Management goeroes hebben weinig oog voor de analytische kanten van het managen.
This matrix is generic. It is a tool for data stewards or other research supporters to assist researchers in taking appropriate measures for the safe use and protection of data about people in scientific research. It is a template that you can adjust to the context of your own institution, faculty and / or department by taking into consideration your setting’s own policies, guidelines, infrastructure and technical solutions. In this way you can more effectively determine the appropriate technical and organizational measures to protect the data based on the context of the research and the risks associated with the data.
Big data heeft niet alleen geleid tot uitdagende technische vraagstukken, ook gaat het gepaard met allerlei nieuwe ethische en morele kwesties. Om verantwoord met big data om te gaan, moet ook over deze kwesties worden nagedacht. Want slecht datagebruik kan nadelige gevolgen hebben voor grote groepen mensen en voor organisaties. In de slotaflevering van deze serie verkennen Klaas Jan Mollema en Niek van Antwerpen op een pragmatische manier de ethische kant van big data, zonder te blijven steken in de negatieve effecten ervan.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
The research proposal aims to improve the design and verification process for coastal protection works. With global sea levels rising, the Netherlands, in particular, faces the challenge of protecting its coastline from potential flooding. Four strategies for coastal protection are recognized: protection-closed (dikes, dams, dunes), protection-open (storm surge barriers), advancing the coastline (beach suppletion, reclamation), and accommodation through "living with water" concepts. The construction process of coastal protection works involves collaboration between the client and contractors. Different roles, such as project management, project control, stakeholder management, technical management, and contract management, work together to ensure the project's success. The design and verification process is crucial in coastal protection projects. The contract may include functional requirements or detailed design specifications. Design drawings with tolerances are created before construction begins. During construction and final verification, the design is measured using survey data. The accuracy of the measurement techniques used can impact the construction process and may lead to contractual issues if not properly planned. The problem addressed in the research proposal is the lack of a comprehensive and consistent process for defining and verifying design specifications in coastal protection projects. Existing documents focus on specific aspects of the process but do not provide a holistic approach. The research aims to improve the definition and verification of design specifications through a systematic review of contractual parameters and survey methods. It seeks to reduce potential claims, improve safety, enhance the competitiveness of maritime construction companies, and decrease time spent on contractual discussions. The research will have several outcomes, including a body of knowledge describing existing and best practices, a set of best practices and recommendations for verifying specific design parameters, and supporting documents such as algorithms for verification.
