Emerging smart manufacturing technologies combine physical production networks with digital IT systems, resulting in complex smart factory networks, which are especially vulnerable to IT security risks, such as IT component nonavailabilities. Companies must employ extensive IT security measures to secure their production facilities. However, complex network structures and inherent dependencies of smart factory networks complicate corresponding investment decisions and increase the need for appropriate decision support. We develop a risk assessment model that supports companies in the investment decision-making process regarding IT security measures by identifying and evaluating the most critical areas of the information network while considering the underlying production network. For this purpose, IT availability risks are quantified by means of graph theory, matrix notation, and value-at-risk. Our model provides a structured approach and considers network structures and interdependencies. The insights gained by our model present a profound economic basis for investment decisions on IT security measures. By applying our model in an exemplary real-world setting, we analyze various IT security measures and their risk reduction effect.

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.

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