Fraunhofer-Institut für Angewandte Informationstechnik FIT

Filters
4
Reset filters

Settings
Now showing 1 - 4 of 4
  • Publication
    Modeling IT Availability Risks in Smart Factories. A Stochastic Petri Nets Approach
    ( 2020)
    Miehle, Daniel
    ;
    Häckel, Björn
    ;
    Pfosser, Stefan
    ;
    Übelhör, Jochen
    In the course of the ongoing digitalization of production, industrial production infrastructures have become increasingly intertwined with information and communication technology. There-by, physical production processes depend more and more on the flawless functioning of information networks. Threats, such as attacks and errors, can compromise the components of in-formation networks, and due to the increasing interconnection, can even cause entire smart factories to fail. However, increasing complexity and lack of transparency of information networks in smart factories complicate the detection and analysis of such threats. Following a De-sign Science Research approach, this study aims to develop a methodology to depict and to model information networks in smart factories to enable the identification and analysis of IT availability risks. Based on a modular stochastic Petri net approach, we provide an artifact that enables the simulation and analysis of threats in smart factory information networks. To demonstrate the applicability and feasibility of our approach, we investigate different threat scenarios regarding their impacts on the operational capability of a close-to-reality information network setting. Further, to complement the evaluation from a practical perspective, we integrated the insights from two expert interviews with two global leading companies in the automation and packaging industry. The results indicate that the developed artifact offers a promising approach to better analyze and understand IT availability risks in smart factory information networks.
  • Publication
    Mindful engagement in emerging IT innovations - a dynamic optimization model considering organizational learning in IT innovation investment evaluation
    ( 2017)
    Häckel, Björn
    ;
    Lindermeir, Andreas
    ;
    Moser, Florian
    ;
    Pfosser, Stefan
    Companies regularly have to decide whether, when, and to what extent to invest in IT innovations with different maturities. Together with mature IT innovations, companies should incorporate emerging IT innovations in their investment strategy. Emerging IT innovations have not yet been widely accepted. Thus, they are characterized by higher uncertainty about their future evolution but have potentially high long-term returns. To enable mindfulness in these decision-making processes, the literature emphasizes organizational learning through continuous engagement in IT innovations to enhance a companys ability to understand, successfully adopt, and implement emerging IT innovations. IT innovation literature so far has focused on qualitative work, but lacks of quantitative models for the analysis of ex-ante investment decisions. Therefore, we develop a dynamic optimization model that determines the optimal allocation of an IT innovation budget to mature and emerging IT innovations, considering the impact of organizational learning. Based on our model, we examine relevant causal relationships by analyzing the influence of uncertainty, a companys initial individual innovativeness, and the markets average investment share on the optimal engagement. We find that companies should always invest at least a small portion of their budget in emerging IT innovations, regardless of their actual innovativeness. Our results offer new insights into the crucial determinants of investment decisions and provide the basis for future quantitative research on emerging IT innovations.
  • Publication
    Development of dynamic key figures for the identification of critical components in smart factory information networks
    ( 2017)
    Häckel, Björn
    ;
    Miehle, Daniel
    ;
    Pfosser, Stefan
    ;
    Übelhör, Jochen
    Informational risks in smart factories arise from the growing interconnection of its components, the increasing importance of real-time accessibility and exchange of information, and highly dynamic and complex information networks. Thereby, physical production more and more depends on functioning information networks due to increasing informational dependencies. Accordingly, the operational capability of smart factories and their ability to create economic value heavily depend on its information network. Thus, information networks of smart factories have to be evaluated regarding informational risks as a first prerequisite for subsequent steps regarding the management of a smart factory. In this paper, we focus on the identification of critical components in information networks based on key figures that quantitatively depict the availability of the information network. To enable analyses regarding dynamic effects, the developed key figures cover dynamic propagation and recovery effects. To demonstrate their applicability, we investigate two possible threat scenarios in an exemplary information net-work. Further, we integrated the insights of two expert interviews of two global companies in the automation and packaging industry. The results indicate that the developed key figures offer a promising approach to better analyse and understand informational risks in smart factory information networks.
  • Publication
    Explaining the energy efficiency gap - expected utility theory versus cumulative prospect theory
    ( 2017)
    Häckel, Björn
    ;
    Pfosser, Stefan
    ;
    Tränkler, Timm
    Energy efficiency is one of the key factors in mitigating the impact of climate change and preserving non-renewable resources. Although environmental and economic justifications for energy efficiency investments are compelling, there is a gap between the observable and some notion of optimized energy consumption - the so-called energy efficiency gap. Behavioral biases in individual decision making have been resonated by environmental research to explain this gap. To analyze the influence of behavioral biases on decisions upon energy efficiency investments quantitatively, we compare Expected Utility Theory with Cumulative Prospect Theory. On basis of a real-world example, we illustrate how the extent of the gap is influenced by behavioral biases such as loss aversion, probability weighting and framing. Our findings indicate that Cumulative Prospect Theory offers possible explanations for many barriers discussed in literature. For example, the size of the gap rises with increased risk and investment costs. Because behavioral biases are systematic and pervasive, our insights constitute a valuable quantitative basis for environmental policy measures, such as customer-focused and quantitatively backed public awareness campaigns, financial incentives or energy savings insurances. In this vein, this paper may contribute to an accelerated adaption of energy efficiency measures by the broader public.