DETERMINING OPTIMAL STRATEGIES FOR INVESTMENTS IN AN EMERGING IT INNOVATION

To generate competitive advantages through investments in emerging IT innovations, an economically well-founded investment strategy is of decisive importance, since timing and extent of investment amounts considerably determine the associated risk and return profile. Due to the uncertainty about emerging IT innovations, an early market entry time is associated with high risk, but offer high returns. A later market entry may carry lower risk but only offers lower returns. To take advantage of both investment strategies while reducing their disadvantages, a mix of both investment strategies can be advantageous. Companies often choose strict early or later investment strategies since an adequate assessment of possible combiniation opportunities and risks is not carried out in advance and company- and innovation-specific factors are neglected. Thus, we develop a quantitative optimization model enabling the determination of an optimal investment strategy and budget allocation to the two different investment strategies in the sense of maximizing the investment´s overall NPV supplementing previous studies by considering company- and IT innovation-specific factors. We show that strict investment strategies are often disadvantageous, that the amount of the investment budget influences the innovation´s expected NPV and that the company\u27s innovativeness has a strong influence on the innovation budget allocation

Modeling IT Availability Risks in Smart Factories

In the course of the ongoing digitalization of production, production environments have become increasingly intertwined with information and communication technology. As a consequence, physical production processes depend more and more on the availability of information networks. Threats such as attacks and errors can compromise the components of information networks. Due to the numerous interconnections, these threats can cause cascading failures and even cause entire smart factories to fail due to propagation effects. The resulting complex dependencies between physical production processes and information network components in smart factories complicate the detection and analysis of threats. Based on generalized stochastic Petri nets, the paper presents an approach that enables the modeling, simulation, and analysis of threats in information networks in the area of connected production environments. Different worst-case threat scenarios regarding their impact on the operational capability of a close-to-reality information network are investigated to demonstrate the feasibility and usability of the approach. Furthermore, expert interviews with an academic Petri net expert and two global leading companies from the automation and packaging industry complement the evaluation from a practical perspective. The results indicate that the developed artifact offers a promising approach to better analyze and understand availability risks, cascading failures, and propagation effects in information networks in connected production environments

Determining Optimal Investment Strategies – On the Economic Evaluation and Analysis of Investments

The main objective of this doctoral thesis is to contribute to the field of economic investment evaluation by developing tailored investment evaluation approaches that enable the determination of optimal investment strategies. Therefore, the doctoral thesis focuses on (i) investments in IT innovations, (ii) investments in IT considering financial constraints, and (iii) investments in EE measures

Bewertung und Planung von IT-Investitionen unter Berücksichtigung finanzieller Beschränkungen

Große Investitionen in Informationstechnologie (IT) sind oftmals langfristig geplante Investitionsvorhaben, die sich über einen längeren Zeitraum erstrecken und dadurch Investitionsauszahlungen in mehreren Perioden erfordern. Bei deren Planung und Budgetierung muss daher ein mehrperiodiger Planungshorizont inklusive relevanter finanzieller Rahmenbedingungen und Risiken berücksichtigt werden. Dadurch können Höhe und Zeitpunkt der Investitionsauszahlungen über mehrere Perioden hinweg optimal bestimmt und der erhoffte Wertbeitrag des Investitionsvorhabens abgesichert bzw. ggf. sogar gesteigert werden. Dies gilt insbesondere für Unternehmen, deren verfügbare finanzielle Eigen- und Fremdmittel stark begrenzt bzw. besonders unsicher sind. Deshalb sollten bei der Planung von langfristigen IT-Investitionen sowohl aktuelle, als auch zukünftig drohende finanzielle Beschränkungen berücksichtigt werden. Dies erfordert einen Disziplinen-übergreifenden Ansatz, der Erkenntnisse aus Finanzmanagement und IT-Management verbindet. In diesem Beitrag wird daher ein Optimierungsmodell entwickelt, bei dem relevante finanziellen Rahmenbedingungen bei der Planung eines ITInvestitionsvorhabens berücksichtigt werden, wodurch dessen Wertbeitrag gesteigert werden kann

Bewertung und Planung von IT-Investitionen unter Berücksichtigung finanzieller Beschränkungen

Große Investitionen in Informationstechnologie (IT) sind oftmals langfristig geplante Investitionsvorhaben, die sich über einen längeren Zeitraum erstrecken und dadurch Investitionsauszahlungen in mehreren Perioden erfordern. Bei deren Planung und Budgetierung muss daher ein mehrperiodiger Planungshorizont inklusive relevanter finanzieller Rahmenbedingungen und Risiken berücksichtigt werden. Dadurch können Höhe und Zeitpunkt der Investitionsauszahlungen über mehrere Perioden hinweg optimal bestimmt und der erhoffte Wertbeitrag des Investitionsvorhabens abgesichert bzw. ggf. sogar gesteigert werden. Dies gilt insbesondere für Unternehmen, deren verfügbare finanzielle Eigen- und Fremdmittel stark begrenzt bzw. besonders unsicher sind. Deshalb sollten bei der Planung von langfristigen IT-Investitionen sowohl aktuelle, als auch zukünftig drohende finanzielle Beschränkungen berücksichtigt werden. Dies erfordert einen Disziplinen-übergreifenden Ansatz, der Erkenntnisse aus Finanzmanagement und IT-Management verbindet. In diesem Beitrag wird daher ein Optimierungsmodell entwickelt, bei dem relevante finanziellen Rahmenbedingungen bei der Planung eines ITInvestitionsvorhabens berücksichtigt werden, wodurch dessen Wertbeitrag gesteigert werden kann

Determining Optimal Investment Strategies – On the Economic Evaluation and Analysis of Investments

The main objective of this doctoral thesis is to contribute to the field of economic investment evaluation by developing tailored investment evaluation approaches that enable the determination of optimal investment strategies. Therefore, the doctoral thesis focuses on (i) investments in IT innovations, (ii) investments in IT considering financial constraints, and (iii) investments in EE measures

Determining Optimal Investment Strategies – On the Economic Evaluation and Analysis of Investments

The main objective of this doctoral thesis is to contribute to the field of economic investment evaluation by developing tailored investment evaluation approaches that enable the determination of optimal investment strategies. Therefore, the doctoral thesis focuses on (i) investments in IT innovations, (ii) investments in IT considering financial constraints, and (iii) investments in EE measures

Explaining the energy efficiency gap - expected utility theory versus cumulative prospect theory

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

DEVELOPMENT OF DYNAMIC KEY FIGURES FOR THE IDENTIFICATION OF CRITICAL COMPONENTS IN SMART FACTORY INFORMATION NETWORKS

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 network. 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