Bird strikes, a risk factor in the aviation industry, are a common problem in certain states of the USA, while they are extremely rare in other states. Similarly, the seasonal distribution of bird strikes is not proportional. This situation poses an unfair situation in the aviation insurance of airline companies in terms of routes taken. The current study, detecting a literature gap related to the principal-agent problem within the aviation sector, evaluates the possible differences in aviation companies' insurance costs, assuming bird strikes are spatially and temporally analyzed in the US, and airline companies are provided with complete information regarding bird-strikes. In this research, QGIS software served in spatial model mappings. In terms of the threshold value, the study results show that making bird-strike insurance aircraft in twenty-one states which were below the threshold value increased the aviation costs of these airline companies, while in the remaining twenty-nine states, non-insurance raised the cost. In this context, as of 2022, it has been determined that not paying an extra premium for bird strikes in twenty-one states below the threshold value will create efficiency, while expending an above-average insurance premium in twenty-nine states and the District of Columbia above the threshold value will create efficiency. The research seeks to answer the following question: Is it fair for airlines operating on routes with low or high bird strike risks to pay the same amount of insurance cost?
Aviation increasingly faces capacity challenges exposing inefficiencies and shortcomings of aviation related processes and systems. The European slot allocation system was designed in an era with little to no capacity constraints, now resulting in regulations not fitting in today’s developments.
MULTIFILE
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
INCLAVI will address the skills mismatches that exist in the aviation sector related to the freedom of movement of persons with disabilities and accessibility requirements in line with the EC Strategy for the Rights of Persons with Disabilities 2021-2030.The project accomplishes this through rigorous cooperation between key global industry and labour market actors combined with a world-class HEI and VET consortium. INCLAVI will also further improve the collaboration between HEIs and VET.INCLAVI will design and co-create a new training curriculum utilising expertise from HEI, VET and Industry Actors to support the reskilling of aviation sector employees and key target groups who have a role in the passenger journey of PwDs from door to door. The training will address students and professionals in areas of work related to travel agencies, airports, and airlines.
