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The nature of modern organizations needs to be increasingly adaptive, since they are dealing with a constant demand to respond to stimuli derived from the dynamic environments they operate in. Changing their capabilities is a common response, and this makes capability management a vital aspect of organizational survivability. To date, there are no approaches specifically designed to address this specific situation. KYKLOS and Compass are two interlinked approaches of different complexity, a DSML and a canvas, developed to support capability change. As recently developed methods, they lacked formal demonstration and evaluation; therefore, the goal of this article is to present the demonstration and evaluation of the two approaches by their stakeholders, in particular, business and modeling experts. A case study in a Swedish company in the ERP system consulting domain that is undergoing changes in its sales and consulting capabilities related to evolving customer requirements has been used to demonstrate and evaluate the two approaches. The process consisted of two evaluation cycles. The first cycle concerned KYKLOS and used two categories of evaluators, the business experts and the modeling experts. While the modeling experts evaluated positively the method, the business experts had difficulties associated with its ease of use and adoption. This resulted in the development of Compass, which was evaluated by business experts during the second evaluation cycle. Compass was evaluated more positively in terms of the difficult aspects, but the challenge is ongoing and motivates further future research. [ABSTRACT FROM AUTHOR]

Due to hyper-competition, technological advancements, regulatory changes, etc, the conditions under which enterprises need to thrive become increasingly turbulent. Consequently, enterprise agility increasingly determines an enterprise's chances for success. As software development often is a limiting factor in achieving enterprise agility, enterprise agility and software adaptability become increasingly intertwined. As a consequence, decisions that regard flexibility should not be left to software developers alone. By taking a Model-driven Software Development (MDSD) approach, starting from DEMO ontological enterprise models and explicit (enterprise) implementation design decisions, the aim of this research is to bridge the gap from enterprise agility to software adaptability, in such a way that software development is no longer a limiting factor in achieving enterprise agility. Low-code technology is a growing market trend that builds on MDSD concepts and claims to offer a high degree of software adaptability. Therefore, as a first step to show the potential benefits to use DEMO ontological enterprise models as a base for MDSD, this research shows the design of a mapping from DEMO models to Mendix for the (automated) creation of a low-code application that also intrinsically accommodates run-time implementation design decisions. [ABSTRACT FROM AUTHOR]

This issue of CSIMQ includes five articles selected on the topics of complexity management and knowledge management in enterprises from various perspectives and towards diverse goals: the economic and societal perspective seeking strategy fulfillment with the help of enterprise architecture management, the operational performance perspective, which requires grounding management decisions on data insights, and the human aspect perspective to facilitate collaboration, creativity and innovation. Methodologically, the issue reports a mix of conceptual modeling and analytics approaches, suggesting an emerging requirement to balance or alternate, in complexity management, abstraction-based analysis, and data-oriented analytics.

This article presents a new meta-modeling approach for intangible resources. The method combines Enterprise Modeling (EM) and the resource value innovation model from the knowledge management (KM) field. In particular, fractal enterprise modeling (FEM) language has been used for constructing the intangible resources’ level of development. The practical application of the proposed method deploys a real case example in the strategic analysis of the organizational change within the product innovation activities. The results imply that the proposed method has a good potential to incorporate the intangibles into EM. First, it has been possible to represent the intangible resources’ level of development. Second, the FEM models produced in the study have been useful in the analysis of how tacit knowledge is acquired and appropriated within the organization’s strategic capability building. The study resulted in producing modeling and analyzing patterns that can be reused in similar situations. The research followed the Design Science Research (DSR) methodology.

Established companies intending to leverage digital technologies are required to innovate their 'legacy' business models through organizational transformations. Existing modeling support often leaves a 'white space' between informal canvas-style models used in the early phases and (semi-)formal aspect models used in the later phases of transformation endeavors. Adopting a Design Science Research approach, this paper presents a semi-formal model that is intended to fill this gap, i. e., to provide easy-to-use support for the heterogeneous stakeholders that participate in early phases of digital transformation endeavors. Being based on the traditional Business Engineering set of models and methods, this comprehensive and collaborative approach was validated together with the digital transformation program manager of a large, international corporation. For supporting analysis, reflection and design tasks that involve a broad range from canvas-style models to enterprise architecture models, four requirements were identified to be central: (remote) collaboration support, a holistic and integrative perspective, an enterprise-level view, and a focus on change. The actual model is comprised of over 20 partial models including popular canvases depicting the transformation program's content on the strategy-to-IT layers, the enterprise and local level, and in the as-is and to-be state. Demonstration and evaluation were done with practitioners and students of an Executive Master program focusing on digital transformation. Both confirmed the utility of the underlying method and recognized its distinctive features, while capability and IT landscape models were found especially relevant. The method is expected to be applicable for digital transformations beyond the case and also to be projectable to smaller-scaled digital business innovations. [ABSTRACT FROM AUTHOR]

Models allow us to simplify reality and give advantages to both decomposition and abstraction. Models can have various forms from textual, tabular, mathematical, and graphical to a combination of these formats. Formal models can be processed, or even executed, by machines. An engineering model must satisfy such characteristics as abstraction, understandability, accuracy, predictiveness, and inexpensiveness. Models explicitly represent knowledge of the modeled domain in a form suitable for reasoning about them and learning. Knowledge may be descriptive, structural, procedural, meta-, or heuristic. Focus on one type of knowledge during the analysis may ignore the other one. Moreover, analysis and reasoning also rely on data representation forms which may lose accuracy due to simplification and different assumptions. Therefore, completeness, correctness, and adequacy of knowledge as well as particularities of the representing structure may affect the results of knowledge processing and decision making. Therefore, the capability of models (and other structures) to represent knowledge completely, adequately, and accurately is still a matter of various research activities. This issue of CSIMQ is devoted to this matter.

This guest editorial presents the special section of 14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling (PoEM 2021). The best papers of PoEM 2021 were invited to be revised and significantly expanded. Eight papers were finally accepted for publication in the special section. These papers are an excellent representation of the state of the art on Enterprise Modeling, showing as well the importance of applying the research contributions into practice. [ABSTRACT FROM AUTHOR]

Though modeling tools are developing fast, today, enterprise modeling is still a highly manual task that requires substantial human effort. Today, human modelers are not only assigned the creative component of the process, but they also need to perform routine work related to comparing the being developed model with existing ones. Larger amount of information available today makes it possible for a modeler to analyze more information and existing models when developing own models. However, it also complicates the process since the modeler is often not able to analyze all of them. In this work, we discuss the potential of the novel idea of using machine learning methods for enterprise modeling assistance that would benefit from their ability to discover tacit knowledge/regularities in the available data. Graph neural networks have been chosen as the main technique. The contribution lies in the proposed modeling assistance scenarios as well as carried out evaluation of the potential benefits for the modeler. The presented illustrative case study scenario is aimed to demonstrate the feasibility of the proposed approach. The viability and potential of the idea are proved via experiments. [ABSTRACT FROM AUTHOR]

Due to their unique characteristics, knowledge-intensive processes (KiPs) are difficult to capture with conventional modeling and management approaches. One such KiP is the advanced therapy medicinal product (ATMP) development process. ATMPs are highly innovative medicinal products that are based on biomedical technology. ATMP development processes need to comply with complex regulatory frameworks. Currently, biomedical scientists that develop ATMPs manage the regulatory aspects of the ATMP development processes in an ad hoc fashion, resulting in inefficiencies such as reworks or even withdrawal of ATMPs from the market. This paper presents an explorative case study in which we use Enterprise Modeling and Context-aware Business Processes to support ATMP scientists in managing the regulatory aspects of ATMP development processes more efficiently and effectively. In our explorative case study, we use enterprise models to describe the important concepts and views in ATMP development processes. By introducing context-awareness to the models, we support ATMP scientists in performing relevant tasks to address the regulatory requirements efficiently and effectively under different contexts. We introduce the novel concept of execution-dependent dynamic context to properly define the context in ATMP development processes. Additionally, this paper takes a broader perspective on the case study by discussing the relevance of the solutions derived for the case study for other KiPs. Thereby this paper aims to present an exemplary approach for context-aware modeling of KiPs. The practical contribution of this paper are the models realized in a real-life ATMP development project. The scientific contribution of this paper is providing an exemplary approach for supporting knowledge workers who perform flexible, KiPs under dynamic contexts and introducing the notion of execution-dependent dynamic context. [ABSTRACT FROM AUTHOR]

Enterprise models have the potential to constitute a valuable asset for organizations, e.g., in terms of enabling a variety of analyses or by fostering cross-organizational communication. Therefore, while designing an enterprise modeling method one needs to ensure that created enterprise models are of good quality in terms of: (1) syntactic validity, which entails that a model adheres to syntactic rules encoded in the underlying modeling language, (2) semantic validity, i.e., that the model should make sense in its context of use, and (3) pragmatic validity, i.e., that the model should effectively and efficiently serve the intended purpose. To ensure these three validity types, verification and validation (V &V) techniques need to be exploited while designing the enterprise modeling method, e.g., to check created enterprise models against syntactic rules, or to ensure intra- and inter-model consistency. This paper targets the systematic embedding of V &V techniques into the engineering of (enterprise) domain-specific modeling methods (DSMMs). Specifically, after identifying and analyzing existing DSMM engineering approaches, we synthesize their elements (such as typical phases and steps) and enrich them with V &V techniques. This paper is an extension of our previous work and additionally contributes (1) a systematic analysis of a wider set of existing approaches to DSMM engineering, (2) an extended background that covers information on models, modeling languages and modeling methods, (3) additional details regarding selected validation and verification techniques for each phase, and finally (4) a road-map encompassing desiderata for further advances in V &V in domain-specific modeling method engineering, from the perspectives of practice, research and education. [ABSTRACT FROM AUTHOR]

The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach. [ABSTRACT FROM AUTHOR]

The changing business landscapes urge organizations to collaborate and combine their expertise to stay competitive. Organizations establish partnerships and collaborate via the Internet, which often happens dynamically and at fast pace resulting in formation of Digital Business Ecosystems (DBEs). DBEs are complex and their management requires having explicit and up-to-date information about them. Modeling enables thorough visual analysis and facilitates the understanding and formation of DBEs. It also allows viewing DBEs through multiple perspectives, as well as exploring alternatives in the course of DBE formation or management. This systematic review aims to synthesize existing studies pertaining to Conceptual Modeling for analysis, design, and management of DBEs. A total of 94 studies were included in the review. The findings suggest that there is a scarcity of existing Conceptual Modeling methods and tools supporting DBEs. Additionally, the extensive emphasis on DBEs’ actors in modeling leads to an urgent need for the methods to be extended to support the establishment of holistic views for integrating multiple perspectives of DBEs. Future research should focus on these areas to facilitate the transformation of how organization’s collaborations are viewed – from a single-organization to a multitude of viewpoints on organizational networks of collaboration, coexistence, and competition. Such models also need to support the key features of DBEs, such as resilience and automation.