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This dissertation investigates the application of machine learning to improve decision making in airline operations. In an introductory overview, we1 discuss the airline industry and the challenges of decision making in airline operations: e. g., complex IT infrastructure, interconnected resources, delay management, fuel price volatility and future environmental regulation. Machine learning can efficiently integrate a large volume of data from a variety of data sources and formats to generate accurate predictions. To assess the viability of using machine learning models for decision making in airline operations, we develop a model based on linear regression and gradient boosting to predict aircraft arrival time. Furthermore, we integrate cost index optimization to model the impact of aircraft speed on arrival time. While we find that machine learning can improve prediction accuracy by more than 30 %, the optimal cost index varies according to fuel cost, flight distance and delay costs. We propose an overall reduction in cost index for short-haul flights to reduce fuel cost while maintaining punctual operations. Arrival time predictions serve as input for daily operations planning and control. For network carriers accurate arrival time predictions are key for efficient hub operations. In a next step, we focus on aircraft arrival time prediction for intercontinental flights. We analyze the accuracy of en-route weather data provided by the flight plan, generate features based on en-route weather data and integrate them in our prediction model. We evaluate three machine learning models: linear regression, random forest and gradient boosting. Through our approach, we can assess the impact of en-route weather data. Overall, an increase in prediction accuracy of 25 % is achieved. By including en-route weather data, prediction accuracy is improved by 5 %. Our model outlines the essential features for intercontinental arrival time predictions and assess the value of en-route weather data. Furthermore, we outline organizational challenges in implementing predictive analytics. Future environmental regulations are a challenge for the airline industry. From 2020 onwards net growth in CO2 emissions is prohibited. Thus, airlines need to focus on initiatives to limit fuel consumption. We develop a prediction model for fuel consumption considering ten different aircraft types. Our results show that fuel consumption can be improved by more than 30 % for short and long-haul flights resulting in an annual reduction of 10.5 million e in fuel costs and a reduction on green house gas emissions of 66.000 tons. Furthermore, we assess the pilots’ willingness to integrate our prediction model in their fuel decision. Our analysis shows that high prediction accuracy, model understanding and a long testing phase are essential for acceptance of the prediction model. The main implication is that decision making in airline operations can be substantially improved through machine learning. Therefore, more prediction.
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Achenbach and Spinler (2018a,b), Achenbach et al. (2017).
Deal Logic Linde / Praxair
(2018)
Currently in news for the last moment approval from the Federal Trade Commission to make the Praxair and Linde merger a reality, the development of the deal has not been short of hurdles. Initially, Praxair-Linde faced resistance from the European Union under the accusation that the merger would hinder competition. Later, both companies had to undertake multiple selling transactions to meet the anti-trust requirements of the countries in which they operated. Praxair and Linde were finally able to meet the requirements in early November for a successful completion of the merger.
Linde and Praxair, the world’s no. 2 and no. 3 industrial gas suppliers respectively, were in the spotlight due to the size of the merger and the impact that it would have on the gas industry, almost making them a monopoly firm.
As a giant in the entertainment space, you have the power to influence not only the kind of content you roll out, but also the mediums on which the content can be offered. With the growing presence of online streaming platforms, the need to make yourself and your content relevant is ever increasing. Under looming concerns of a possible monopoly in the entertainment sector, Disney's purchase of 21st Century Fox has effects on the entertainment industry in the long run. With expectations of downsizing operations and possible large scale lay-offs, the real-world implications of the buyout aren't exactly lighthearted fun.
Disrupt yourself to avoid getting disrupted: Large firms struggling with further growth, acquiring smaller innovation drivers with complementary assets isn’t something new. But what is hard, in this context, is to justify the surging valuations and to identify the underlying synergies. When it comes to the High-Tech sector this can quickly become a philosophical question. The big question in the acquisition of Red Hat Inc. by IBM, analyzed in this Deal Logic, is the one concerning the future of cloud computing, especially when it comes to customer approval.
Deal Logic LVMH / Belmond
(2019)
In the recent past, the demand for luxury experiences has grown. An increase in the middle and upper class disposable income, changes in lifestyle patterns, and demand for unique and exotic holiday experiences have been the forefront drivers. The global luxury travel market is expected to garner USD 1.2tr by 2022. Epitomising desirable destinations, luxurious accommodations, convenient transport facilities, and authentic travel experience, the luxury travel market has great future potential to grow and diversify. Customization and personalization gain increasing importance in luxury tourism. Also, the concept of luxury travel changes from opulence to exclusive. Synonymous with luxury, LVMH bets on the future of this market that is increasingly going experiential. Belmond would help it increase its luxury image. It would benefit from a luxury perception with both tangible and experiential products in its portfolio.
In this competitive world, a company has to keep evolving – either by expansion and/or by diversification. Particularly looking at the sporting industry, the former nowadays is a necessity. With a market size of USD 60bn, the global sporting goods market is growing rapidly. Every company in the industry has to be agile to avoid being disrupted. The acquisition of Amer Sports by Anta Sports reflects this line of thought. Founded three decades ago as a low-cost manufacturer for global brands, Anta is now aiming to rival Adidas and Nike with its planned USD 6.3bn takeover.
Library security
(2015)
This paper examines the sensitivity of profit shifting to the corporate tax rate difference between a subsidiary and its parent company. We exploit tax rate variation stemming from European tax reforms over the period 2003-2013 while accounting for tax base adjustments that might affect firms’ profit shifting response to tax rate changes. We find that affiliates’ profits are sensitive to tax rate changes. However, tax base broadening reforms mitigate the tax rate incentives for profit shifting and significantly reduce the semi-elasticity of profits with respect to corporate tax rates. Finally, we provide evidence of a downward trend in the tax sensitivity of profit shifting, suggesting that the spread of anti-avoidance regulation may have successfully constrained profit-shifting strategies.
This study examines the relation between executives’ inside debt holdings and corporate tax risk. As executives’ inside debt holdings are unsecured and unfunded, they should align executives’ interests with those of outside debtholders and incentivize executives to act more conservatively toward risk. Hence, inside debt should also reduce the risk of tax avoidance activities. Consistent with this prediction, we find that executive inside debt holdings are negatively related to tax risk. Further, this relation becomes stronger at higher levels of tax risk. We also find that the relation between insider debt and tax risk is stronger for firms that are not facing liquidity constraints and among well-governed firms. The latter result implies that institutional ownership and inside debt compensation are substitutes in reducing tax risk. Overall, our results suggest that part of the observed cross-sectional difference in tax avoidance can be explained by a reduction in tax risk that is related to executive inside debt holdings.