De Nederlandse overheid stimuleert digitale toepassingen in het mkb. De assumptie is dat het kleinbedrijf digitaal achterloopt bij het grootbedrijf en daardoor slechter presteert. Dit onderzoek laat zien dat gebruik van te veel digitale toepassingen juist leidt tot slechtere bedrijfsresultaten. De sleutel voor beter presteren ligt namelijk bij menselijk en financieel kapitaal.
This study introduces a novel methodology for the post-analysis of operational predictability by leveraging timestamps collected through the Airport Collaborative Decision Making (A-CDM) framework. Focusing on the start-up and departure phases, the analysis highlights the importance of accurately planning and managing key timestamps, such as the Target Off-Block Time (TOBT) and Target Start-Up Approval Time (TSAT), which are critical for operational efficiency. Using one week of sample data from Schiphol Airport, this research demonstrates the potential benefits of the proposed framework in improving predictability during the start-up phase, particularly by identifying and analyzing outliers and anomalies. The start-up phase, a critical component of the outbound process, was broken down into subphases to allow for a more detailed assessment. The findings suggest that while 96% of flights maintain TOBT accuracy within ±20 minutes, 68% of flights miss their TOBT by 2 to 17.5 minutes, with 364 notable outliers. These deviations highlight areas for further investigation, with future work aiming to explore the impact of influencing factors such as weather, resource availability, and support tools. The proposed framework serves as a foundation for improving operational predictability and efficiency at airports.
This study examines the effect of seat assignment strategies on the transfer time of connecting passengers at a hub airport. Passenger seat allocation significantly influences disembarkation times, which can increase the risk of missed connections, particularly in tight transfer situations. We propose a novel seat assignment strategy that allocates seats to nonpaying passengers after check-in, prioritising those with tight connections. This approach diverges from traditional methods focused on airline turnaround efficiency, instead optimizing for passenger transfer times and reducing missed connections. Our simulation, based on real-world data from Paris-Charles de Gaulle airport, demonstrates that this passenger-centric model decreases missed connections by 12%, enhances service levels, reduces airline compensation costs, and improves airport operations. The model accounts for variables such as seat occupancy,luggage, and passenger type (e.g., business, leisure) and is tested under various scenarios, including air traffic delays.
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.
Het project Early STATUS (Early Strategic Alerts for Turnaround of Small businesses) wil een instrument voor het vroegtijdig signaleren van stagnatie bij MKB bedrijven en een adviesmethode om de koers van deze bedrijven te wijzigen onderzoeken en testen. De vraagarticulatie bestond uit 26 interviews en 8 focusgroepen, in het kader van een KIEM subsidieproject. Uit het vooronderzoek komt naar voren dat het kleinere MKB, bedrijven met 10 tot 50 werknemers, kwetsbaar is voor verval: de waan van de dag regeert en er is weinig capaciteit om de bakens te verzetten. Dit is een structureel probleem en komt door de coronacrisis nijpender naar voren. Opvallend is dat accountants en bedrijfsadviseurs moeite hebben problemen tijdig te signaleren en te adresseren. In de wetenschappelijke literatuur is er weinig aandacht voor dit fenomeen. De vraagarticulatie heeft geleid naar de volgende behoefte: “een praktisch instrumentarium te gebruiken door mkb-ondernemers en hun adviseurs om strategische problemen vroegtijdig te signaleren en alle betrokkenen aan te zetten tot ingrijpen.” Het instrumentarium wordt ontwikkeld door een consortium dat bestaat uit 3 lectoren, 4 onderzoekers en 5 studenten van Hogeschool Rotterdam, aangevuld met een externe onderzoeker. Praktijkpartners zijn 2 accountantskantoren, 6 MKB adviesbureaus en accountancybrancheorganisatie SRA. De Universiteit van Leiden, Erasmus Universiteit Rotterdam en Montpellier Business School leveren academische experts. De hoofdvraag van het onderzoek luidt: “in welke mate draagt een vroegsignaleringsinstrument dat wordt uitgezet via een accountantskantoor bij ondernemers en medewerkers en daaropvolgend een adviesmethode die wordt toegepast door mkb-adviseurs en accountants bij aan het vroeg signaleren en verder voorkomen van verval bij mkb-ondernemingen met 10-50 medewerkers?” Het instrumentarium wordt door het onderzoekconsortium ontwikkeld en vervolgens getest door accountants en mkb-adviseurs bij hun cliënten: maakt het vroegsignaleringsinstrument een eventuele strategische crisis voldoende tijdig duidelijk en stimuleert de adviesmethode de betrokkenen voldoende om daadwerkelijk in te grijpen?
Plastic waste is one of the largest environmental problems in the 21st century. By 2050, up to 12,000 Mt of plastic waste is estimated to be in landfills or in the natural environment. Biochemical recycling by using modified microbial enzymes have shown potentials in the back-to-monomer (BTM) recycling of polyethylene terephthalate by breaking down the polymers into re-usable monomers. These enzymes can be produced via fungal species. In order to make this biochemical BTM process viable a process integrated enzyme production is key in increasing the efficiency and reducing the cost of enzymes. For this a molecular monitoring method, such as RNA-seq (RNA-sequencing), is needed. RNA-seq can achieve a snapshot on enzyme producing process inside of the cell by semi-quantitatively measuring the volume of enzyme encoding RNAs. This information can bring hints on fungal strain improvement by promoting the desired enzymes. It also helps to instantly monitor the BTM production outcomes. However, conventional RNA-seq platforms can only be performed via service providers or startup investments reaching 2 million euros. Each round of analysis could take as long as 6 weeks turnaround time. Furthermore, the method creates huge amount of complicated datasets, only by expert skills and specialized high performance computing the data can be sorted in a comprehensive manner. To solve these problems, in this project, by combining the expertise on plastic end-of-life control, fungal enzyme production, molecular monitoring and Bioinformatics from both the UAS and SME sides, we aim to implement a novel RNA-seq based system to monitor the in-process enzyme production for plastic degradation. We will optimize the existing portable RNA-seq prototype machinery for semi-real time monitoring of the BTM recycling process. The downstream data will be handled by a tailored analysis pipeline designed with expert knowledge via an user-friendly interface.
