=Paper=
{{Paper
|id=Vol-2749/paper10
|storemode=property
|title=Managing the Architecture Complexity of Intelligent Digital Systems
|pdfUrl=https://ceur-ws.org/Vol-2749/paper10.pdf
|volume=Vol-2749
|authors=Kurt Sandkuhl,Alfred Zimmermann,Rainer Schmidt,Dierk Jugel,Michael Möhring
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/SandkuhlZ0JM20
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==Managing the Architecture Complexity of Intelligent Digital Systems==
https://ceur-ws.org/Vol-2749/paper10.pdf
Managing the Architecture Complexity of Intelligent
Digital Systems
Kurt Sandkuhl1, Alfred Zimmermann2, Rainer Schmidt3,
Dierk Jugel2, Michael Möhring3
1 Institute
of Computer Science, University of Rostock,
kurt.sandkuhl@uni-rostock.de
2 Reutlingen University, Reutlingen, Germany
Alfred.Zimmermann@Reutlingen-University.de, Dierk Jugel@hhz.de
3 Munich University of Applied Sciences, Germany
rainer.schmidt@hm.edu, michael.moehring@hm.edu
Abstract. Digital technologies are main strategic drivers for digitalization and
offer ubiquitous data availability, unlimited connectivity, and massive processing
power for a fundamentally changing business. This leads to the development and
application of intelligent digital systems. The current state of research and prac-
tice of architecting digital systems and services lacks a solid methodological
foundation that fully accommodates all requirements linked to efficient and ef-
fective development of digital systems in organizations. Research presented in
this paper addresses the question, how management of complexity in digital sys-
tems and architectures can be supported from a methodological perspective. In
this context, the current focus is on a better understanding of the causes of in-
creased complexity and requirements to methodological support. For this pur-
pose, we take an enterprise architecture perspective, i.e. how the introduction of
digital systems affects the complexity of EA. Two industrial case studies and a
systematic literature analysis result in the proposal of an extended Digital Enter-
prise Architecture Cube as framework for future methodical support.
Keywords: Architecture Complexity, Digital Enterprise Architecture, Digital
Systems
1 Introduction
Digital transformation is the current dominant type of business transformation [2],
[3] having IT both as a technology enabler and as a strategic driver. Digital technologies
are main strategic drivers [1] for digitalization because digital technologies are chang-
ing the way, how business is conducted and have the potential to disrupt existing busi-
nesses. SMACIT defines in [1] the strategic core of digital technologies, with abbrevi-
ations for Social, Mobile, Analytics, Cloud, the Internet of Things. From today’s view
some scholars argue that we have to enlarge this technological core by artificial intelli-
gence and cognition, biometrics, robotics, blockchain and edge computing. Digital
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
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technologies deliver three core capabilities for a fundamentally changing business [1]:
ubiquitous data availability, unlimited connectivity, and massive processing power.
This leads to the development and application of intelligent digital systems (see sec-
tion 3). We see great future prospects for digital systems with artificial intelligence (AI)
[4], [5], with the potential to contribute to improvements in many areas of work and in
society through digital technologies. We understand digitalization based on new meth-
ods and technologies of artificial intelligence as a complex integration of digital ser-
vices, products and related systems. Classical industrial products are limited in their
change and configuration possibilities once deployed to users. On the contrary, digit-
ized products are more dynamic [2]. They contain both hardware and software with
(cloud-)services. They can be upgraded via network connections. In addition, their
functionality can be extended or adapted using external services. Therefore, the func-
tionality of products is dynamic and can be adapted to changing requirements and hith-
erto unknown customer needs. In particular, it is possible to create digital products and
services step-by-step or provide temporarily unlockable functionalities. So, customers
whose requirements are changing can add and modify service functionality without
hardware modification.
Unfortunately, the current state of art in research and practice of architecting digital
systems and services lacks a solid methodological foundation as the established
methodical approaches do not fully accommodate requirements, e.g., caused by
product-IT integration [6] or digital manufacturing. The long-term aim of our work is
to contribute to an efficient and effective development of digital systems in organiza-
tions. Our conjecture is the managing complexity will be an important aspect of reach-
ing this aim. Therefore, our current research focuses on the main research question:
How can management of complexity in digital systems and architectures be supported
from a methodological perspective?
In our work, we take an enterprise architecture (EA) perspective (see section 3), i.e.
we do not consider single products, applications or business services but the overall
change in business architecture, application architecture and technology architecture of
an enterprise. As a first step for investigating the above research question, this paper
focuses on a better understanding of the causes of increased complexity and require-
ments to methodological support. More concrete, our focus in this paper is to investi-
gate how the introduction of digital systems affects the complexity of EA.
The rest of the paper is structured as follows. Section 2 introduces the research meth-
ods applied in the paper. Section 3 summarizes the background for our work from EA
and digital systems, and discusses related work on architecture complexity. Section 4
presents two industrial case studies of digital system development. Section 5 investi-
gates the effects on architecture complexity in the case studies, Section 6 presents the
extended Digital Enterprise Architecture Cube as a result of the case study analysis.
Section 7 summarizes our findings and discusses future work.
2 Research Approach
This paper is part of a research process aiming to provide methodological and tool sup-
port for managing architecture complexity. It follows the five stages of Design Science
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research [23], namely, problem explication, requirements definition, design and devel-
opment of the design artifact, demonstration, as well as evaluation. This study concerns
the first step, the problem explication including confirmation of problem relevance.
This part of our research started from the research question motivated and presented in
section 1: RQ: In the context of digital transformation, how does the introduction of
digital systems affect the complexity of enterprise architectures?
The research method used for working on this research question is a combination of
literature study and descriptive case study. Based on the research question, we analyzed
the literature in these areas. The purpose of the analysis was to find work from enter-
prise architecture management or digital systems that explicitly addresses changes in
architecture complexity when introducing artificial intelligence (see section 3.3).
As the literature analysis did not produce relevant papers, we identified industrial
cases of AI introduction into the EA and performed qualitative case studies in order to
obtain relevant and original data (see section 4). Qualitative case study is an approach
to research that facilitates exploration of a phenomenon within its context using a vari-
ety of data sources. This ensures that the subject under consideration is explored from
a variety of perspectives which allows for multiple facets of the phenomenon to be
revealed and understood. Within the case studies, we used two different perspectives,
which at the same time represent sources of data: we analyzed the project documenta-
tion and we investigated the enterprise architecture, business process and software de-
sign models. Yin [24] differentiates case studies explanatory, exploratory and descrip-
tive. The case studies in section 4 are considered descriptive, as they describe the phe-
nomenon of initiating DT development and the real-life context in which it occurs.
Based on the results of the case studies, we argue that additional perspectives and
architecture views would be beneficial for managing architecture complexity.
3 Background and Related Work
3.1 Digital Enterprise Architectures
The term enterprise architecture (EA) in general denotes the fundamental conception
or representation of an enterprise—as embodied in its main elements and relation-
ships—in an appropriate model. Enterprise architecture management (EAM) provides
an approach for a systematic development of an enterprise’s architecture in line with
its goals by performing planning, transforming, and monitoring functions. The reasons
for applying EAM are manifold, such as supporting the alignment of IT to business
goals or the reduction of complexity. In general, an EA captures and structures all rel-
evant components for describing an enterprise, including the processes used for devel-
opment of the EA as such [7]. Research activities in EAM are manifold. The literature
analysis included in [8] shows that elements of EAM [9], process and principles [10],
and implementation drivers and strategies [11] are among the frequently researched
subjects. Furthermore, there is work on architecture analysis [12] and decision making
based on architectures [13], which so far does not include AI-related decisions.
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Digital enterprise architecture [14], [15] provides a comprehensive view on inte-
grated elements from both business and IT for implementing digital transformation
strategies including digital systems and services (see next section). More precisely we
integrate configurations of stakeholders (roles, accountabilities, structures, knowledge,
skills), business and technical processes (workflows, procedures, programs), and tech-
nology (infrastructure, platforms, applications) to execute digital strategies and com-
pose value-proposition-oriented digital products and ser-vices. Digital business design
covers not simple business restructuring or just IT architecture. Digital business is fore-
most an aspect that is currently in use and constantly changing.
3.2 Intelligent Digital Systems and Services
From today’s view, probably no digital technology is more exciting than artificial
intelligence offering massive automation capabilities for intelligent digital systems and
services. Artificial intelligence (AI) [4], [16] is often used in conjunction with other
digital technologies, like analytics, ubiquitous data, the Internet of Things, cloud com-
puting, and unlimited connectivity. Fundamental capabilities of AI concern automatic
generated solutions from previous useful cases and solution elements, inferred from
causal knowledge structures like rules and ontologies, and from learned solutions based
on data analytics with machine learning and deep learning with neural networks.
Artificial intelligence receives a high degree of attention due to recent progress in
several areas such as image detection, translation and decision support [4]. It enables
interesting new business applications such as predictive maintenance, logistics optimi-
zation and improving customer service management. Artificial intelligence supports
decision-making in many business areas. Most companies expect to gain competitive
advantage from AI. Today's advances in the field of artificial intelligence [17], [18]
have led to a rapidly growing number of intelligent services and applications. The joint
development of competencies via intelligent digital systems promises great value for
science, economy and society and is driven by data, calculations and advances in algo-
rithms for machine learning, perception and cognition, planning and natural language.
Artificial intelligence is often characterized as impersonal: From this point of view,
intelligent systems operate completely automatically and independently of human in-
tervention. The public discourse on autonomous algorithms working on passively col-
lected data contributes to this view. However, this perspective of huge automation ob-
scures the extent to which human work necessarily forms the basis for modern AI sys-
tems [16] and makes them possible in the first place. The human element of intelligent
systems includes tasks like optimizing knowledge representations, developing algo-
rithms, collecting and tagging data, and deciding what to model and how to interpret
the results. The study of artificial intelligence from a human-centric perspective re-
quires a deep understanding of the role of human ethics, human values and customs,
and the practices and preferences for development and interaction with intelligent sys-
tems. With the success of AI, new concerns and challenges regarding the impact of
these technologies on human life are emerging. These include issues of security and
trustworthiness of AI technologies in digital systems, the fairness and transparency of
systems, and the conscious and unintended impact of AI on people and society.
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Combining product components of hardware and software with cloud-provided in-
telligent services enable new ways of intelligent interaction with customers, as in [19].
The lifecycle of digitized products is extended by intelligent services. An example is
Amazon Alexa, which groups a physical device having a micro-phone and speaker with
services, called Alexa skills. Users can enhance Alexa's capabilities with skills which
are similar to apps. The set of Alexa skills is dynamic and can be tailored to the cus-
tomer’s requirements during runtime. Alexa enable voice interaction, music playback,
to-do lists, set alarms, stream podcasts, play audio books and provide weather, traffic,
sports, and other real-time information such as news. Using programmed skills Alexa
can also connect and control intelligent products and devices.
3.3 Literature Analysis on Architecture Complexity
As part of our research work, we performed a literature analysis that aimed at identify-
ing research work from enterprise architecture management or digital systems that ex-
plicitly addresses changes in architecture complexity when introducing artificial intel-
ligence. In order to identify relevant work, we decided to perform a systematic literature
review (SLR) based on the procedure proposed by Kitchenham [22] with the main re-
search question What published work exists on architecture complexity of digital sys-
tems, artificial intelligence and in digital transformation?
The literature source examined was Scopus, which includes most publications from
the AIS electronic library (AISeL), IEEE Xplore and Springer. Publications with sig-
nificant impact on research should reach one of these major outlets. Starting from the
research question, we constructed a search query for Scopus by including different key-
word combinations and synonyms. The final search queries are shown in Table 1.
Table 1. Search queries and number of hits for the RQ
Search query No. of Rele-
papers vant
papers
1 “architecture complexity” 230 7
Refinements of query 1:
2 (“architecture complexity”) AND (“digital system” OR 2 1
“intelligent system”)
3 (architecture complexity) AND (artificial intelligence) 3 0
4 (architecture complexity) AND (digital transformation 0 0
OR digitalization)
5 (architecture complexity) AND (digital business) 2 1
The search results show that there is quite some work on “architecture complexity”
(query #1), but most of this work is focused on non-IT architectures (buildings, facili-
ties, models), hardware architectures (system-on-chip, FPGA etc.) or general software
architectures. However, there is not much work on architecture complexity of digital
systems, intelligent systems or artificial intelligence (queries #2 to #4). Most papers
found were on system-on-chip architectures or protein structures. The only relevant
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paper [20] proposes an approach for evaluating the complexity of EA components land-
scapes with a focus on public administration. This approach could be relevant for our
long-term aim to provide method support, but it is not tackling our current focus of
understanding the changes in complexity caused by AI. The search for papers on archi-
tecture complexity in the context of digital transformation (query #5) also returned only
one relevant paper [21] that investigates how to monitor complexity of IT-architectures.
However, this paper does not address the effects of AI and might be relevant only for
our future work.
4 Industrial Case Studies
The two case studies described in this section were selected from different research and
development projects with industrial partners conducted at Rostock University during
fall 2019 and spring 2020. The participating researchers made notes during meetings,
collected documents and field notes when working with the companies. This material
forms the basis for the case studies and is presented in a condensed way in this section.
4.1 Case A: AI for Fraud Detection
Case study company A is a small payment service provider from Germany offering
various IT-based services for handling payment transactions for small and medium-
sized banks. The company was among the first in Germany to offer support for instant
payment transactions (IPT). Today it normally takes one business day for a payment to
reach the beneficiary, but instant payments realize this in close to real-time (i.e., within
less than 10 seconds). This is independent of the underlying payment instrument used
(credit transfer, direct debit or payment card) and clearing (bilateral interbank clearing
or clearing via infrastructures) or settlement (e.g. with guarantees or in real time).
Instant payment solutions usually consist of the scheme layer (end-user solutions for
the market), clearing layer (arrangements for clearing of transactions between payment
service providers) and settlement layer (arrangements for settlement of transactions).
Company A provides clearing layer and settlement layer functions in combination with
value-added services, such as fraud detection, sanction screening and embargo check-
ing. The case considered in this paper emerged when the company decided to explore
possibilities of AI use in IPT handling
After a requirements analysis, the case study company performed a feasibility study
that investigated different AI approaches for detecting fraudulent transactions [31] and
developed a concept how to integrate the required AI sub-system into the existing en-
terprise architecture. In the business architecture, the future roles expected to use the
AI solution for IP fraud detection were identified. These roles are the ones who need to
understand the decisions of the AI solution. Furthermore, the business process steps to
be automated by the future AI component also had to be determined and the related
affected tasks of other processes were located. This makes clear what process steps
deliver input and what steps need to receive output information.
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In the information architecture, focus was on identifying what information required
for the fraud detection already is available (and what applications or services in the
application architecture provide or consume this information) and, more important,
what information is missing. Here, the required information for fraud detection is
spread onto different data sources (payment monitoring system, core banking system,
customer transaction history). With this distribution onto different data sources, an in-
tegrated data set has to be created to allow for a performant implementation of the AI
solution. Integrating data “on the fly” would require too much time and disturb the other
application using the same data.
In the application and technology architecture, the applications affected by a new AI
solution, either because they have to provide data or because they receive the AI solu-
tion’s results, were identified. Furthermore, the technology currently used indicated
constraints for the future AI solutions with respect to physical location of data storages
and technical architecture of the services used.
Fig. 1. Excerpt from architecture of case A
Figure 1 shows an excerpt of the architecture model for case A with focus on inves-
tigation of suspicious transactions.
4.2 Case B: AI for Object Recognition and Marketing Support
Case study Company B is content-marketing enterprise specialized in the creation
and distribution of online-videos, and in using these videos for marketing purposes.
This company aims at new business models exploiting the possibility to create interac-
tion with the users and new innovative media formats. In particular, free online videos
have a high reach in the advertising-relevant target group. Such videos contain several
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scenes and show mostly fashion-related content applicable for content-related advertis-
ing. For example. if a video shows a close-up of a male face wearing sunglasses, ad-
vertisements should be placed for these glasses. Knowing what kind of object is shown
in the video therefore is crucial for the service. Traditionally, the objects in videos were
identified by manual “tagging” of the videos. This approach is labor-intensive and dif-
ficult to scale up due to the need to hire and train the workforce. Automatic image
detection technologies can enable more efficient and cost-effective operation.
The case study company started to develop an innovative technological approach by
combining a technique from the symbolic and approximate sub-disciplines of AI re-
search. The aim is to apply knowledge captured in an ontology to improve the process
of object recognition in videos, which is based on an artificial neural network (ANN)
and a deep-learning approach. The ontology is supposed to capture the relevant
knowledge for the application field of discovering fashion items in videos. This
knowledge includes, for example, a taxonomy of fashion items, environments suitable
for specific fashion categories (mountain, skiing, outdoor), social contexts relevant for
fashion categories (weddings, parties), and more. Furthermore, the ontology also is
used to capture combinations of fashion items relevant for defined marketing purposes,
like, for example, the fashion for a particular target group. For each concept in the on-
tology, there is a corresponding classification model in the deep learning part of the
system. This part consists of the Deep Learning Management software component
providing access to the ANN Database containing available models.
From an organizational perspective, both the maintenance of the ontology, the con-
tinued training of the deep learning module, the integration of the automatic tagging
into existing processes and the development of new business services based on this
platform had to accompany the implementation of the above AI solution. From a tech-
nical perspective, the key task was the integration with the existing marketing and con-
tent distribution engine, which also includes customer profiles, campaign management
and advertisements.
5 Case Study Analysis
5.1 Approach for Analyzing Architecture Complexity
Complexity has been subject of research since several decades. In information sys-
tems research, many scholars consider research on system complexity as more im-
portant than algorithmic or algebraic complexity. In his well-received discussion of hi-
erarchy, Simon defined 1962 a complex system as a system consisting of a large num-
ber of parts that have many interactions [25]. [26] described a complex organization as
a set of interdependent parts, which together make up a whole that is interdependent
with some larger environment. [27] investigates and describes key elements of complex
adaptive systems. As EA capture the essential structures and elements of an enterprise
and thus relate to socio-economic-technical systems, we consider the aforementioned
work on organizational complexity as relevant for our field.
For investigating changes in complexity caused by the introduction of AI in the cases
studies, an operationalization of complexity is required. For this purpose, we consider
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existing operationalizations for project complexity and product complexity as in par-
ticular interesting. Project complexity addresses aspects of interaction of stakeholder
and processes related to the business architecture and product complexity parts and
variations of a product which is related to digital systems as “products” and their appli-
cation architecture.
A review regarding the concept of project complexity performed by Baccarini [28]
proposes to define complexity as “consisting of many varied interrelated parts”, to dis-
tinguish between organizational and technological complexity, and to operationalize
this in terms of “differentiation and interdependence”. Differentiation refers to the num-
ber of varied elements, e.g. tasks or components; interdependence characterizes the in-
terrelatedness between these elements. Regarding organizational complexity, [28] iden-
tified among other indicators the number of organizational units involved and the divi-
sion of labor. For technological complexity, the diversity of inputs and output and the
number of specialties (e.g. subcontractors) are considered. In the area of product com-
plexity, work of Hobday, [29], regarding distinctive features of complex products and
systems identifies dimensions defining the nature of a product and its complexity. The
not exhaustive list of 15 critical product dimensions provided by Hobday includes quan-
tity of sub-systems and components, degree of customization of products and intensity
of supplier involvement. These dimensions will be used in combination with Bacca-
rini’s project complexity indicators when evaluating the case studies in section 4.
5.2 Architecture Complexity in the Case Studies
Using the indicators proposed by Baccarini and Hobday (see section 5.1), we analysed
the cases in section 4 regarding the changes implemented during the introduction of AI.
The result is summarized in table 2.
Judging from the “increase” in most indicators presented in table 1 for both cases,
we have reason to believe that there is confirmation for an increase in complexity. As
this concerns the business and application architecture, we argue that this concerns the
overall EA complexity. A possible explanation might be that both cases were finished
not too long ago and that a consolidation of the enterprise architecture is required that
integrates and optimizes inefficient components. This requires further investigation.
Table 2: Indicators for the change in architecture complexity
Indicator Case A Case B
Business Architecture
Organizational No change Increase: Sales and Customer support
units involved had to be involved for defining rele-
vant tags
Organizational Increase: new role created for Increase: two new roles created (train-
roles involved managing the AI configuration ing ML for new detectors; knowledge
engineer)
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Business Pro- Increase: fraud detection pro- Increase: new processes for the new
cesses affected cess with interface to IP trans- roles (see above) and their interface to
action handling existing sales and operations processes
Application Architecture
Sub-systems Increase: AI sub-system, new Increase: two AI sub-systems; new
and components data extraction and integration services for situation detection and
system contextualization
Degree of cus- No change regarding the estab- No change regarding the established
tomization lished IT sub-systems IT sub-systems
Intensity of sup- No change (after finishing the Increase: supplier of ML component
plier involve- AI project) continues to render services
ment
6 Extended Digital Enterprise Architecture Reference Cube
Enterprise Architecture Management (EAM) defines today with frameworks, stand-
ards, tools and practical expertise a large set of different views and perspectives. We
argue that a new complexity-focused digital enterprise architecture approach should
better enable the digitalization of adaptive intelligent products and services. DEA –
Digital Enterprise Architecture Reference Cube (Fig. 2) is our current extended archi-
tectural reference model from [30] to support architecture management, engineering,
and analytics considering a set of multi-perspective viewpoints for enterprise architec-
tures. Our research focused to the presented industrial case studies essentially defines
the original base of the Digital Enterprise Architecture Reference Cube (DEA), having
now eleven integral architecture domains for a holistic architecture classification
model. The integral architecture areas of the DEA have a core of standardized architec-
ture aspects and their relations to TOGAF and ArchiMate and extend these standardized
architecture domains with our perspectives that focus on the new topics of Artificial
Intelligence-based digitalization.
DEA - Digital Enterprise Architecture Reference Cube provides our comprehensive
architectural reference model to integrate in a bottom-up manner dynamically com-
posed micro-granular architectural services and their models for supporting intelligent
digital services and products. We have extended our service-oriented enterprise archi-
tecture reference model for the evolving digital transformation context by micro-gran-
ular structures, like the Internet of Things, and Microservices. Further, we have associ-
ated multi-perspective architectural decision models, which are supported by view-
points and functions of an architecture management cockpit.
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Fig. 2. Digital Enterprise Architecture Reference Cube
Digital enterprise architecture should be both holistic and easily adaptable to support
micro-granular structures like IoT and the digital transformation with new business
models and technologies, like social software, big data, services computing with cloud
computing, mobility platforms and systems, security systems, and semantics support.
DEA is more specific than existing architectural standards of EAM – Enterprise Ar-
chitecture Management and extends these architectural standards for digital enterprise
architectures with services and cloud computing. DEA provides a holistic classification
model with ten integral architectural domains. These architectural domains cover spe-
cific architectural viewpoint descriptions in accordance to the orthogonal dimensions
of both architectural layers and architectural aspects. DEA abstracts from a concrete
business scenario or technologies, but it is applicable for concrete architectural instan-
tiations to support digital transformations.
Metamodels and their architectural data are the core part of the digitization architec-
ture. Architecture metamodels should support analytics-based architectural decision
management and the strategic as well as IT/business alignment. Three quality perspec-
tives are important for an adequate IT/business alignment and are differentiated as: (I)
IT system qualities: performance, interoperability, availability, usability, accuracy,
maintainability, and suitability; (II) business qualities: flexibility, efficiency, effective-
ness, integration and coordination, decision support, control and follow up, and organ-
izational culture; and finally (III) governance qualities: plan and organize, acquire and
implement deliver and support, monitor and evaluate.
DEA abstracts from a particular business scenario or technology because it can be
applied to concrete architecture instantiations to support digital transformations inde-
pendently of different domains. The DEA reference cube covers the top of the Platform
and Ecosystem Architecture. A digital platform is in our understanding a repository of
business, data, and infrastructure services used to configure digital offerings from dig-
ital services rapidly. Digital Services and components are slices of code that perform a
specific task. We position reusable digital services as parts of an ecosystem of services.
A digital platform linearizes the complexity of cooperating services. It integrates core
124
technology services to provide standardized access points and repositories for an intel-
ligent service ecosystem of business services, data services, and infrastructure services.
The value of a platform to users results from the number of platform and service users.
A digital platform and an ecosystem should enable shared value creation for all stake-
holders and facilitate the exchange of goods, services, and social currency. Platforms
do not own or control their resources and are therefore well suited for scalability within
the ecosystem.
7 Concluding Remarks and Future Work
Based on established definitions of complexity, in particular from project and prod-
uct management, the paper investigated possible changes in complexity when AI func-
tionality was added to business services and enterprise architecture. The investigated
cases confirm our conjecture that the EA grows more complex. However, due to the
very small number of cases, more work is needed in this area. So far, we consider our
results only as a confirmation of the problem relevance.
One of the building blocks for methodical support to managing complexity is – to
our opinion – the use of EAM and the differentiation into different perspectives as pre-
sented in section 6 when discussing the extended version of the Enterprise Architecture
Reference Cube.
Future work will have to include the investigation of more cases to more clearly
define requirements to the methodical support. Furthermore, the overall DSR process
described in section 2 has to be continued by clarifying root causes and designing an
initial method proposal.
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