@techreport{FischerHrsg2017, author = {Fischer (Hrsg.), Doris}, title = {Tourism in W{\"u}rzburg: Suggestions on how to enhance the travel experience for Chinese tourists}, edition = {1. Auflage}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-143898}, pages = {64}, year = {2017}, abstract = {This report provides suggestions on how to enhance the travel experience for Chinese tourists in the German city of W{\"u}rzburg. Based on a user experience survey and a market research, this work includes a quantitative and competitive analysis. It further provides concrete and hands-on measurements for the city council to improve the experience of Chinese visitors coming to W{\"u}rzburg.}, subject = {China}, language = {en} } @book{Schubert2013, author = {Schubert, Fabian}, title = {Lagequalit{\"a}t, Lagequalit{\"a}t, Lagequalit{\"a}t - Standortbewertungsmethoden f{\"u}r den Einzelhandel und Lagewertigkeitsver{\"a}nderungen durch Business Improvement Districts - am Beispiel der Stadt Gießen}, publisher = {Verlag MetaGIS Infosysteme}, address = {Mannheim}, isbn = {978-3-936438-64-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180730}, publisher = {Universit{\"a}t W{\"u}rzburg}, pages = {317}, year = {2013}, abstract = {Die Lage(qualit{\"a}t) stellt den wichtigsten Faktor f{\"u}r den Erfolg eines Standorts dar! Dies gilt sp{\"a}testens seit der Entstehung der ersten Fußg{\"a}ngerzonen in den 1950er Jahren und der Herausbildung der 1A-Lagen als begehrte innerst{\"a}dtische Unternehmensstandorte. Verwunderlich ist jedoch, dass trotz einer weitl{\"a}ufigen Bekanntheit des Begriffs der Lage(qualit{\"a}t), bzw. der 1A-, B- und C-Lage, zum aktuellen Zeitpunkt in Theorie und Praxis nicht nur vielf{\"a}ltige Bezeichnungen zur Beschreibung und Klassifizierung innerst{\"a}dtischer Handelsstandorte, sondern auch eine große Bandbreite an Kriterien und Methodiken bestehen, die zur Qualit{\"a}tsermittlung herangezogen werden. Im Hinblick auf die aktuell knappen kommunalen Haushaltsmittel, den steigenden Wettbewerbsdruck im Handel und die zunehmende Krisenanf{\"a}lligkeit des Wirtschafts-, Finanz- und Immobiliensektors und dem daraus resultierenden Bedeutungszuwachs fundierter Standort- bzw. Lageanalysen, stellt sich die Frage, welche Kriterien aus wissenschaftlicher Sicht zur Ermittlung von Lagequalit{\"a}ten geeignet sind und wie ein aus diesen bestehendes Instrumentarium auszugestalten ist. Dar{\"u}ber hinaus ist vor dem Hintergrund der in den letzten Jahren wachsenden Aktivit{\"a}ten zur Zentrenrevitalisierung zudem zu {\"u}berpr{\"u}fen, ob ein solches Lagequalit{\"a}teninstrumentarium zur Schaffung einer soliden Datenbasis eingesetzt werden k{\"o}nnte, welche als wesentliche Grundlage zur Evaluierung verschiedener innerst{\"a}dtischer Wiederbelebungsmaßnahmen fungiert. Diesen und weiteren im Kontext der aktuellen Innenstadt- und Einzelhandelsentwicklung auftretenden Fragestellungen geht die vorliegende Arbeit nach.}, subject = {Gießen}, language = {de} } @book{Wieland2015, author = {Wieland, Thomas}, title = {R{\"a}umliches Einkaufsverhalten und Standortpolitik im Einzelhandel unter Ber{\"u}cksichtigung von Agglomerationseffekten - Theoretische Erkl{\"a}rungsans{\"a}tze, modellanalytische Zug{\"a}nge und eine empirisch-{\"o}konometrische Marktgebietsanalyse anhand eines Fallbeispiels aus dem l{\"a}ndlichen Raum Ostwestfalens/S{\"u}dniedersachsens}, publisher = {Verlag MetaGIS Infosysteme}, address = {Mannheim}, isbn = {978-3-936438-73-4}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180753}, publisher = {Universit{\"a}t W{\"u}rzburg}, pages = {X, 289}, year = {2015}, abstract = {Die steigende Relevanz von Einzelhandelsagglomerationen z{\"a}hlt zu den zentralen raumbezogenen Elementen des Strukturwandels im Einzelhandel. Sowohl geplante Einkaufszentren als auch Standortkooperationen von eigentlich in interformalem Wettbewerb stehenden Betriebsformen pr{\"a}gen immer mehr die Standortstrukturen des Einzelhandels. Die vorliegende Untersuchung besch{\"a}ftigt sich mit dem r{\"a}umlichen Einkaufsverhalten der Konsumenten im Zusammenhang mit derartigen Erscheinungen. Zun{\"a}chst werden aus verschiedenen theoretischen Perspektiven (Mikro{\"o}konomie, Raumwirtschaftstheorie, verhaltenswissenschaftliche Marketing-Forschung) jene positiven Agglomerationseffekte im Einzelhandel hergeleitet, die auf dem Kundenverhalten basieren; hierbei lassen sich verschiedene Typen von Kopplungs- und Vergleichsk{\"a}ufen als relevante Einkaufsstrategien identifizieren. Die angenommene (positive) Wirkung von Einzelhandelsagglomerationen wird mithilfe eines {\"o}konometrischen Marktgebietsmodells - dem Multiplicative Competitive Interaction (MCI) Model - auf der Grundlage prim{\"a}rempirisch erhobener Marktgebiete {\"u}berpr{\"u}ft. Die Analyseergebnisse zeigen {\"u}berwiegend positive Einfl{\"u}sse des Potenzials f{\"u}r Kopplungs- und Vergleichsk{\"a}ufe auf die Kundenzufl{\"u}sse einzelner Anbieter, wenngleich sich diese in ihrer Intensit{\"a}t und Ausgestaltung unterscheiden. Die Untersuchung zeigt die Relevanz von Agglomerationseffekten im Einzelhandel auf, wobei ein quantitatives Modell auf der Basis des h{\"a}ufig verwendeten Huff-Modells formuliert wird, mit dem es m{\"o}glich ist, diese Effekte zu analysieren. Konkrete Anwendungen hierf{\"u}r finden sich in der betrieblichen Standortanalyse und der Vertr{\"a}glichkeitsbeurteilung von Einzelhandelsansiedlungen.}, subject = {Einzelhandel}, language = {de} } @phdthesis{Demmer2019, author = {Demmer, Claudia}, title = {Merger-specific Efficiency Gains}, doi = {10.25972/OPUS-18392}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-183928}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2019}, abstract = {The present thesis analyzes whether and - if so - under which conditions mergers result in merger-specific efficiency gains. The analysis concentrates on manufacturing firms in Europe that participate in horizontal mergers as either buyer or target in the years 2005 to 2014. The result of the present study is that mergers are idiosyncratic processes. Thus, the possibilities to define general conditions that predict merger-specific efficiency gains are limited. However, the results of the present study indicate that efficiency gains are possible as a direct consequence of a merger. Efficiency changes can be measured by a Total Factor Productivity (TFP) approach. Significant merger-specific efficiency gains are more likely for targets than for buyers. Moreover, mergers of firms that mainly operate in the same segment are likely to generate efficiency losses. Efficiency gains most likely result from reductions in material and labor costs, especially on a short- and mid-term perspective. The analysis of conditions that predict efficiency gains indicates that firm that announce the merger themselves are capable to generate efficiency gains in a short- and mid-term perspective. Furthermore, buyers that are mid-sized firms are more likely to generate efficiency gains than small or large buyers. Results also indicate that capital intense firms are likely to generate efficiency gains after a merger. The present study is structured as follows. Chapter 1 motivates the analysis of merger-specific efficiency gains. The definition of conditions that reasonably likely predict when and to which extent mergers will result in merger-specific efficiency gains, would improve the merger approval or denial process. Chapter 2 gives a literature review of some relevant empirical studies that analyzed merger-specific efficiency gains. None of the empirical studies have analyzed horizontal mergers of European firms in the manufacturing sector in the years 2005 to 2014. Thus, the present study contributes to the existing literature by analyzing efficiency gains from those mergers. Chapter 3 focuses on the identification of mergers. The merger term is defined according to the EC Merger Regulation and the Horizontal Merger Guidelines. The definition and the requirements of mergers according to legislation provides the framework of merger identification. Chapter 4 concentrates on the efficiency measurement methodology. Most empirical studies apply a Total Factor Productivity (TFP) approach to estimate efficiency. The TFP approach uses linear regression in combination with a control function approach. The estimation of coefficients is done by a General Method of Moments approach. The resulting efficiency estimates are used in the analysis of merger-specific efficiency gains in chapter 5. This analysis is done separately for buyers and targets by applying a Difference-In-Difference (DID) approach. Chapter 6 concentrates on an alternative approach to estimate efficiency, that is a Stochastic Frontier Analysis (SFA) approach. Comparable to the TFP approach, the SFA approach is a stochastic efficiency estimation methodology. In contrast to TFP, SFA estimates the production function as a frontier function instead of an average function. The frontier function allows to estimate efficiency in percent. Chapter 7 analyses the impact of different merger- and firm-specific characteristics on efficiency changes of buyers and targets. The analysis is based on a multiple regression, which is applied for short-, mid- and long-term efficiency changes of buyers and targets. Chapter 8 concludes.}, subject = {Verarbeitende Industrie}, language = {en} } @article{KuemmelLindenberger2014, author = {K{\"u}mmel, Reiner and Lindenberger, Dietmar}, title = {How energy conversion drives economic growth far from the equilibrium of neoclassical economics}, series = {New Journal of Physics}, volume = {16}, journal = {New Journal of Physics}, number = {125008}, issn = {1367-2630}, doi = {10.1088/1367-2630/16/12/125008}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-118102}, year = {2014}, abstract = {Energy conversion in the machines and information processors of the capital stock drives the growth of modern economies. This is exemplified for Germany, Japan, and the USA during the second half of the 20th century: econometric analyses reveal that the output elasticity, i.e. the economic weight, of energy is much larger than energyʼs share in total factor cost, while for labor just the opposite is true. This is at variance with mainstream economic theory according to which an economy should operate in the neoclassical equilibrium, where output elasticities equal factor cost shares. The standard derivation of the neoclassical equilibrium from the maximization of profit or of time-integrated utility disregards technological constraints. We show that the inclusion of these constraints in our nonlinear-optimization calculus results in equilibrium conditions, where generalized shadow prices destroy the equality of output elasticities and cost shares. Consequently, at the prices of capital, labor, and energy we have known so far, industrial economies have evolved far from the neoclassical equilibrium. This is illustrated by the example of the German industrial sector evolving on the mountain of factor costs before and during the first and the second oil price explosion. It indicates the influence of the 'virtually binding' technological constraints on entrepreneurial decisions, and the existence of 'soft constraints' as well. Implications for employment and future economic growth are discussed.}, language = {en} } @phdthesis{Siller2023, author = {Siller, Benjamin}, title = {Influence of Lead Time and Emission Policies on the Design of Supply Chains - Insights from Supply Chain Design Models}, doi = {10.25972/OPUS-29671}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-296713}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2023}, abstract = {Companies are expected to act as international players and to use their capabilities to provide customized products and services quickly and efficiently. Today, consumers expect their requirements to be met within a short time and at a favorable price. Order-to-delivery lead time has steadily gained in importance for consumers. Furthermore, governments can use various emissions policies to force companies and customers to reduce their greenhouse gas emissions. This thesis investigates the influence of order-to-delivery lead time and different emission policies on the design of a supply chain. Within this work different supply chain design models are developed to examine these different influences. The first model incorporates lead times and total costs, and various emission policies are implemented to illustrate the trade-off between the different measures. The second model reflects the influence of order-to-delivery lead time sensitive consumers, and different emission policies are implemented to study their impacts. The analysis shows that the share of order-to-delivery lead time sensitive consumers has a significant impact on the design of a supply chain. Demand uncertainty and uncertainty in the design of different emission policies are investigated by developing an appropriate robust mathematical optimization model. Results show that especially uncertainties on the design of an emission policy can significantly impact the total cost of a supply chain. The effects of differently designed emission policies in various countries are investigated in the fourth model. The analyses highlight that both lead times and emission policies can strongly influence companies' offshoring and nearshoring strategies.}, subject = {Supply Chain Management}, language = {en} } @phdthesis{Stein2019, author = {Stein, Nikolai Werner}, title = {Advanced Analytics in Operations Management and Information Systems: Methods and Applications}, doi = {10.25972/OPUS-19266}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-192668}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2019}, abstract = {Die digitale Transformation der Gesellschaft birgt enorme Potenziale f{\"u}r Unternehmen aus allen Sektoren. Diese verf{\"u}gen aufgrund neuer Datenquellen, wachsender Rechenleistung und verbesserter Konnektivit{\"a}t {\"u}ber rasant steigende Datenmengen. Um im digitalen Wandel zu bestehen und Wettbewerbsvorteile in Bezug auf Effizienz und Effektivit{\"a}t heben zu k{\"o}nnen m{\"u}ssen Unternehmen die verf{\"u}gbaren Daten nutzen und datengetriebene Entscheidungsprozesse etablieren. Dennoch verwendet die Mehrheit der Firmen lediglich Tools aus dem Bereich „descriptive analytics" und nur ein kleiner Teil der Unternehmen macht bereits heute von den M{\"o}glichkeiten der „predictive analytics" und „prescriptive analytics" Gebrauch. Ziel dieser Dissertation, die aus vier inhaltlich abgeschlossenen Teilen besteht, ist es, Einsatzm{\"o}glichkeiten von „prescriptive analytics" zu identifizieren. Da pr{\"a}diktive Modelle eine wesentliche Voraussetzung f{\"u}r „prescriptive analytics" sind, thematisieren die ersten beiden Teile dieser Arbeit Verfahren aus dem Bereich „predictive analytics." Ausgehend von Verfahren des maschinellen Lernens wird zun{\"a}chst die Entwicklung eines pr{\"a}diktiven Modells am Beispiel der Kapazit{\"a}ts- und Personalplanung bei einem IT-Beratungsunternehmen veranschaulicht. Im Anschluss wird eine Toolbox f{\"u}r Data Science Anwendungen entwickelt. Diese stellt Entscheidungstr{\"a}gern Richtlinien und bew{\"a}hrte Verfahren f{\"u}r die Modellierung, das Feature Engineering und die Modellinterpretation zur Verf{\"u}gung. Der Einsatz der Toolbox wird am Beispiel von Daten eines großen deutschen Industrieunternehmens veranschaulicht. Verbesserten Prognosen, die von leistungsf{\"a}higen Vorhersagemodellen bereitgestellt werden, erlauben es Entscheidungstr{\"a}gern in einigen Situationen bessere Entscheidungen zu treffen und auf diese Weise einen Mehrwert zu generieren. In vielen komplexen Entscheidungssituationen ist die Ableitungen von besseren Politiken aus zur Verf{\"u}gung stehenden Prognosen jedoch oft nicht trivial und erfordert die Entwicklung neuer Planungsalgorithmen. Aus diesem Grund fokussieren sich die letzten beiden Teile dieser Arbeit auf Verfahren aus dem Bereich „prescriptive analytics". Hierzu wird zun{\"a}chst analysiert, wie die Vorhersagen pr{\"a}diktiver Modelle in pr{\"a}skriptive Politiken zur L{\"o}sung eines „Optimal Searcher Path Problem" {\"u}bersetzt werden k{\"o}nnen. Trotz beeindruckender Fortschritte in der Forschung im Bereich k{\"u}nstlicher Intelligenz sind die Vorhersagen pr{\"a}diktiver Modelle auch heute noch mit einer gewissen Unsicherheit behaftet. Der letzte Teil dieser Arbeit schl{\"a}gt einen pr{\"a}skriptiven Ansatz vor, der diese Unsicherheit ber{\"u}cksichtigt. Insbesondere wird ein datengetriebenes Verfahren f{\"u}r die Einsatzplanung im Außendienst entwickelt. Dieser Ansatz integriert Vorhersagen bez{\"u}glich der Erfolgswahrscheinlichkeiten und die Modellqualit{\"a}t des entsprechenden Vorhersagemodells in ein „Team Orienteering Problem."}, subject = {Operations Management}, language = {en} } @techreport{AlbersKerstingKosse2023, type = {Working Paper}, author = {Albers, Thilo N. H. and Kersting, Felix and Kosse, Fabian}, title = {Income misperception and populism}, doi = {10.25972/OPUS-32169}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-321696}, pages = {38}, year = {2023}, abstract = {We propose that false beliefs about own current economic status are an important factor for explaining populist attitudes. Eliciting subjects' receptiveness to rightwing populism and their perceived relative income positions in a representative survey of German households, we find that people with pessimistic beliefs about their income position are more attuned to populist statements. Key to understanding the misperception-populism relationship are strong gender differences in the mechanism: men are much more likely to channel their discontent into affection for populist ideas. A simple information provision does neither sustainably reduce misperception nor curb populism.}, subject = {Populismus}, language = {en} } @phdthesis{deGraafgebButtler2024, author = {de Graaf [geb. Buttler], Simone Linda}, title = {From Small to Large Data: Leveraging Synthetic Data for Inventory Management}, doi = {10.25972/OPUS-36136}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-361364}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2024}, abstract = {In a world of constant change, uncertainty has become a daily challenge for businesses. Rapidly shifting market conditions highlight the need for flexible responses to unforeseen events. Operations Management (OM) is crucial for optimizing business processes, including site planning, production control, and inventory management. Traditionally, companies have relied on theoretical models from microeconomics, game theory, optimization, and simulation. However, advancements in machine learning and mathematical optimization have led to a new research field: data-driven OM. Data-driven OM uses real data, especially time series data, to create more realistic models that better capture decision-making complexities. Despite the promise of this new research area, a significant challenge remains: the availability of extensive historical training data. Synthetic data, which mimics real data, has been used to address this issue in other machine learning applications. Therefore, this dissertation explores how synthetic data can be leveraged to improve decisions for data-driven inventory management, focusing on the single-period newsvendor problem, a classic stochastic optimization problem in inventory management. The first article, "A Meta Analysis of Data-Driven Newsvendor Approaches", presents a standardized evaluation framework for data-driven prescriptive approaches, tested through a numerical study. Findings suggest model performance is not robust, emphasizing the need for a standardized evaluation process. The second article, "Application of Generative Adversarial Networks in Inventory Management", examines using synthetic data generated by Generative Adversarial Networks (GANs) for the newsvendor problem. This study shows GANs can model complex demand relationships, offering a promising alternative to traditional methods. The third article, "Combining Synthetic Data and Transfer Learning for Deep Reinforcement Learning in Inventory Management", proposes a method using Deep Reinforcement Learning (DRL) with synthetic and real data through transfer learning. This approach trains a generative model to learn demand distributions, generates synthetic data, and fine-tunes a DRL agent on a smaller real dataset. This method outperforms traditional approaches in controlled and practical settings, though further research is needed to generalize these findings.}, subject = {Bestandsmanagement}, language = {en} } @phdthesis{Oberdorf2022, author = {Oberdorf, Felix}, title = {Design and Evaluation of Data-Driven Enterprise Process Monitoring Systems}, doi = {10.25972/OPUS-29853}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-298531}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2022}, abstract = {Increasing global competition forces organizations to improve their processes to gain a competitive advantage. In the manufacturing sector, this is facilitated through tremendous digital transformation. Fundamental components in such digitalized environments are process-aware information systems that record the execution of business processes, assist in process automation, and unlock the potential to analyze processes. However, most enterprise information systems focus on informational aspects, process automation, or data collection but do not tap into predictive or prescriptive analytics to foster data-driven decision-making. Therefore, this dissertation is set out to investigate the design of analytics-enabled information systems in five independent parts, which step-wise introduce analytics capabilities and assess potential opportunities for process improvement in real-world scenarios. To set up and extend analytics-enabled information systems, an essential prerequisite is identifying success factors, which we identify in the context of process mining as a descriptive analytics technique. We combine an established process mining framework and a success model to provide a structured approach for assessing success factors and identifying challenges, motivations, and perceived business value of process mining from employees across organizations as well as process mining experts and consultants. We extend the existing success model and provide lessons for business value generation through process mining based on the derived findings. To assist the realization of process mining enabled business value, we design an artifact for context-aware process mining. The artifact combines standard process logs with additional context information to assist the automated identification of process realization paths associated with specific context events. Yet, realizing business value is a challenging task, as transforming processes based on informational insights is time-consuming. To overcome this, we showcase the development of a predictive process monitoring system for disruption handling in a production environment. The system leverages state-of-the-art machine learning algorithms for disruption type classification and duration prediction. It combines the algorithms with additional organizational data sources and a simple assignment procedure to assist the disruption handling process. The design of such a system and analytics models is a challenging task, which we address by engineering a five-phase method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks. The method facilitates the integration of heterogeneous data sources through dedicated neural network input heads, which are concatenated for a prediction. An evaluation based on a real-world use-case highlights the superior performance of the resulting multi-headed network. Even the improved model performance provides no perfect results, and thus decisions about assigning agents to solve disruptions have to be made under uncertainty. Mathematical models can assist here, but due to complex real-world conditions, the number of potential scenarios massively increases and limits the solution of assignment models. To overcome this and tap into the potential of prescriptive process monitoring systems, we set out a data-driven approximate dynamic stochastic programming approach, which incorporates multiple uncertainties for an assignment decision. The resulting model has significant performance improvement and ultimately highlights the particular importance of analytics-enabled information systems for organizational process improvement.}, subject = {Operations Management}, language = {en} }