Kühne Foundation Endowed Chair of Logistics Management
Filtern
Erscheinungsjahr
- 2018 (3) (alle_anzeigen)
Dokumenttyp
- Dissertation (3)
Sprache
- Englisch (3)
Volltext vorhanden
- ja (3)
Gehört zur Bibliographie
- nein (3)
Schlagworte
- Air transport (2)
- Luftverkehr (2)
- Automotive industry (1)
- Business plan (1)
- Comparison (1)
- Geschäftsmodell (1)
- Kraftfahrzeugindustrie (1)
- Machine learning (1)
- Maschinelles Lernen (1)
- Neues Produkt (1)
Institut
- Kühne Foundation Endowed Chair of Logistics Management (3) (alle_anzeigen)
This dissertation integrates three papers on risk management in complex new product development projects (NPD) with an integrated perspective on the automotive industry. It seeks to provide a comprehensive access to the topic by highlighting the most relevant risk management aspects in NPD – processes and methods (chapter 2), organization and coordination (chapter 3) as well as culture and strategy (chapter 4).
The focus of chapter 2 lies on the identification and assessment of risks as the initial and most critical risk management steps. It illustrates the application of the analytic hierarchy process (AHP) method as an established multi-criteria decision analysis method to create transparency on the overall risk position of a selected highly complex NPD in the automotive industry. Chapter 2 confirms the AHP method as a suitable approach for stronger preventive risk management in complex NPD with stakeholders with conflicting functional perspectives. This sets the stage for chapter 3 and a subsequent consideration of organization and coordination.
Chapter 3 considers an issue of organization and coordination in complex NPD that is mainly based on the fact that the involved stakeholders have individual targets and rely on concurrent processes. The selected automotive industry example illustrates the resulting managerial challenge of balancing the overall NPD requirements such as product characteristics and development lead time (system perspective) and the targets of single stakeholders in NPD (individual perspective). The NK model as an important setting for search builds the methodical basis to solve this issue and results in optimized organizational and coordination setups.
The implications on risk management in complex NPD as a result of newly emerging NPD types in the automotive industry are subject to chapter 4. These implications are in line with profound industry changes that include changes in the requirements of customers and markets. Chapter 4 uses complexity theory and deploys the lens of complex adaptive systems (CAS) to NPD to outline the emerging NPD types and their implications on risk framing activities. An extended risk framework is derived as basis that addresses the cultural and strategic shift in the automotive industry with regard to managing risk in emerging NPD types.
This dissertation assesses the characteristics and viability of the emerging longhaul Low Cost Carriers (LCCs). In particular, the aim is to understand their business model, evaluate the cost and revenue performance, and investigate its impact on other carriers. Existing academic literature is inconclusive about characteristics and viability of the business model. To validate its defining characteristics, 37 airlines flying on North Atlantic routes are clustered using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) along a newly constructed long-haul airline business model framework. To contribute to the evaluation of business model viability, cost differences between clusters are uncovered followed by a discussion of their sustainability. Key findings include the characterization of the emerging long-haul LCC business model and its significant differences from Full-Service Network Carrier (FSNC) and leisure carrier models. On a cluster average, 33% lower unit costs compared to FSNCs are identified, of which 24 percentage points are evaluated as sustainable. As these cost advantages over FSNCs are smaller compared to the original savings of short-/medium-haul LCCs, revenue competitiveness on the longhaul becomes more critical. To assess long-haul revenue performance, a new metric for benchmarking the revenue per equivalent flight capacity is defined. Subsequently, a revenue model combining traffic, fare, load factor, and seat data from the North Atlantic is developed to determine the revenue per flight capacity across a sample of city-pairs. The results show that LCCs earn revenue per flight capacity comparable to FSNCs on shorter long-haul routes. Key factors to compensate lower direct yields are fewer low-yield connecting passengers, sales of ancillary services, higher load factors, and significantly more passengers per aircraft. Long-haul LCC market impact, and in particular their impact on incumbents’ fare levels, has not yet been assessed and short-/medium-haul LCC-related findings cannot be readily applied. To evaluate the impact of long-haul LCC presence on the incumbents’ pricing, a stylized analytical model is proposed for hypotheses development. Subsequently, Two-Steps Least Squares (2SLS) regressions with Instrumental Variables (IVs) are performed, distinguishing between economy, premium economy, and business classes, based on a sample of North Atlantic routes. Confirming the first hypothesis, LCC presence reduces incumbents’ long-haul economy and premium economy fares by -10% and -13%, respectively, ceteris paribus. LCC presence, however, does not significantly impact long-haul business class fare levels, confirming the second hypothesis. These findings are particularly relevant as the North Atlantic market represents to date one of the remaining profit pools for North American and European legacy carriers. The findings of this research implicate that the long-haul LCC model is economically viable, at least on trunk routes with high demand. FSNC management should be aware of the rising competition and fare impact on North Atlantic routes. Potential reactions could include the de-bundling of entry fares with the option of ancillary sales even on long-haul routes, a re-evaluation of the revenue impact of low-yield connecting passengers, a focus on premium passengers, and a continuous reduction of operating costs.
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).