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Auflistung nach Schlagwort "decision support systems"

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  • Konferenzbeitrag
    Can algorithms help us manage dairy cows?
    (41. GIL-Jahrestagung, Informations- und Kommunikationstechnologie in kritischen Zeiten, 2021) Cockburn, Marianne
    Digitalisation has reached agricultural production and specifically dairy farming, where a wide range of sensing technologies are now available. From farm management systems over body condition scoring systems to those that detect behavioural changes. All these systems have one aim: to offer decision support to the farmer and aid his management decisions. Currently, however, little is known about the return of investment that these systems offer, or even the effectiveness of their functionality. Only little information is available about the underlying algorithms, despite them presenting the essence of performance. Thus, we can only consider the published literature to get an impression of such systems’ outcome. In the current study, we therefore evaluated machine-learning related studies published in the scientific literature between 2015 and 2020. We found that machine-learning algorithms were implemented across all fields of dairy science, but only a minority of them could reliably aid management decisions in practice. In this publication, we aim to give an overview of the achievements of current machine-learning algorithms published in dairy science literature and give an outlook on how they could develop further in the future.
  • Konferenzbeitrag
    Do not disturb! trust in decision support systems improves work outcomes under certain conditions
    (Mensch und Computer 2020 - Tagungsband, 2020) Müller, Lea S.; Meeßen, Sarah M.; Thielsch, Meinald T.; Nohe, Christoph; Riehle, Dennis M.; Hertel, Guido
    Organizations provide their employees with decision support systems (DSS) to facilitate successful decision making. However, the mere provision of a DSS may not be sufficient to facilitate beneficial work outcomes because employees often do not rely on a DSS. Therefore, we examined whether users’ trust in a DSS increases positive effects of DSS provision on several core work outcomes (i.e., performance, well-being, and release of cognitive capacities). Moreover, we examined whether trust effects on these work outcomes depend on specific context conditions (i.e., user accountability, distraction, and market dynamics). We tested our hypotheses in a laboratory experiment with N = 201 participants who received assistance by a DSS in a simulated sales planning scenario. In line with our assumptions, trust in the DSS was positively related to users’ performance and well-being. Moreover, the link between trust and strain as well as release of cognitive capacities were qualified by distraction, so that higher distraction diminished these links. No such moderation occurred for user accountability and market dynamics.
  • Textdokument
    Towards Designing a User-centric Decision Support System for Predictive Maintenance in SMEs
    (INFORMATIK 2021, 2021) Kellner, Domenic; Lowin, Maximilian; von Zahn, Moritz; Chen, Johannes
    In manufacturing, small and medium-sized enterprises (SMEs) face global competition. In the field of predictive maintenance (PdM), artificial intelligence (AI) helps to prevent machine failures and has the potential to significantly reduce costs and increase process efficiency. Even though PdM has several benefits, it also entails considerable challenges for SMEs, especially when it comes to user interactions. In this short paper, we harness the design science methodology and discuss several problems regarding user interactions with predictive maintenance applications. We incorporate two different literature streams, namely, predictive maintenance and decision support systems. Finally, we present necessary design requirements, principles, features, and propose a research design to further develop and evaluate a user-centric PdM decision support system. Thereby, we contribute to making AI tangible in SMEs.