Fraunhofer-Institut für Angewandte Informationstechnik FIT

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Revealing influences on carsharing users' trip distance in small urban areas

2022-04-01 , Baumgarte, Felix , Keller, Robert , Röhrich, Felix , Valett, Lynne Merle , Zinsbacher, Daniela

Carsharing is an essential part of the transformation towards sustainable mobility in smaller urban areas. To expand their services and the positive social and environmental benefits, carsharing operators must understand their users' travel behavior. To accelerate this understanding, we analyze usage data of a station-based carsharing service from a small city in Germany with machine learning and explainable artificial intelligence to reveal influencing factors on the trip distance. The resulting four overarching groups are personal characteristics, time-related, car-related, and environmental features. We further analyze the driving distance of several subgroups split by personal and time-related features. Our findings highlight the importance of time-related features for the trip distance of carsharing users in all subgroups. We also discuss the influence of non-time-related features on the user groups. With these results, we derive valuable insights for research and carsharing operators by understanding patterns in individual user behavior in smaller urban areas.

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IT Availability Risks in Smart Factory Networks - Analyzing the Effects of IT Threats on Production Processes Using Petri Nets

2022 , Berger, Stephan , Dun, Christopher van , Häckel, Björn

In manufacturing, concepts like the Internet of Things or Cyber-physical Systems accelerate the development from traditional production facilities towards smart factories. Thereby, emerging digital technologies increasingly connect information networks with production processes, forming complex smart factory networks (SFNs). Due to their reliance on information flows and the high degree of cross-linking, SFNs are, in particular, vulnerable to IT availability risks caused by attacks and errors. Against this backdrop, we present a modelling approach for analyzing the effects of IT threats on production processes. Based on Petri Nets, we provide modular SFN components for modelling SFN architectures and for simulating stochastic attack and error propagation. With this, we support the analysis and comparison of different SFN architectures regarding spreading effects, availability of information and production components, and associated effects on productivity. Our approach enables and serves as a foundation for decision support on SFN layouts from a risk perspective and the derivation of IT security mitigation measures in both research and practice. We evaluate our artefact by implementing and applying a software prototype in artificial and real-life settings.

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Dividing the ICO Jungle: Extracting and Evaluating Design Archetypes

2019 , Bachmann, Nina , Drasch, Benedict , Miksch, Michael , Schweizer, André

The sale of blockchain-based digital tokens as a novel funding mechanism, referred to as initial coin offerings (ICO), has grown exponentially, resulting in $12bn raised globally during the first half of 2018. Due to the novelty of the phenomenon, the concept is not yet entirely understood. Existing research provides first insights into ICO endeavors and design only. To date, comprehensive and in-depth analyses of ICO design archetypes to better understand prevailing ICO characteristics are missing. We bridge this gap by enriching an existing ICO taxonomy and applying a cluster analysis to identify predominant ICO archetypes. As a result, we identify five ICO design archetypes: the average ICO, the liberal ICO, the visionary ICO, the compliant ICO, and the native ICO. We thereby contribute to a comprehensive and in-depth understanding of the ICO phenomenon and its implications. Further, we offer practitioners tangible design suggestions for future ICOs.

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Business Value of the Internet of Things - A Project Portfolio Selection Approach

2018 , Fähnle, Annika , Püschel, Louis , Röglinger, Maximilian , Stohr, Alexander

The Internet of Things (IoT) counts among the most disruptive digital technologies on the market. Despite the IoTs emerging nature, there is an increasing body of knowledge related to technological and business topics. Nevertheless, there is a lack of prescriptive knowledge that provides organizations with guidance on the economic valuation of investments in the IoT perspective. Such knowledge, however, is crucial for pursuing the organizational goal of long-term value maximization. Against this backdrop, we develop an economic decision model that helps organizations determine an optimal IoT project portfolio from a manufacturers perspective and complies with the principles of project portfolio selection and value-based management. For our purposes, IoT project portfolios are compilations of projects that aim to implement IoT technology in an organizations production process, products, or infrastructure. Our decision model schedules IoT projects for multiple planning periods and considers monetary as well as monetized project effects. On this foundation, it determines the project sequence with the highest value contribution. To evaluate our decision model, we discussed its real-world fidelity and understandability with an industry expert renowned for its proficiency in IoT technology, implemented a software prototype, and demonstrated its applicability based on real-world data.

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Diversifikation des marktlichen Risikos bei der Vermarktung industrieller Energieflexibilität im Kontext von Demand Response

2022-02-18 , Mijatovic, Laura , Rockstuhl, Sebastian , Wagon, Felix

Die zunehmende Stromerzeugung aus erneuerbaren Energien ist ein zentraler Baustein für die erfolgreiche Gestaltung der Energiewende in Deutschland. Die Stromerzeugung aus erneuerbaren Energien unterliegt jedoch witterungsabhängigen Schwankungen und gefährdet somit die Stabilität des Stromnetzes. Ein vielversprechender Ansatz, um diese Herausforderung zu adressieren, ist die Anpassung des Stromverbrauchs an die aktuelle Stromerzeugung im Kontext von Demand Response. Insbesondere energieintensive industrielle Verbraucher können durch eine flexible Anpassung des Stromverbrauches an einen zeitvariablen Stromtarif im Erwartungswert Kosten einsparen. Die finanziellen Einsparungen unterliegen jedoch einem marktlichen Risiko aufgrund unsicherer und volatiler Strompreise. Flexibilitätsversicherungen bieten die Möglichkeit, das marktliche Risiko industrieller Verbraucher auf einen Versicherungsanbieter zu transferieren. Eine wichtige Voraussetzung für das Geschäftsmodell von Versicherungsanbietern ist die Diversifikation des transferierten Risikos. In dieser Arbeit wird auf der Basis von zwei beispielhaften Industrieprozessen simulativ untersucht, ob und in welchem Umfang eine Diversifikation marktlicher Risiken im Kontext von Demand Response erreicht werden kann. Die Ergebnisse der Arbeit sind einerseits relevant für die Bewertung und Entwicklung zukünftiger Geschäftsmodelle für das Angebot von Flexibilitätsversicherungen, andererseits sind diese übertragbar auf energieintensive Unternehmen mit unterschiedlich flexiblen Prozessen, die somit Risikodiversifikation erzielen können.

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Driving Sustainably - The Influence of IoT-based Eco-Feedback on Driving Behavior

2020 , Bätz, Alexander , Gimpel, Henner , Heger, Sebastian , Wöhl, Moritz

One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations to-wards reduced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior. One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations towards reduced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior.

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Economic Perspective on Algorithm Selection for Predictive Maintenance

2019 , Fabri, Lukas , Häckel, Björn , Oberländer, Anna Maria , Töppel, Jannick , Zanker, Patrick

The increasing availability of data and computing capacity drives optimization potential. In the industrial context, predictive maintenance is particularly promising and various algorithms are available for implementation. For the evaluation and selection of predictive maintenance algorithms, hitherto, statistical measures such as absolute and relative prediction errors are considered. However, algorithm selection from a purely statistical perspective may not necessarily lead to the optimal economic outcome as the two types of prediction errors (i.e., alpha error ignoring system failures versus beta error falsely indicating system failures) are negatively correlated, thus, cannot be jointly optimized and are associated with different costs. Therefore, we compare the prediction performance of three types of algorithms from an economic perspective, namely Artificial Neural Networks, Support Vector Machines, and Hotelling T² Control Charts. We show that the translation of statistical measures into a single cost-based objective function allows optimizing the individual algorithm parametrization as well as the un-ambiguous comparison among algorithms. In a real-life scenario of an industrial full-service provider we derive cost advantages of more than 17% compared to an algorithm selection based on purely statistical measures. This work contributes to the theoretical and practical knowledge on predictive maintenance algorithms and supports predictive maintenance investment decisions.

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Disentangling Capabilities for Industry 4.0 - an Information Systems Capability Perspective

2022 , Huber, Rocco , Oberländer, Anna Maria , Faisst, U. , Röglinger, Maximilian

Digital technologies revolutionise the manufacturing industry by connecting the physical and digital worlds. The resulting paradigm shift, referred to as Industry 4.0, impacts manufacturing processes and business models. While the ‘why’ and ‘what’ of Industry 4.0 have been extensively researched, the ‘how’ remains poorly understood. Manufacturers struggle with exploiting Industry 4.0’s full potential as a holistic understanding of required Information Systems (IS) capabilities is missing. To foster such understanding, we present a holistic IS capability framework for Industry 4.0, including primary and support capabilities. After developing the framework based on a structured literature review, we refined and evaluated it with ten Industry 4.0 experts from research and practice. We demonstrated its use with a German machinery manufacturer. In sum, we contribute to understanding and analysing IS capabilities for Industry 4.0. Our work serves as a foundation for further theorising on Industry 4.0 and for deriving theory-led design recommendations for manufacturers.

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Evaluating Investments in Flexible On-Demand Production Capacity. A Real Options Approach

2020 , Freitag, Bettina , Häfner, Lukas , Pfeuffer, Verena , Übelhör, Jochen

Ongoing digitalization of production accelerates trends like mass customization, ever shorter lead times, and shrinking product life cycles. Thereby, industrial companies face increasingly volatile demand that complicates an appropriate production capacity planning. On the other hand, the comprehensive digitalization of production environments favors, amongst others, the dynamic integration of flexible external on-demand production capacity provided by specialized external capacity providers (ECPs). To enable the usage of on-demand production capacity, industrial companies may require significant upfront investments (e.g., for inter-organizational information systems, planning and organizational processes, employee training). The objective of this paper is to develop a model that evaluates such enabling upfront investments from the perspective of a manufacturing company. To consider flexibility of action, we apply real options analysis in a discrete-time binomial tree model and weigh these so-called expansion options to related cash outflows. In addition, we evaluate our model by means of a simulation and sensitivity analyses and derive insights for both researchers and practitioners. The insights gained by our model present a profound economic basis for investment decisions on upfront investments in flexible on-demand production capacity.

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Don't Slip on the Initial Coin Offering (ICO) - A Taxonomy for a Blockchain-enabled Form of Crowdfunding

2018 , Fridgen, Gilbert , Regner, Ferdinand , Schweizer, André , Urbach, Nils

Blockchain is rapidly evolving and there is an increasing interest in the technology in both practice and academia. Recently, a blockchain use case called Initial Coin Offering (ICO) draws a lot of attention. ICO is a novel form of crowdfunding that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although, more than 4.5 billion USD have been invested via ICOs, the phenomenon is poorly understood. Scientific research lacks a structured classification of ICOs to provide further insights into their characteristics. We bridge this gap by developing a taxonomy based on real-world ICO cases, related literature, and expert interviews. Further, we derive and discuss prevailing ICO archetypes. Our findings contribute to theory development in the field of ICOs by enriching the descriptive knowledge, identifying design options, deriving ICO archetypes, and laying the foundation for further research. Additionally, our research pro-vides several benefits for practitioners. Our proposed taxonomy illustrates that there is no one-size-fits-all model of ICOs and might support the decision-making process of start-ups, investors and regulators. The proposed ICO archetypes indicate how common ICOs are designed and thus might serves as best practices. Finally, our analysis indicates that ICOs represent a valid alter-native to traditional crowdfunding approaches.

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