined further [23]. Furthermore, since the creation of
models involves effort, the level to which a model meets its purpose paves the way for
its Return on Modelling Effort (RoME, see Chapter 4 of [20]).
35
3 Digital-Enterprise Modelling
In this section we aim to explore some of the challenges which the transition to the
digital age potentially poses to enterprise modelling.
3.1 The dynamics of the digital age
As the digital age revolutionises the enterprise landscape, enterprises are confronted
with wave after wave of digital innovations. This leads to a situation in which these
enterprises need to work hard to keep their business models up-to-date and viable [22].
As a result, modern day enterprises need to be more agile than ever.
In the context of IT, the need for more agility has triggered the emergence of soft-
ware development approaches, such as Agile, DevOps, etc. One of the key messages
from these approaches is to avoid a big-design up front (BDUF). This may sound as a
potential threat to enterprise modelling. Nevertheless, enterprise modelling as such is a
mere neutral means to an end with a clear (intended) Return on Modelling Effort.
If the sketch on the back of a napkin of a new business process and its underly-
ing IT support, suffices as a design document for an agile project, then this is fine. It
would, indeed, imply that this “sketch” is a valid (albeit an ultra-light one) enterprise
model fitting its purpose. At the same time, however, one might wonder if a pile of such
“sketches” would suffice to conduct an enterprise-wide impact analysis, check compli-
ance to e.g. the EU’s GDPR,4 or conduct a well-founded security risk analysis. As such,
while a “sketch” might suffice the project goals of an agile project, it might not meet
the overall goals of the enterprise, and its ongoing transformations, as a whole (such as
coherence management, risk management and compliance). Furthermore, when using
a workflow engine to drive the business process, the sketch would still need to be elab-
orated in terms of a more detailed business process model (which is also an enterprise
model) that can be fed into the workflow engine.
Whatever the outcome of such a debate, it leads to the need to define situational fac-
tor, which define the purpose, the available resources for (enterprise) modelling efforts,
and the potential return on modelling effort. The resulting challenge for the field of en-
terprise modelling is therefore to provide the means to identify what kind of enterprise
modelling is needed in specific situations, including the ability to make a conscious
trade-off between local project needs and more enterprise-wide needs to coordinate
across enterprise transformations [24].
The tension between the (agile) needs of projects, and the need to manage a portfolio
of projects as part of a larger enterprise transformation, does result in a need to reflect
on the modelling concepts to be used in the different situations. For example, at an
enterprise-wide level, it might be better to use so-called architecture principles [10] to
express the overall direction of change, rather than the more detailed boxes-and-lines
diagrams such as ArchiMate [12] models. At the same time, the latter type of models are
a prerequisite to conduct a detailed impact analysis, or a thorough GDPR compliance
check. As such, the overall purposes as identified in section 2 will likely lead to the use
4 http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=
CELEX:32016R0679
36
of different modelling concepts. In other words, purpose specific modelling languages
(PSML), as a refinement to domain specific modelling languages (DSML).
3.2 Modelling concepts for the digital age
In moving beyond “automation of information processing”, the transition to the digital
age also results in new “ingredients” that make up the socio-technical fabric of modern-
day organisations and their enterprises, including the digital actors as discussed above.
In [8], we already explored some of the consequences this may have on enterprise mod-
elling languages such as ArchiMate. Here, we take a broader view on this topic, by not
limiting ourselves on the possible impact of a specific modelling language. In doing
so, we briefly highlight some of the areas in which we see a need for new modelling
concepts. At the same time, we certainly do not claim to be complete.
Moving from the outside in, a first challenge is to include value co-creation consid-
erations in the design of e.g. business models. Existing approaches such as the business
model canvas [22] focus on value exchange between economic actors in a traditional
supplier and consumer role. Value network modelling techniques, such as e3Value [9],
seem to be better positioned to deal with this shift. However, the shift to value co-
creation, requires a re-think of the traditional producer and consumer roles [6], thus
leading to a need for new / different modelling concepts [26]. Value network modelling
techniques, such as e3Value [9], seem to be better positioned to deal with this shift.
Moving inward, we arrive at the level of business processes. At this level, one can
expect even more impact on the modelling concepts needed as a result of the transition
to the digital age. For example, in [17] the authors report on what the possible impact
of blockchain on business process management can be. More generally, as argued in [8]
there is a need to more explicitly position the roles of human actors and digital actors,
and their collaboration.
Finally, the transition to the digital age also introduces new risks, as well as the
need for regulations (such as the GDPR). To analyse the possible exposure to these
risks, and ensure compliance to new regulations, enterprise models can indeed be used
(see section 2). However, this does require these models to capture the relevant aspects
of an enterprise, thus requiring modelling concepts able to express this. For example,
in the context of the GDPR, this may include aspects such as the location where data is
stored, where it is processed, where / how it is gathered, etc.
As argued in [8], the increase in the number of modelling concepts does require
more modular modelling languages, where modelling standards should should focus
primarily on providing a generic core of well-defined modelling concepts, in combina-
tion with refinement mechanisms that can be used to extend / tailor the core to the needs
at hand. The latter may involve both specialisations of the core concepts, as well as e.g.
the introduction of (purpose specific / user defined) specialisation layers.
4 Digital Enterprise-Modelling
In this section, we aim to explore how the transition to the digital age may impact
enterprise modelling itself. Needless to say that we can only explore some of the op-
portunities.
37
4.1 Enterprise cartography
In the past, it was already a challenge to keep enterprise models up-to-date. The dy-
namics of the digital age will only make this harder. Digital technologies can, indeed,
be used to support this task. In particular, approaches that use different forms of sensor
data (including log files) to infer up-to-date enterprise models, or at least (in)validate
existing enterprise models in the light of new evidence.
Existing approaches to deal with this challenge, such as software cartography [11],
process mining [1], and the more general notion of enterprise cartography [27], may
indeed provide a good starting point. These approaches would benefit even more, when
digital enterprises are actually designing with “mining in mind”. In other words, include
sensors in the design of the enterprise to enable future mining of process structures,
application landscapes, (in)formal business communication, etc.
4.2 Models as active enterprise knowledge
Increasingly, enterprise models are also used as artefacts in an operational sense. Busi-
ness process models are used as a specification for business process engine to do its
work, business rule specifications / models are similarly used to run rule engines. In
the context of software engineering, this has resulted in concepts such as models at
runtime [3].
A broader view on this was already provided by [14], who suggested to treat mod-
els as ways to capture active knowledge that may support all operational activities in
organisations / enterprises. Additionally, so-called Hybrid Wiki’s [5, 16] have also been
suggested as a strategy to capture, and operationalise, enterprise knowledge in a semi-
structured format.
Digital technologies, in particular in terms of an integrated enterprise-modelling
data-ecosystem, will further enable the use of models to capture and utilise enterprise
knowledge as part of the operational activities. A specific kind of enterprise models
are, of course, models act as complete replicas of part of the enterprise, e.g. enabling
detailed simulations. Such models are, nowadays, frequently referred to as digital twins.
4.3 Interactive models
Models are quite often used in a context in which the need to span the “boundaries”
between different groups of stakeholders with differing backgrounds and interests, as
such turning them into so-called boundary objects [13]. As a consequence, boundary
objects a “form” that is engaging to its users, for instance in terms of tangible and / or
interactive models. This is where digital technologies potentially have a role to play.
For instance, research involging the use of so-called tangible user interfaces, indi-
cates that it is possible to more effectively mix social, digital, and physical actors, to
better capture (and discuss) designs [25, 15]. Interactive tabletops have already been
shown to support modelling of concepts maps [21] or business process models [7].
The field of collaboration engineering [4] also relies on the use of digital technolo-
gies to support the collaborative process, e.g. allowing for anonymous collaborative
38
brainstorming. Something that would be virtually impossible to do in real time using a
pen-and-paper based approach.
What still seems to be missing, however, is a better integration of these techniques
with traditional enterprise modelling tools. On might even go as far as stating that an
integrating architecture is needed for enterprise-modelling data-ecosystem to bring such
concepts to fruition.
5 Conclusion
In this paper, we explored the impact on the transition to the digital age on enterprise
modelling. In line with this, we reflected on the role of enterprise modelling towards
the coordination of enterprise transformations in general. We then explored some of
the challenges which the shift to “digital enterprises” puts on enterprise modelling,
while finally also reflecting on how enterprise modelling itself may benefit from the
new digital technologies.
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