As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
A practical framework for the implementation of digitalization entitled the “Data Analytic Capability Wheel” was presented. The aspects encompassed by this framework included data quality, data analytics, IT infrastructure, processes, employee knowledge and skills, and management.
To compete in a globalized world, organizations need develop their data analytic capability. Using a review of the literature and case studies, this study examines how organizations acquire and orchestrate the necessary resources to build their data analytic capability in a way that fits their organization and context. The literature review identified six categories of assets and resources needed for the development of data analytic capability, namely data, data analytics, technology, structure and processes, management, and knowledge and skills. The case study findings showed that their impact is interlinked, with the presence of technical knowledge in the organization being essential.
De analyse van data over het leren van studenten kan waardevol zijn. 'Learning analytics' gebruikt studentdata om het leerproces te verbeteren. Welke organisatorische vaardigheden hebben Nederlandse instellingen voor hoger onderwijs nodig om learning analytics succesvol in te zetten?Doel We onderzoeken welke organisatievaardigheden er nodig zijn om in het hoger onderwijs met 'learning analytics' te werken. Met learning analytics krijgen studenten, docenten en studiebegeleiders inzicht in het leerproces. Dit doen ze door data van studenten te analyseren. In de praktijk blijkt het lastig voor onderwijsinstellingen om hier over de hele breedte van de organisatie mee te gaan werken. We kijken in dit onderzoek welke vaardigheden er nodig zijn binnen een organisatie om 'learning analytics' slim in te zetten. Resultaten Dit onderzoek loopt. Tot nu toe hebben we drie wetenschappelijke artikelen gepubliceerd: A First Step Towards Learning Analytics: Implementing an Experimental Learning Analytics Tool Where is the learning in learning analytics? A systematic literature review to identify measures of affected learning From Dirty Data to Multiple Versions of Truth: How Different Choices in Data Cleaning Lead to Different Learning Analytics Outcomes Looptijd 01 december 2016 - 01 december 2020 Aanpak Het onderzoek bestaat uit literatuuronderzoek, een case study bij Nederlandse onderwijsinstellingen en een validatieproject. Dit leidt tot de ontwikkeling van een Learning Analytics Capability Model (LACM): een model dat beschrijft welke organisatorische vaardigheden nodig zijn om learning analytics in de praktijk toe te passen.
