The in-depth assessment of the situation of the European textile and clothing sector is composed by six independent reports with a close focus on key aspects useful to understand the dynamics and the development of the textile and clothing industry, drivers of change – most notably the impact of the financial crisis – and identification of policy responses and best practices. This has been done in six specific tasks leading to the six reports: Task 1 Survey on the situation of the EU textile and clothing sector Task 2 Report on research and development Task 3 Report on SME situation Task 4 Report on restructuring Task 5 Report on training and Education Task 6 Report on innovation practices. This final report draws on the key findings of each independent report, highlighting major conclusions in order to describe the situation of the textile and clothing industry and the way forward for the sector. In line with the terms of reference the findings in the six reports have been analysed in connection with the developments following the recommendations drafted by the High Level Group on textiles and clothing (further referred to as HLG), installed in 2004 as a response to the European Commission Communication of 29th of October 2003 on the textile and clothing industry. The HLG was composed of leading personalities representing stakeholders in the textiles and clothing industry and issued two reports entailing a vision and recommendations.
MULTIFILE
30-11-2012
Risk matrices have been widely used in the industry under the notion that risk is a product of likelihood by severity of the hazard or safety case under consideration. When reliable raw data are not available to feed mathematical models, experts are asked to state their estimations. This paper presents two studies conducted in a large European airline and partially regarded the weighting of 14 experienced pilots’ judgment though software, and the calculation of agreement amongst 10 accident investigators when asked to assess the worst outcome, most credible outcome and risk level for 12 real events. According to the results, only 4 out of the 14 pilots could be reliably used as experts, and low to moderate agreement amongst the accident investigators was observed.
Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
MULTIFILE
31-01-2019
