The Practice of Enterprise Modeling

Systems, such as production plants, logistics networks, IT service companies, and international financial companies, are complex systems operating in highly dynamic environments that need to respond quickly to a variety of change drivers.

The digital transformation is a global mega trend that is triggered by the evolution of digital technology, that has the potential for every organisation to either optimize their current business via a digital innovation or by transforming the business via digital disruption. The challenge for every organisation is therefore to select and personalise the appropriate digital innovation. There is a plethora of methods and assessment frameworks, here we introduce the OMiLAB Innovation Corner that assists in (1) creating new business, (2) design the organisational model and (3) engineer proof-of-concept prototypes as a “communication media”. The unique value proposition of OMiLAB Innovation Corner is the model-based foundation that supports decision makers in key phases of the innovation. First, the creation of new business models by providing digital design thinking tools is assisted. Second, the design of the digital organisation by providing extended modelling capabilities is supported. Third, a proof-of-concept engineering providing robots and sensors is enabled. We share our practical exeriences by introducing (a) how new business models are created in the H2020 project Change2Twin to help manufacturing SMEs in their digital transformation, (b) how conceptual models are design in the H2020 project BIMERR to create digital twins of renovation processes and (d) how proof-of-concept engineering is performed in the FFG project complAI to analyse different robotic behaviour.

Today’s dynamic business environment requires enterprises to be agile in order to remain competitive. Competition also impacts enterprise modeling (EM) aiming at the systematic development of the enterprise’s business processes, organizational structures, and information systems. Although EM is a mature discipline, organizations still do not exploit the full potential of EM. We suggest, the concept of a Digital Twin of an Organization (DTO) provides a means of digitalization to introduce more agility. A DTO draws upon graph-based, machine readable knowledge representation of enterprise models. In order to run through various scenarios in real time, the DTO approach makes use of Context Spaces that provide the required information semantically structured, which improves the comprehensibility and applicability of the used models. The DTO combines EM with operational reality and, thus, increases the agility of the enterprise.

Products and services are essential to an enterprise’s operations and hence they need to be designed, developed, and delivered in congruence with the enterprise’s business strategy and design. This paper investigates how to identify relevant aspects of products and services to be added to enterprise models by extending existing Enterprise Modeling (EM) languages for product/service modeling. The proposal is to decide on the relevance for EM based on “touch points”. The proposal is applied to extending the 4EM method with a Product/Service Model and is based on literature analysis in the area of product and service modeling and on an industrial case study.

The importance of involving enterprise stakeholders in organizational transformation and development processes has been acknowledged in many scholarly publications in the context of business information systems research. Method and tool support for this is particularly explored and provided by the field of participatory enterprise modelling (PEM). In PEM, modelling sessions involving all relevant stakeholders and guided by a modelling facilitator are a central element. However, the published work on PEM is not very extensive with respect to structuring such modelling sessions, in particular when combining analytical and design parts. It is hence hard for novice modelling facilitators to plan a workshop, to switch between different workshop phases and to react to unforeseen events. Since existing literature covers only generic aspects of workshop moderation, we fill this gap in providing an initial model that can serve to inform, structure and guide PEM sessions. The model has been developed by analysing examples from real-world modelling sessions.

Trust is widely acknowledged as the cornerstone of relationships in social life. But what makes an agent trust a person, a resource or an organization? Which characteristics should a trustee have in order to be considered trustworthy? The importance of understanding trust in organizations has motivated us to investigate the representation of trust concerns in enterprise models. Based on a well-founded reference ontology of trust, we propose a pattern language for trust modeling in ArchiMate. We present a first iteration of the design cycle, which includes the development of the pattern language and its demonstration by means of a realistic case study about trust in a COVID-19 data repository.

With the increased uptake of DEMO also comes a growing need for enterprise-grade tool support. In this paper, we therefore report on a study concerning the selection, configuration, and extension, of an enterprise-grade tool platform to support the use of DEMO in practice.

The selection process resulted in the selection of Sparx Enterprise Architect for further experimentation in terms of configuration towards DEMO. The configuration of this tool framework to support DEMO modelling, also provided feedback on the consistency and completeness of the DEMO Specification Language (DEMOSL), the specification language that accompanies the DEMO method.

Enterprise modeling deals with the increasing complexity of processes and systems by operationalizing model content and by linking complementary models and languages, thus amplifying the model-value beyond mere comprehensible pictures. To enable this amplification and turn models into computer-processable structures a comprehensive formalization is needed. In this paper we build on the widely accepted approach of logic as basis for modeling languages and define them as languages in the sense of typed predicate logic comprising a signature \(\varSigma \) and a set of constraints. We concretize how the basic concepts of a language – object and relation types, attributes, inheritance and constraints – can be expressed in logical terms. This naturally leads to the denotation of a model as \(\varSigma \)-structure satisfying all constraints. We apply this definition also on the metalevel and propose a formal modeling language to specify metamodels called M2FOL. A thus formalized metamodel then rigorously defines the signature of a language and we provide an algorithmic derivation of the formal modeling language from the metamodel. The effectiveness of our approach is demonstrated by formalizing the Petri Net modeling language, a method frequently used for analysis and simulation in enterprise modeling.

We are interested in semi-automating the process of generating a formal specification from a legal contract in natural language text form. Towards this end, we present a tool, named ContracT, that annotates legal contract text using an ontology for legal contracts. In the last part of the paper, we present results from a preliminary empirical evaluation of the tool that provided encouraging results in identifying contract concepts in text and discuss critical points to be tackled in future studies.

While ADOxx is a popular platform for the creation and use of enterprise modeling languages, it provides only limited support for a well-formedness check of created enterprise models. In this paper, we propose to complement the meta modeling platform ADOxx with Alloy, which natively provides extensive model checking capabilities, so as to enable a well-formedness check of enterprise models created in ADOxx.

Using the \(e^{3}{value}\) modeling language as a point of departure, we particularly provide (a) a partial ADOxx implementation of \(e^{3}{value}\), (b) a proof-of-concept XML2Alloy parser, which allows for converting \(e^{3}{value}\) models created in ADOxx into Alloy format, so that (c) \(e^{3}{value}\) well-formedness constraints stated in Alloy can be used to check the validity of an \(e^{3}{value}\) model with the Alloy Evaluator. Beyond the specific proof-of-concept, we also discuss further possibilities of using ADOxx in conjunction with Alloy, particularly in checking the soundness of meta models underlying an enterprise modeling language.

Effective decision-making based on precise understanding of an organisation is critical for modern organisations to stay competitive in a dynamic and uncertain business environment. However, the state-of-the-art technologies that are relevant in this context are not adequate to capture and quantitatively analyse complex organisations. This paper discerns the necessary information for an organisational decision-making from management viewpoint, discusses inadequacy of the existing enterprise modelling and specification techniques, proposes a domain specific language to capture the necessary information in machine processable form, and demonstrates how the collected information can be used for a simulation-based evidence-driven organisational decision-making.

Modern digital organizations are constantly facing new opportunities and threats, originating from the highly dynamic environments they operate in. On account of this situation, they need to be in a state of constant change and evolution to achieve their goals or ensure survival, and this is achieved by adapting their capabilities. Enterprise Modeling and capability modeling have provided a plethora of approaches to facilitate the analysis and design of organizational capabilities. However, there is potential for improving management of capability change. This Design Science research aims to provide methodological and tool support for organizations that are undergoing changes. A previously introduced meta-model will serve as the basis for a method supporting capability change. The goal of this study is to explore expert knowledge about organizational change in order to evaluate the initial version of the meta-model and identify possible weaknesses. Ten semi-structured interviews have been conducted to explore the perspectives of experienced decision-makers on capability change. Three categories emerged from the analysis, reflecting on how capability change is observed, decided and delivered respectively. These have been used as input for revising the conceptual structure of the capability change meta-model.

In the 1950’s, W. Ross Ashby introduced the Law of Requisite Variety in the context of General Systems Theory. A key concept underlying this theory is the notion of variety, defined as the total number of distinct states of a system (in the most general sense). We argue that domain modelling (including enterprise modelling) needs to confront different forms of variety, also resulting in a need to “reflect”/“manage” this variety. The aim of this paper is to, inspired by Ashby’s Law of Requisite Variety, explore some of the forms of variety that confront domain modelling, as well as the potential consequences for models, modelling languages, and the act of modelling. To this end, we start with a review of our current understanding of domain modelling (including enterprise modelling), and the role of modelling languages. We then briefly discuss then notion of Requisite Variety as introduced by Ashby, which we then explore in the context of domain modelling.

The evolution of industries is constantly forcing enterprises to adapt to ever-changing market dynamics. Companies are challenged by remodelling their resources, processes, and competencies as well as to define new goals in accordance with evolving complex and dynamic environments. Virtual Factory as a dynamic, cognitive, open, and holistic system promises a potential for an adaptive enterprise modelling tool to support manufacturing companies in dealing with such challenges. This short paper attempts to frame the theoretical concepts for evolving markets and adaptive organisations (systems) in terms of the theory of industrial cycles, systems theory, and competence theory. Furthermore, the Virtual Factory concept is presented and discussed based on framed theories and four dimensions of competence theory.

In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason with their content. In other words, it is important for these reference conceptual models to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages the rich semantics of ontology-driven conceptual models (ODCM). In particular, the technique employs the notion of Relational Context to guide automated model breakdown. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator).

In recent years, there has been a growing interest, within the financial sector, in the adoption of ontology-based conceptual models to make the nature of conceptualizations explicit, as well as to safely establish the correct relations between them, thereby supporting semantic interoperability. Despite the wide number of efforts to create a unified view of the reality related to economic and financial domains, no comprehensive enough formal model has been developed to, on one hand, accurately describe the semantics regarding the world of money and currencies and, on the other hand, differentiate them from virtual currencies - of which cryptocurrencies are the most significant representative. This research aims at tackling these questions by conducting an ontological analysis of money and related concepts, grounded in the Unified Foundational Ontology, based on a literature review of the most relevant economic theories, and considering recent innovations in the financial industry.

The goal of this paper is to demonstrate the feasibility of combining visualization and reasoning for business model design by combining the machine-interpretability of ontologies with a further development of the widely accepted business modeling tool, the Business Model Canvas (BMC). Since ontologies are a machine-interpretable representation of enterprise knowledge and thus, not very adequate for human interpretation, we present a tool that combines the graphical and human interpretable representation of BMC with a business model ontology. The tool connects a business model with reusable data and interoperability to other intelligent business information systems so that additional functionalities are made possible, such as a comparison between business models. This research follows the design science strategy with a qualitative approach by applying literature research, expert interviews, and desk research. The developed AOAME4BMC tool consists of the frontend, a graphical web-based representation of an enhanced BMC, a web service for the data exchange with the backend, and a specific ontology for the machine-interpretable representation of a business model. The results suggest that the developed tool AOAME4BMC supports the suitability of an ontology-based representation for business model design.

Business processes usually have distinct organizational stakeholders with contrasting concerns regarding it. The multitude of concerns often results in multiple business process models with dominant perspectives in the detriment of others which is common to see among the different departments of an organization such as Human Resources, Information Technology, Risk, and Auditing. To the best of our knowledge, there seems to lack of approaches that explore the generation of concern-based business processes to obtain consistent views shaped by departmental interests. Therefore, this paper fills a gap in addressing organizational stakeholders’ needs through concern-based business process decomposition and filtering of process activities applied over a consolidated business process model, expecting as an outcome the support and satisfaction of the complex and contrasting concerns of the distinct organizational departments.

Ecosystems have both inter-organizational process models and corresponding business value models. Whereas process models show how actors interact in terms of message flows and the time-ordering of activities, business value models present what is exchanged of economic value and abstract away from operational activities and their control flows. As organizations usually have process models but not explicit value models, we propose to derive a value model from a process model. We hypothesize on how these two models conceptually correspond to each other. We employ a real-world case to verify our hypotheses and learn about the applicability of the proposed design.

Cybersecurity is known as the practice of protecting systems from digital attacks. Organizations are seeking efficient solutions for the management and protection of their assets. It is a complex issue, especially for great enterprises, because it requires an interdisciplinary approach. The kinds of problems enterprises must deal with and this domain complexity induces misinterpretations and misunderstandings about the concepts and relations in question. This article focus on dealing with Cybersecurity from an ontological perspective. The first contribution is a search of previously existing works that have defined Cybersecurity Ontologies. The paper describes the process to search these works. The second contribution of the paper is the definition of characteristics to classify the papers of Cybersecurity Ontologies previously found. This classification aims to compare the previous works with the same criteria. The third contribution of the paper is the analysis of the results of the comparison of previous works in the field of Cybersecurity Ontologies. Moreover, the paper discusses the gaps found and proposes good practice actions in Ontology Engineering for this domain. The article ends with some next steps proposed in the evolution towards a pragmatic and iterative solution that meets the needs of organizations.

Management Science tries to enable managers and decision-makers to take the desired solutions to guide systems toward their objectives. This requires identifying the different dimensions of the system. Organizations and enterprises are complex systems associated with uncertainties in dynamic business contexts, that interact with their environments. Due to pressures such as collaborations with their customers, suppliers, their environment, the seek for innovations, etc., the performance may be changed by internal and external risks and opportunities that push and pull the enterprises like forces. Thanks to Physics of Decision (PoD), by identifying these pressures according to the organization’s features and objectives, unstable conditions due to the forces, can be detected and identified as risks and opportunities. This article attempts to present a time-dependent dynamic framework, based on a physical approach to identify risks and opportunities seen as forces applied on Organizations and Enterprises.

Increased digitalization and the pervasiveness of Big Data, along with vastly improved data processing capabilities, have led to the consideration of digital data as additional sources of system requirements, complementing conventional stakeholder-driven approaches. The volume, velocity and variety of these digital sources present numerous challenges which existing system development methods are unable to manage in a systematic and efficient manner. We propose a holistic and data-driven framework for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources for the purposes of requirements elicitation and management. The proposed framework includes a conceptualization in the form of a meta-model and a high-level process for its use; the framework is illustrated in a real case of an enterprise software.

Requirements elicitation, analysis and modeling are critical activities for software success. However, software systems are increasingly complex, harder to develop due to an ever-growing number of requirements from numerous and heterogeneous stakeholders, concerning dozens of requirements types, from functional to qualitative, including adaptation, security and privacy, ethical, acceptance and more. In such settings, requirements engineers need support concerning such increasingly complex activities, and Requirements Engineering (RE) modeling tools have been developed for this. However, such tools, although effective, are complex, time-consuming and requiring steep learning curves. The consequent lack of acceptance and abandonment in using such tools, by engineers, paves the way to the application of RE techniques in a more error-prone, low-quality way, increasing the possibility to have failures in software systems delivered. In this paper, we identify main areas of lack of acceptance, affecting RE engineers, for such tools, and propose an approach for making modeling tools more effective in engaging the engineer in performing RE in a tool-based way, receiving adequate feedback and staying motivated to use modeling tools. This is accomplished by performing acceptance requirements analysis (through the Agon Framework) and using gamification to increase the engagement of engineers during the usage of RE modeling tools. Towards this end, we performed a case study, within the VisiOn European Project, for enhancing a tool for modeling privacy and security requirements. Our case study provides preliminary evidence that our approach supports in making RE modeling tools more engaging from the engineer perspective.

Given the complexity and evolutionary nature of a System-of-Systems (SoS) entailing frequently changing goals and requirements, Extended Enterprise Collaboration (EEC) is needed. The idea behind EEC consists in teaming up with other stakeholders in partnerships in order to manage the skills, risks and investments required to rush the product to market. However, collaboration is far from being trivial, as it is constrained by several challenges such as heterogeneity of stakeholders and their ways of working - let alone global crisis like COVID-19. Thus, an overview of these challenges from both academic and industrial perspectives would be beneficial. With this in mind, based on a survey, this paper provides a comprehensive overview that goes beyond the challenges, to consider the benefits of, methods for, barriers and bridges to efficient collaboration. The survey was conducted among research-active academics in the field of SoS engineering, and companies in the US and European aerospace and defense industries. On the one hand, the results show that in response to COVID-19, work agreements (clear policies, commonly used processes and procedures, tools interoperability, clarity around roles and responsibilities of each collaborating firm, and proper change communication) are required in order to reduce uncertainty. On the other hand, it was revealed that “human bridges” are the backbone of EEC. It is, indeed, human factors that either impede or promote efficient collaboration. An important finding that comes in agreement with recent calls for shifting the focus from “technology-centered” to “human-centered” SoS engineering.

Process mining techniques provide data-driven visualizations that help gaining multi-perspective insights into business processes. These techniques build on a variety of algorithms, however without any explicit reference to the spectrum of potential analysis of operations. For this reason, it is unclear if the state of the art of process mining has missed opportunities to develop techniques that could be of potential value to an analyst. In this paper, we refer to research on information visualization where this problem has been addressed from a more general angle. More specifically, we use the framework defined for space-time cube operations to explore to which extent process mining instantiates these operations. To this end, we refer to most widely used commercial process mining tools and analyze their analysis operations. We find that the majority of the operations are already supported by the tools, but there are still unsupported ones, which exhibit opportunities for future research and tool innovation.