The paper introduces a new concept – discovery power – that can be used to characterize an enterprise modeling language. The concept is different from, but connected to, the concept of expressive power. The concept is defined as “the degree of help provided by the structure of an enterprise modeling language to expand a partly built model or fill gaps in it”. The paper also suggests a way of evaluating the discovery power of enterprise modeling languages.
Model-Driven Development (MDD) is considered an effective technique for Internet of Things (IoT) application development. Our observation is that existing model-based approaches for IoT solutions focus on the software and systems perspective and show a need for more integration with organizational and business model aspects. Therefore, we developed a method and tool support for developing IoT applications in the field of air conditioning facilities. In this work, we applied quality criteria to evaluate the included Domain-Specific Modeling Language (DSML). To practically validate the modeling language as such and also the way it can be used and supported by the tool, we performed a real-world use case. The main contributions of this paper are a quality evaluation of the DSML and the tool support and lessons learned from both.
Traceability is a central concept in the domain of Enterprise Engineering. Indeed, the controlled transformation of the more business-oriented requirements layers to designs and runtime code has been studied over the years in domains such as IT Governance, Enterprise Architecture, and Software Development. However, traceability on the entire flow from business to IT is often lacking in complex projects in industry, thereby hampering the agility, productivity, and resilience of organizations in the digital era. Consequentially, organizations often fail to realize IT business value. Therefore, we investigate how the traceability issue in IT can be defined, and to what extent traceability issues still exist in practice based on 28 interviews with 16 interviewees from various organizations, differing in industry, size, and geographical location. Results indicate that opinions on the value and importance of traceability vary greatly, along with the views on how realistic it is to establish traceability in practice. Also, available theories and frameworks do not seem to be applied in many cases, even though they are not completely unknown to the interviewees. Clearly, there is a gap between theory and practice regarding traceability. The reasons for this are not entirely clear from this research. However, we call for new, holistic, and integrated research on traceability across multiple domains, highlighting the multidimensionality of the traceability issue in IT, and fostered in the Enterprise Engineering community.
In enterprise data management, the development of APIs for integrating diverse information systems often entails repetitive and labor-intensive tasks, such as translating variables and methods between systems. The advent of low-code platforms has significantly altered this landscape, facilitating the automatic and swift generation of APIs for both incoming and outgoing data and service actions. This paper explores a new approach using the Design and Engineering Methodology for Organizations (DEMO) data models within a low-code platform. Our methodology simplifies the API creation process by using DEMO’s Fact and Action models. Using a low-code platform, we enable users to efficiently generate endpoints for various functionalities, ranging from basic data item lists to complex query results, all achieved through intuitive drag-and-drop operations within a user-friendly graphical interface. This approach not only streamlines the development of APIs for internal tasks but also eases integration with external systems. Moreover, our approach includes the automated scanning of data from external APIs. Utilizing a user-friendly GUI, our system can automatically retrieve data from external sources and align it with internal data, ensuring consistent integration. This paper details this approach, emphasizing its effectiveness in integrating external information into local systems.
The Marispace-X project aims to create a digital ecosystem of providers and users of data from the oceans. For this purpose, different use cases (munitions detection, biological climate protection, the construction of offshore wind platforms and the testing of IoT sensors) are used as examples to model the benefits of the planned ecosystem. One of the main focuses of the project is to analyze and build emerging business models. Most research in this sector is focussed on singular business models, while industry specific business models are underrepresented. The problems in building such an ecosystem are not only technical, but also social in nature. Therefore, this work aims to illustrate how academia and industry can work together to address problems and develop possible solutions for Digital Business Ecosystems (DBE). This conjunction of problems, i.e., the view of an industry domain as a business model and the view of digital business ecosystems, motivate this research. This work shows how to start building an ecosystem or platform and how to sensitize relevant stakeholders to existing challenges in designing ecosystems. Furthermore, some solution approaches are presented to address sub-problems in business model design, especially in the area of value creation through data. Lessons learned and further open challenges can be found at the end of the paper. The contributions of this paper are: (1) an emerging digital business ecosystem with different roles, (2) a problem/goal model for science-communication with relevant stakeholders, (3) the application of an analysis tool for business models with a FAIR-Data Value Chain, (4) remaining challenges, especially in the areas of ecosystem governance and modelling.
The emergence of the new business context is creating innovative systems and models of work, having a deep impact on business environment and organizational interdependencies. In many modern cases such systems consist of a network of business agents which are autonomous in their behavior, believes and values, but should maintain interaction between each other in order to enhance overall innovation capacity and competitiveness. Distributed and cooperative multi-agent networks should be considered as an inimitable asset and collaborations and networks encompass a broad range of inter-organizational or even inter-personal relationships. These forms of conducting business activity are characterized by a low volume of transaction costs and the grade of mutual trust between the agents represents the crucial factor for its formation. This work applies the concept of multi-agent simulations to study impact of alternative trust management strategies on the overall effectiveness of the enterprise. A real case of the software freelance market is used for simulation experiments based on the NetLogo multi-agent platform. Analysis of simulations shows that cognitive trust management strategies give advantage even in environments with a high level of information noise. Developed multi-agent decentralized algorithms were generalized and can be applied for automation of routine tasks in other domains where trust management in networks of cooperative agents plays an important role.
Collecting, processing and utilizing data information plays an increasingly important role in data warehousing/business intelligence (DWH/BI) systems. Currently, these systems are predominantly described using natural text, informal diagrams, or UML (Unified Modeling Language) diagrams. These potentially imprecise methods for describing new or complex DWH/BI topics can in combination with variations in naming and interpretations of concepts between different authors lead to miscommunication issues. This paper shows the possibility and benefits of using a more formal approach in documenting and describing such systems or their parts, which results in a more precise form of communication between researchers or industry experts. The conceptual analysis of selected parts of the DWH/BI domain is done using the OntoUML modeling language.
Digital ecosystems and platforms require a business model, which is a model of how a business creates, delivers, and captures value. We argue that the business model should be a networked business model, as ecosystems and platforms are connected networks of organizations and consumers. Furthermore, we emphasize that a business model should be a conceptual model that is expressed using a (semi) formal language. This is not only needed in order to create an unambiguous and shared understanding of the ecosystem at hand; it is also a prerequisite for software-assisted analysis and for the use of other design techniques, e.g. for business process engineering, and ICT architecture design. We explain these two requirements concerning business modelling using a series of industry strength cases.
An overview of the Dynamic Information System Modeller and Executor, an enterprise engineering, DEMO based, open source, low code platform with an Adaptive Object Model approach for workflow management in organizations. An alternative take, seeing an organization as a living organism, thus providing the tools for instant change in the processes to immediately reflect on their execution. We detail the multiple components of the System Modeller and how they interconnect with each other to produce the system executor that can be used by users for their day-to-day workflow in their organizations.
The logistics sector experiences an increasing shortage of secure truck parkings, in part due to lacking insight into occupancy rates. Extending earlier work – in which a variety of machine learning approaches are evaluated to predict truck parking occupancy – this paper presents a reference use case, data space architecture, and prototype for smart truck parking. The research builds upon case study research and 1.5 years’ worth of real-world data of a truck parking in Deventer, the Netherlands. Deploying the conceptual design from prior work, the main contributions of this paper are: (1) a reference use case, (2) a data space architecture, and (3) a prototype for smart truck parking. The reference use case is developed following the use case methodology of Fraunhofer. The architecture is rooted in the international data spaces reference architecture model, modelled using the ArchiMate language and specification, and validated by means of expert opinion. The cross industry standard process for data mining is utilized to develop and deploy a prototype, based on a single case mechanism experiment, for a smart truck parking application. Future research can focus on evaluating and developing ontologies to extend the current conceptual representation of the data objects and the experimental development of federative machine learning approaches among truck parkings.
The study is aimed at determining the optimal strategy for the development of the company in terms of increasing the number of loyal customers using multi-agent modeling methods. The constructed model takes into account not only the main characteristics of the company's customers, but also a set of market tools available to the company at various stages of its existence to attract customers. The multi-agent model developed using the platform NetLogo can also be applied in other domains characterized by a high concentration of analogous (competing) entities (agents, goods, companies).
Data-driven architecture is a new paradigm promoted by LeanIX that focuses on making Enterprise Architecture accessible to a wider audience of stakeholders in organizations, to increase data quality and provide transparency when undergoing organizational transformations. This democratization of Enterprise Architecture allows organizations to transform faster and take advantage of trends such as the API Economy, Software-as-a-Service, and Citizen Developers with low-code applications. From this perspective, in this paper, we present the most common challenges that organizations face in their Enterprise Architecture practice due to siloed information and lack of communication between stakeholders. Furthermore, using tools like Excel and PowerPoint to manage the architecture poses challenges due to obsolete data, inability to create meaningful analyses and having multiple sources of truth. In the case of C&A, Helvetia and Marc O'Polo we present how having a modern data-driven Enterprise Architecture has helped these organizations with addressing their challenges and transforming quickly. Finally, we present our ideas on avenues for advancing the field of Enterprise Architecture from a research and practice perspective.
The production of self-executing computational agreements in the form of smart contracts remains a manual coding endeavour that hinders the widespread adoption of solutions that run on top of a distributed ledger technology such as blockchain. We explore the automatic generation of smart contracts based on a visual composition of reusable action rule specifications and other elements from the action model of the DEMO methodology. Several design and implementation considerations entail this choice of SC generation, all of which motivated a smart contract-enabled extension of DEMO's way of modelling. The main research contribution is a foundation of synergistic knowledge accompanied by an extension proposal involving DEMO and smart contracts that can be built upon in future business cases where enterprise interoperability supported by blockchain technology is a requirement.
In Enterprise Engineering (EE), like other fields, visual information has several advantages over text and we are used to represent a process model as visual information. However, little is known about the contributing factors that influence the understanding of these process models. Visual literacy and nudges are two potential contributing factors to the ability to understand process models and reduce clutter. In this study, we researched by using an online questionnaire (N = 37) whether we could enhance the understanding of process models with the help of nudges, visual literacy and the interaction effects between the nudges and visual literacy. We executed a univariate ANOVA to compare the effect of the nudges on the understanding of process models with the two nudge conditions, visual literacy and all possible interactions as predictors. The results showed that nudges do not significantly influence the understanding of process models, which was not in line with our expectations. Visual literacy may have a significant influence, which aligns with our expectations. There was one significant interaction between visual literacy and the arrow nudge; however, not in the direction that we expected, therefore, not in line with our expectations. Given our small sample size, our significance could rest on a coincidence. We offer no open-and-shut conclusions about enhancing the understanding of process models with the help of nudges, visual literacy and the interaction effects between the nudges and visual literacy.
We consider current Design and Engineering Methodology for Organizations (DEMO) Action Rules Specification to be unnecessarily complex and ambiguous. Even while using a "structured English" syntax similar to the one used in Semantics of Business Vocabulary and Business Rules (SBVR), such specifications are: incomplete while not containing enough ontological information to derive a functional implementation; and complex by containing mostly unneeded specifications. We propose a new meta-model for DEMO's Action Model in the form of an Extended Backus–Naur Form (EBNF) syntax which is being implemented in a prototype that directly executes DEMO models as an Information and Workflow System. This prototype includes an action engine that runs DEMO transactions and the enclosed actions specified in our approach. We are currently integrating Blockly in our solution to allow syntactically correct visual programming of our proposed new Action Rule language that includes constructs to evaluate logical conditions, update the state of internal or external information systems, obtain input and provide output (formatted with a 'What You See Is What You Get' (WYSIWYG) template editor) to users, among others.
The core methodology of Enterprise Engineering (EE) is Design and Engineering Methodology for Organisations (DEMO) and has been the subject of modelling tools. This methodology can be split into a method or process part and a notation part, describing the meta-model and its visualisation. The way the notation of the methodology has been described for these tools has been of different detail levels. This paper describes the DEMO notation using the grammar of the Simplified platform as an exercise towards a complete notation grammar that can describe all existing and possibly future notations and also to complete the DEMO notation specification. The grammar is part of the Simplified platform, and the notation is the published definition of the notation part of the DEMO methodology. We have chosen a practical approach to developing the notation script and thinking out-of-the-box by not creating a theoretical box a priori.
Digital transformation has resulted in the availability of more data in higher quality and business models aiming at exploiting them, both on the level of individual enterprises and digital ecosystems. Among the essential elements of business models are the value offering made to target groups and the value creation required for this. Using the example of the maritime dataspace MARISPACE-X, the paper investigates an approach to support business model development combining data value chains with data sovereignty based on the FAIR principles as differentiating feature. The contributions of this paper are (1) an innovative dataspace as example case for business model development, (2) an approach to integrate FAIR principles into data value chains, and (3) analysis of existing literature in the field.
Producers of manufacturing equipment can, instead of just selling their products, also offer their customers services to increase customer satisfaction, gain competitive advantage, and increase their profits. These goals can be reached by helping the customers optimise their processes and improve their reliability and flexibility. This can be done by supporting the customer that invests in new manufacturing machines with a planning and control tool connecting the machines and the processes between them. More specifically, this will become possible by introducing and integrating active data management and analysis, and planning applications in the current architecture of companies. All of the processes currently being done manually in the customer companies, from monitoring to production planning based on direct observation and the experience of production managers, can be automated using these applications. This paper presents a reference architecture supporting the connection of these processes using the mentioned applications, and validates the developed models based on a real case study of a production machine manufacturer and its customers.
Enterprises are fast evolving into hyperconnected ecosystems that need to continue meeting the stated goals in a dynamic environment that changes along multiple dimensions. Ability to respond to these changes in an ever-shrinking window of opportunity is a critical need that has remained unfulfilled. With enterprises increasingly reliant on software systems, these need to be amenable to quick adaptation in order to effect these decisions. We propose an approach aimed at meeting these needs in a holistic, automated, and human-in-loop manner. The proposed approach builds further upon proven ideas from Modelling & simulation, Artificial Intelligence, and Control Theory, and is validated in part on real-world industry-scale problems.
