http://dl.gi.de/handle/20.500.12116/20086 Thu, 23 Jul 2026 23:29:30 GMT 2026-07-23T23:29:30Z http://dl.gi.de/handle/20.500.12116/20116 A Domain-specific Modeling Technique for Value-driven Strategic Sourcing Rafati, Laleh; Roelens, Ben; Poels, Geert Strategic sourcing recognizes that procurement should support a firm’s effort to achieve its long-term objectives. In particular, procurement needs to be a cross-functional end-to-end process inside the organization that is oriented towards value creation within the company and between the company and its partners in the value chain. The main challenge to the implementation of value-driven strategic sourcing is the lack of instruments that are characterized by analytical rigor and robustness in the identification of strategic sourcing options to achieve strategic goals. Therefore, this research aims to develop a domain-specific modeling technique founded on the Service-Dominant Logic which focuses on the systemic exploration of sourcing alternatives and emphasizes the delivery of value to achieve desired outcomes. This paper reports on a first cycle of Design Science Research which includes the demonstration and the evaluation of the value and utility of the modeling artefacts by means of a case study about IT outsourcing in the healthcare industry. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/20116 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/20117 An Approach to Flexible Multilevel Modelling Macías, Fernando; Rutle, Adrian; Stolz, Volker; Rodriguez-Echeverria, Roberto; Wolter, Uwe Multilevel modelling approaches tackle issues related to lack of flexibility and mixed levels of abstraction by providing features like deep modelling and linguistic extension. However, the lack of a clear consensus on fundamental concepts of the paradigm has in turn led to lack of common focus in current multilevel modelling tools and their adoption. In this paper, we propose a formal framework, together with its corresponding tools, to tackle these challenges. The approach facilitates definition of flexible multilevel modelling hierarchies by allowing addition and deletion of intermediate abstraction levels in the hierarchies. Moreover, it facilitates separation of concerns by allowing integration of different multilevel modelling hierarchies as different aspects of the system to be modelled. In addition, our approach facilitates reusability of concepts and their behaviour by allowing definition of flexible transformation rules which are applicable to different hierarchies with a variable number of levels. As a proof of concept, a prototype tool and a domain-specific language for the definition of these rules is provided. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/20117 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/20115 Supporting the Model-Driven Organization Vision through Deep, Orthographic Modeling Tunjic, Christian; Atkinson, Colin; Draheim, Dirk In a model-driven organization, all stakeholders are able to deal with information about an organization in the way that best supports their goals and tasks. In other words, they are able to select models of the organization at the optimal level of abstraction (e.g. platform independent) in the optimal form (e.g. graph-based) and with the optimal scope (e.g. a single component). However, no approach exists today that seamlessly supports this capability over the entire life-cycle of organizations and the IT systems that drive them. Enterprise architecture modeling approaches focus on supporting model-based views of the static architecture of organizations (i.e. enterprises) but generally provide little if any support for operational views. On the other hand, business intelligence approaches focus on providing operational views of organizations and usually do not accommodate static architectural views. In order to fully support the model-driven organization (MDO) vision, therefore, these two worlds need to be unified and a common, natural and uniform approach for defining and supporting all forms of views on organizations, at all stages of their life-cycles, needs to be defined and implemented in an efficient and scalable way. This paper presents a vision for achieving this goal based on the notions of deep and orthographic modeling. After explaining the background to the problem and introducing these two paradigms, the paper presents a novel approach for unifying them, along with a prototype implementation and example. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/20115 2018-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/20113 Semantic Annotations of Enterprise Models for Supporting the Evolution of Model-Driven Organizations Fill, Hans-Georg In model-driven organizations enterprise models are used to represent and analyze the current and future states of aspects such as strategies, business processes or the enterprise architecture. Thereby, the scope of representation and analysis depends on the used modeling method. Although adaptations of modeling methods are frequently conducted to meet requirements emerging from business changes, such modifications may not be favorable due to potential side effects on other enterprise systems, e.g. through inconsistencies with existing standards and resulting conflicts in algorithmic processing. In the paper at hand we therefore propose the use of semantic annotations of enterprise models for dynamically extending the representation and analysis scope of enterprise modeling methods. Through a loose-coupling between enterprise models and formal semantic schemata, additional information can be represented and processed by algorithms without changes in the original modeling language. In this way, the evolution of information requirements of an organization can be satisfied while maintaining the consistency of the used enterprise modeling languages. For illustrating the feasibility of the approach we describe a use case from the area of risk management. The use case is realized using the SeMFIS platform that supports the annotation of enterprise models and the subsequent machine-based analysis of annotations. Mon, 01 Jan 2018 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/20113 2018-01-01T00:00:00Z