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
Limited data is available on the size of urban goods movement and its impact on numerous aspects with respect to livability such as emissions and spatial impact. The latter becomes more important in densifying cities. This makes it challenging to implement effective measures that aim to reduce the negative impact of urban good movement and to monitor their impact. Furthermore, urban goods movement is diverse and because of this a tailored approach is required to take effective measures. Minimizing the negative impact of a heavy truck in construction logistics requires a different approach than a parcel delivery van. Partly due to a lack of accurate data, this diversity is often not considered when taking measures. This study describes an approach how to use available data on urban traffic, and how to enrich these with other sources, which is used to gain insight into the decomposition (number of trips and kilometers per segment and vehicle type). The usefulness of having this insight is shown for different applications by two case studies: one to estimate the effect of a zero-emission zone in the city of Utrecht and another to estimate the logistics requirements in a car-free area development.
MULTIFILE
CILOLAB contributes to the transition of the UFT-system towards zero emission city logistics in 2025 by examining, developing and enabling alternatives for urban logistics activities. Specifically, CILOLAB focuses on the transferability and scaling-up of successful logistics initiatives; i.e. concepts that facilitate decoupling between transport towards and in cities. CILOLAB is an action-driven partnership where cities cooperate with transport operators, interest groups, research institutes and societal partners and collaboratively develop new approaches for urban logistical solutions. Through continuous monitoring and impact assessment these solutions are evaluated and further developed within this experimentation environment, all contributing to the CILOLAB ambition.
CILOLAB contributes to the transition of the UFT-system towards zero emission city logistics in 2025 by examining, developing and enabling alternatives for urban logistics activities. Specifically, CILOLAB focuses on the transferability and scaling-up of successful logistics initiatives; i.e. concepts that facilitate decoupling between transport towards and in cities. CILOLAB is an action-driven partnership where cities cooperate with transport operators, interest groups, research institutes and societal partners and collaboratively develop new approaches for urban logistical solutions. Through continuous monitoring and impact assessment these solutions are evaluated and further developed within this experimentation environment, all contributing to the CILOLAB ambition.
Restoring rivers with an integrated approach that combines water safety, nature development and gravel mining remains a challenge. Also for the Grensmaas, the most southern trajectory of the Dutch main river Maas, that crosses the border with Belgium in the south of Limburg. The first plans (“Plan Ooievaar”) were already developed in the 1980s and were highly innovative and controversial, as they were based on the idea of using nature-based solutions combined with social-economic development. Severe floodings in 1993 and 1995 came as a shock and accelerated the process to implement the associated measures. To address the multifunctionality of the river, the Grensmaas consortium was set up by public and private parties (the largest public-private partnership ever formed in the Netherlands) to have an effective, scalable and socially accepted project. However, despite the shared long term vision and the further development of plans during the process it was hard to satisfy all the goals in the long run. While stakeholders agreed on the long-term goal, the path towards that goal remains disputed and depends on the perceived status quo and urgency of the problem. Moreover, internal and external pressures and disturbances like climate change or the economic crisis influenced perception and economic conditions of stakeholders differently. In this research we will identify relevant system-processes connected to the implementation of nature-based solutions through the lens of social-ecological resilience. This knowledge will be used to co-create management plans that effectively improve the long-term resilience of the Dutch main water systems.
