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.
The proposed study is focused on finding out whether Virtual Reality is a feasible method to train for composite manufacturing. The demand for cost-effective training methods for composite production is growing. The current training methods are not satisfying the demands of the fast-growing industry. This could be solved with the help of Virtual Reality (VR), potentially cutting down training time and use of material, hence reducing costs. This project will create insight into the technical and economic feasibility of this idea. This will be achieved with interns from Inholland, lecturer and researchers.
Service logistics in de vliegtuigonderhoudindustrie is een zeer kennisintensieve en concurrerende markt. De meest cruciale factor in deze industrie is het laag houden van de downtime tijdens maintenance, repair en overhaulactiviteiten. Met name opslag, distributie en het managen van spare parts spelen hierin een belangrijke rol. Om tijdig vliegtuigen te kunnen onderhouden, hebben onderhoudsbedrijven vaak duizenden onderdelen, van kleine ophangpennen tot zeer dure motoren, op voorraad. Hierin zit dan ook de paradox: onderhoudskosten dalen door lagere down time en grote voorraden zorgen op hun beurt weer voor hoge warehousing kosten. Het lectoraat Aviation Engineering voert thans een RAAK-MKB project uit waarin primair wordt onderzocht of historische onderhoudsdata kan worden gebruikt voor MRO-onderhoudsplannen die de downtime verlagen. Gaandeweg de uitvoering van dit project is echter gebleken dat niet alle onderhoud van te voren gepland kan worden en dat juist real time data tijdens de vlucht erg relevant is voor snel en efficiënt onderhoud. De doelstelling van dit KIEM-project is enerzijds het vergaren van nieuwe kennis en inzichten over service logistics en het daarmee aanjagen nieuw onderzoek waarin wordt onderzocht of de inzet van real time big data bijdraagt aan het verminderen van de downtime. Anderzijds wordt onderzocht of nieuwe samenwerkingen (met IT-bedrijven) mogelijk zijn die voorraadkosten verminderen. Onderzoek wordt gedaan naar: 1. Knelpunten voor de inzet van real time big data in relatie tot MRO-activiteiten. 2. Vraagarticulatie en samenwerkingsmogeljkheden met nieuwe mkb-bedrijven. 3. Spare part warehousing efficiëntie (parts pooling). 4. Infrastructuur en standaarden voor opslag en toegankelijkheid van gezamenlijke en individuele (bedrijfsgevoelige) data. De HvA, NAG en JetSupport verwachten dat met dit project nieuwe mkb-onderhoudsbedrijven, vliegtuigmaatschappijen en overheden gaandeweg het project gaan aanhaken. Uitkomsten zijn enerzijds nieuwe kennis en inzichten op het gebied van service logistics en anderzijds commitment voor een vervolgonderzoek op het lopende RAAK-project.
‘Dieren in de dijk’ aims to address the issue of animal burrows in earthen levees, which compromise the integrity of flood protection systems in low-lying areas. Earthen levees attract animals that dig tunnels and cause damages, yet there is limited scientific knowledge on the extent of the problem and effective approaches to mitigate the risk. Recent experimental research has demonstrated the severe impact of animal burrows on levee safety, raising concerns among levee management authorities. The consortium's ambition is to provide levee managers with validated action perspectives for managing animal burrows, transitioning from a reactive to a proactive risk-based management approach. The objectives of the project include improving failure probability estimation in levee sections with animal burrows and enhancing risk mitigation capacity. This involves understanding animal behavior and failure processes, reviewing existing and testing new deterrence, detection, and monitoring approaches, and offering action perspectives for levee managers. Results will be integrated into an open-access wiki-platform for guidance of professionals and in education of the next generation. The project's methodology involves focus groups to review the state-of-the-art and set the scene for subsequent steps, fact-finding fieldwork to develop and evaluate risk reduction measures, modeling failure processes, and processing diverse quantitative and qualitative data. Progress workshops and collaboration with stakeholders will ensure relevant and supported solutions. By addressing the knowledge gaps and providing practical guidance, the project aims to enable levee managers to effectively manage animal burrows in levees, both during routine maintenance and high-water emergencies. With the increasing frequency of high river discharges and storm surges due to climate change, early detection and repair of animal burrows become even more crucial. The project's outcomes will contribute to a long-term vision of proactive risk-based management for levees, safeguarding the Netherlands and Belgium against flood risks.
The transition to a circular economy requires innovative digital solutions to extend the lifespan of electrical and electronic appliances (EEA) and reduce the volume of waste generated by this product stream. Digital Product Passports (DPPs) make product and usage information accessible to supply chain partners and serve as a crucial tool for optimising circular strategies. DPP data on performed maintenance, upgrades, (sensor) data on EEA usage, diagnostics and repairs support supply chain actors throughout the product lifecycle in carrying out their circular responsibilities. This project focuses on the application of DPPs in the "Middle-of-Life" phase of EEA products, specifically dishwashers and coffee machines. The central research question is: How can the EEA supply chain design and actively manage a DPP in a way that creates value for all stakeholders in the Middle-of-Life phase and contributes to product life extension and circularity? The applied methodology is based on Design Science Research (DSR) and Co-design, in which manufacturers, repair services, collection partners and DPP solution providers collaborate on a practice-oriented implementation. In co-design sessions, the requirements and functionalities of DPPs are defined based on identified circular roles and related information needs. These are then translated into a DPP "Proof of Concept", which is tested by partners across the electronics value chain. The intended outcome is an implemented and validated DPP concept that unlocks product data, optimises circular processes, and strengthens collaboration within the supply chain. This project contributes to strategic policy agendas on digitalisation and circularity and offers a blueprint for the broader application of DPPs in the EEA sector. The project partners – ATAG Benelux, E-Care, Beekman B.V., Holland Circulair, Eviden, Saxion University of Applied Sciences, and HU University of Applied Sciences Utrecht – combine their expertise to develop a future-proof, scalable and practice-based DPP solution.
