Blockchain Technology Based Supply Chain Systems and Supply Chain Performance: A Resource-Based View

Blockchain technology (BCT) is set to transform firms’ present ways of managing supply chains. This thesis explores how the efforts by firms to integrate BCT into their supply chain systems and activities, can enable certain supply chain capabilities and subsequently, improve supply chain performance. Using an abductive research approach, qualitative content analyses of 126 cases of firms’ efforts in BCT-based supply chain systems (BCT-SCS) across multiple industries are conducted to identify the BCT-enabled supply chain capabilities and their performance outcomes. Findings reveal that the present BCT-SCS efforts are mainly oriented towards improving the operational-level capabilities namely, information sharing and coordination capabilities rather than strategic-level capabilities namely, integration and collaboration capabilities. The predominant performance outcomes resulting from these capabilities along with the BCT-SCS are quality compliance and improvement, process improvement, flexibility, reduced cost, and reduced process time. However, the performance outcomes vary with industry type, based on the risks that the industry faces. Based on the study’s findings, an integrated framework of research propositions is presented to facilitate future empirical research

Influences on aircraft target off-block time prediction accuracy

With Airport Collaborative Decision Making (A-CDM) as a generic concept of working together of all airport partners, the main aim of this research project was to increase the understanding of the Influences on the Target Off-Block Time (TOBT) Prediction Accuracy during A-CDM. Predicting the TOBT accurately is important, because all airport partners use it as a reference time for the departure of the flights after the aircraft turn-round. Understanding such influencing factors is therefore not only required for finding measures to counteract inaccurate TOBT predictions, but also for establishing a more efficient A-CDM turn-round process. The research method chosen comprises a number of steps. Firstly, within the framework of a Cognitive Work Analysis, the sub-processes as well as the information requirements during turn-round were analysed. Secondly, a survey approach aimed at finding and describing situations during turn-round that are critical for TOBT adherence was pursued. The problems identified here were then investigated in field observations at different airlines’ operation control rooms. Based on the findings from these previous steps, small-scale human-in-the-loop experiments were designed aimed at testing hypotheses about data/information availability that influence TOBT predictability. A turn-round monitoring tool was developed for the experiments. As a result of this project, the critical chain of turn-round events and the decisions necessary during all stages of the turn-round were identified. It was concluded that information required but not shared among participants can result in TOBT inaccuracy swings. In addition, TOBT predictability was shown to depend on the location of the TOBT turn-round controller who assigns the TOBT: More reliable TOBT predictions were observed when the turn-round controller was physically present at the aircraft. During the experiments, TOBT prediction could be improved by eight minutes, if available information was cooperatively shared ten minutes prior turn-round start between air crews and turn-round controller; TOBT prediction could be improved by 15 minutes, if additional information was provided by ramp agents five minutes after turnround start

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Optimized data processing algorithms for biomarker discovery by LC-MS

This thesis reports techniques and optimization of algorithms to analyse label-free LC-MS data sets for clinical proteomics studies with an emphasis on time alignment algorithms and feature selection methods. The presented work is intended to support ongoing medical and biomarker research. The thesis starts with a review of important steps in a data processing pipeline of label-free Liquid Chromatography – Mass Spectrometry (LC-MS) data. The first part of the thesis discusses an optimization strategy for aligning complex LC-MS chromatograms. It explains the combination of time alignment algorithms (Correlation Optimized Warping, Parametric Time Warping and Dynamic Time Warping) with a Component Detection Algorithm to overcome limitations of the original methods that use Total Ion Chromatograms when applied to highly complex data. A novel reference selection method to facilitate the pre-alignment process and an approach to globally compare the quality of time alignment using overlapping peak area are introduced and used in the study. The second part of this thesis highlights an ongoing challenge faced in the field of biomarker discovery where improvements in instrument resolution coupled with low sample numbers has led to a large discrepancy between the number of measurements and the number of measured variables. A comparative study of various commonly used feature selection methods for tackling this problem is presented. These methods are applied to spiked urine data sets with variable sample size and class separation to mimic typical conditions of biomarker research. Finally, the summary and the remaining challenges in the data processing field are summarized at the end of this thesis.

Blockchain in Service Management and Service Research – Developing a Research Agenda and Managerial Implications

As blockchain technology is maturing to be confidently used in practice, its applications are becoming evident and, correspondingly, more blockchain research is being published, also extending to more domains than before. To date, scientific research in the field has predominantly focused on subject areas such as finance, computer science, and engineering, while the area of service management has largely neglected this topic. Therefore, we invited a group of renowned scholars from different academic fields to share their views on emerging topics regarding blockchain in service management and service research. Their individual commentaries and conceptual contributions refer to different theoretical and domain perspectives, including managerial implications for service companies as well as forward-looking suggestions for further research.Information and Communication TechnologyEconomics of Technology and Innovatio

Fabricate 2020

Fabricate 2020 is the fourth title in the FABRICATE series on the theme of digital fabrication and published in conjunction with a triennial conference (London, April 2020). The book features cutting-edge built projects and work-in-progress from both academia and practice. It brings together pioneers in design and making from across the fields of architecture, construction, engineering, manufacturing, materials technology and computation. Fabricate 2020 includes 32 illustrated articles punctuated by four conversations between world-leading experts from design to engineering, discussing themes such as drawing-to-production, behavioural composites, robotic assembly, and digital craft