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The article emphasizes that future marketers need to focus on sustainable value creation for shareholders, society, and the planet. They should be adept at using data responsibly to make informed decisions and leverage technological innovations like AI, AR, VR, and robotics. Generative AI will transform content creation, market research, and strategic marketing processes. Marketers must also understand AI's limitations and contextualize its use to maintain human connections and interactions.
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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.
Circular Economy is a novel disruptive paradigm redefining sustainability in the hospitality industry and addressing the environmental challenges set by this fast-growing impactful industry. To address these challenges, the creation of further knowledge on circular economy and its applications in the hospitality sector is fundamental, together with providing hoteliers and restaurateurs with proper skills and knowhow to tackle such challenges. Drawing on a on going pilot project on Circular Economy in Hotels in Amsterdam, the Friesland hospitality sector and the Professorship of Sustainability in Hospitality and Tourism at NHL Stenden University of Applied Sciences have set out to develop an innovative learning experimental environment in which Friesland hoteliers and restaurateurs can develop further knowledge and identify - together with students, researchers, and experts – possible key actions and strategies to implement regenerative circular processes of material up-cycling. To which extent this learning community of the Northern Netherlands contributes to develop wider knowledge on circular economy in hospitality and to identify, implement, and test innovative regenerative circular actions will be evaluated.
Het doel van dit interdisciplinaire SIA KIEM project Fluïde Eigenschap in de Creatieve Industrie is te onderzoeken of en hoe gedeelde vormen van eigenaarschap in de creatieve industrie kunnen bijdragen aan het creëren van een democratischer en duurzamer economie, waarin ook het MKB kan participeren in digitale innovatie. Het project geeft een overzicht van beschikbare vormen van (gedeeld) eigenaarschap, hun werking en hoe deze creatieve professionals kunnen ondersteunen bij de transitie naar de platformeconomie. Dit wordt toegepast op een concrete case, dat van een digitale breimachine. Naast het leveren van een goede praktijk, moet het project leiden tot een groter internationaal onderzoeksvoorstel over Fluid Ownership in the Creative Industry, dat dieper ingaat op de beschikbare eigendomsoplossingen en hoe deze waarde zullen creëren voor de creatieve professional.
