TY - CHAP A1 - Rojahn, Marcel A1 - Gronau, Norbert T1 - Digital platform concepts for manufacturing companies BT - a review T2 - 10th International Conference on Future Internet of Things and Cloud (FiCloud) N2 - Digital Platforms (DPs) has established themself in recent years as a central concept of the Information Technology Science. Due to the great diversity of digital platform concepts, clear definitions are still required. Furthermore, DPs are subject to dynamic changes from internal and external factors, which pose challenges for digital platform operators, developers and customers. Which current digital platform research directions should be taken to address these challenges remains open so far. The following paper aims to contribute to this by outlining a systematic literature review (SLR) on digital platform concepts in the context of the Industrial Internet of Things (IIoT) for manufacturing companies and provides a basis for (1) a selection of definitions of current digital platform and ecosystem concepts and (2) a selection of current digital platform research directions. These directions are diverted into (a) occurrence of digital platforms, (b) emergence of digital platforms, (c) evaluation of digital platforms, (d) development of digital platforms, and (e) selection of digital platforms. Y1 - 2023 SN - 979-8-3503-1635-3 U6 - https://doi.org/10.1109/FiCloud58648.2023.00030 SP - 149 EP - 158 PB - IEEE CY - [Erscheinungsort nicht ermittelbar] ER - TY - CHAP A1 - Rojahn, Marcel A1 - Gronau, Norbert ED - Bui, Tung X. T1 - Openness indicators for the evaluation of digital platforms between the launch and maturity phase T2 - Proceedings of the 57th Annual Hawaii International Conference on System Sciences N2 - In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation. KW - federated industrial platform ecosystems KW - technologies KW - business models KW - data-driven artifacts KW - design-science research KW - digital platform openness KW - evaluation KW - morphological analysis Y1 - 2024 SN - 978-0-99813-317-1 SP - 4516 EP - 4525 PB - Department of IT Management Shidler College of Business University of Hawaii CY - Honolulu, HI ER - TY - CHAP A1 - Grum, Marcus A1 - Blunk, Oliver A1 - Rojahn, Marcel A1 - Fettke, Peter A1 - Gronau, Norbert T1 - Research challenges of knowledge modelling and the outline of a research agenda T2 - Knowledge in digital age : IFKAD 2020 KW - knowledge management KW - process modelling KW - research challenges Y1 - 2020 SN - 978-88-96687-13-0 SN - 2280-787X PB - The Arts of Business Institute CY - Matera, Italy ER - TY - CHAP A1 - Grum, Marcus A1 - Thim, Christof A1 - Roling, Wiebke A1 - Schüffler, Arnulf A1 - Kluge, Annette A1 - Gronau, Norbert ED - Masrour, Tawfik ED - El Hassani, Ibtissam ED - Barka, Noureddine T1 - AI case-based reasoning for artificial neural networks T2 - Artificial intelligence and industrial applications N2 - Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level. KW - case-based reasoning KW - neural networks KW - industry 4.0 Y1 - 2023 SN - 978-3-031-43523-2 SN - 978-3-031-43524-9 U6 - https://doi.org/10.1007/978-3-031-43524-9_2 VL - 771 SP - 17 EP - 35 PB - Springer CY - Cham ER - TY - CHAP A1 - Grum, Marcus A1 - Rapp, Simon A1 - Gronau, Norbert A1 - Albers, Albert ED - Shishkov, Boris T1 - Accelerating knowledge BT - the speed optimization of knowledge transfers T2 - Business modeling and software design N2 - As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation. KW - knowledge transfers KW - business process optimization KW - interventions KW - product development KW - product generation engineering KW - empirical evaluation Y1 - 2019 SN - 978-3-030-24853-6 SN - 978-3-030-24854-3 U6 - https://doi.org/10.1007/978-3-030-24854-3_7 VL - 356 SP - 95 EP - 113 PB - Springer CY - Cham ER -