Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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ackground and aim – Driven by new technologies and societal challenges, futureproof facility managers must enable sustainable housing by combining bricks and bytes into future-proof business support and workplace concepts. The Hague University of Applied Sciences (THUAS) acknowledges the urgency of educating students about this new reality. As part of a large-scale two-year study into sustainable business operations, a living lab has been created as a creative space on the campus of THUAS where (novel) business activities and future-proof workplace concepts are tested. The aim is to gain a better understanding amongst students, lecturers, and the university housing department of bricks, bytes, behavior, and business support. Results – Based on different focal points the outcomes of this research present guidelines for facility managers how data-driven facility management creates value and a better understanding of sustainable business operations. In addition, this practice based research presents how higher education in terms of taking the next step in creating digitized skilled facility professionals can add value to their curriculum. Practical or social implications – The facility management profession has an important role to play in the mitigation of sustainable and digitized business operations. However, implementing high-end technology within the workplace can help to create a sustainable work environment and better use of the workplace. These developments will result in a better understanding of sustainable business operations and future-proof capabilities. A living lab is the opportunity to teach students to work with big data and provides a playground for them to test their circular workplace, business support designs, and smart building technologies.
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The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.
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Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
Hogeschool Rotterdam wil in samenwerking met IT-Campus en Rotterdamse mkb-bedrijven onderzoeken of de dataskills die studenten in hun opleiding verwerven, aansluiten op de datageletterdheid die van hen als startende professionals wordt verlangd. Om dit te beoordelen vragen we Rotterdamse ondernemers naar de datagedreven uitdagingen en problemen die zij voor zich zien en of zij bij de instroom van startende professionals voldoende kennis en skills zien om die uitdagingen het hoofd te bieden. Met de uitkomsten kunnen kennisinstellingen een helder beeld krijgen van het concept datageletterdheid en hiermee een handvat bieden aan opleidingen om dataskills in de curricula aan te laten sluiten op de behoefte in de arbeidsmarkt van de Metropoolregio Rotterdam-Den Haag (MRDH). We werken toe naar een ontwerp Data Skills-set. Misschien is het beter om te spreken van datacompetenties, hetgeen onderdeel is van de zoektocht in dit onderzoek. Welke terminologie is het meest behulpzaam in het oplijnen van onderwijs en werkveld op het gebied van data: geletterdheid, competenties, skills of een combinatie daarvan. Is het van belang of juist contraproductief om daarin (merk)specifieke tooling een plek te geven? We vragen ons ook af of datageletterdheid als een generiek concept domeinoverstijgend bruikbaar is, bijvoorbeeld tussen het economisch en technisch domein. De verwachting is dat de bevindingen op het gebied van datageletterdheid in de regio Rotterdam te generaliseren zijn naar andere delen van Nederland. Ook die hypothese willen we verkennen in dit onderzoek. Door het beantwoorden van deze vragen willen we een start maken voor het ontwerp van een instrument voor professionele ontwikkeling in het werkveld als ook een referentiekader voor het gesprek met onderwijspartners en overheid. Daarnaast kan zo’n ontwerp DataSkills-set ervoor zorgen dat de onderwijsdomeinen in gesprek blijven met elkaar ten aanzien van nieuwe methoden en onderwijsvormen voor vaardigheden.
The DPP4CD project, “Digital Product Passport(s) for Circular Denim: From Pilot to Practice,” focuses on delivering pilot and scalable Digital Product Passports (DPPs) in the circular denim industry. This aligns with the upcoming European Ecodesign for Sustainable Products Regulation (ESPR), making DPPs mandatory for textiles from 2027. A DPP for circular denim should clearly detail material composition, production methods, repair records, and recycling options to meet EU rules like ESPR, Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS). It combines dynamic lifecycle data into a standard, interoperable system that boosts traceability, cuts SME admin burdens, and supports sustainable, circular practices. Led by Saxion and HvA, the multidisciplinary project is based on a real-world Dutch use case with MUD Jeans, a leader in circular denim. The project combines circular economy principles with existing digital technologies, working with partners such as tex.tracer, Tejidos Royo, bAwear, Denim Deal, MODINT, EuFSI and, GS1 Netherlands. Instead of developing new tools, the project applies scalable technologies (augmented DPP extension) and methods e.g. blockchain, life cycle assessments, and traceability standards to denim supply chains. The project defines legal, environmental, technical, and user requirements for DPPs in circular denim and designs a modular, data-driven, and ESPR-compliant system that integrates offline and online components while ensuring interoperability, affordability, reliability, accountability, and scalability. It develops a data framework for material tracking, supported by interoperable digital solutions to improve data-sharing and transparency. A pilot DPP with MUD Jeans will cover the full lifecycle from production to recycling, enabling scalable DPP. The project aims to address societal challenges related to circularity, ensure scalable and implementable solutions, and create a digital platform where knowledge can be developed, shared, and utilised. By combining circular practices with digital technologies, DPP4CD will help textile businesses transition towards sustainable, transparent, and future-proof supply chains.
