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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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.
MULTIFILE
The growing availability of data offers plenty of opportunities for data driven innovation of business models for SMEs like interactive media companies. However, SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models. In this paper we develop and evaluate a practical method for designing data driven business models (DDBM) in the context of interactive media companies. The development follows a design science research approach. The main result is a step-by-step approach for designing DDBM, supported by pattern cards and game boards. Steps consider required data sources and data activities, actors and value network, revenue model and implementation aspects. Preliminary evaluation shows that the method works as a discussion tool to uncover assumptions and make assessments to create a substantiated data driven business model.
MULTIFILE
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
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.
Met het project Circl-Wood willen projectpartners Fijnhout en Nijboer, samen met de Hogeschool van Amsterdam (HvA) kennis ontwikkelen over het ontwerpen en produceren van hoogwaardige objecten uit afvalhout (Dit kan afvalhout betreffen uit verschillende bronnen; afvalinzameling, woningrenovatie, recycling bedrijven, maar ook reststukken van houtleveranciers en houtverwerkende bedrijven) met behulp van geavanceerde numerieke ontwerpgereedschappen (“computational design”). De projectpartners willen samen onderzoeken of het mogelijk is om hoogstaande en in het oog springende circulaire objecten te ontwerpen van een specifieke hoeveelheid afvalhout, met de HvA ligstoel - die in 2018 in een eerder KIEM project is gemaakt - als iconisch voorbeeld. Hierbij worden de kenmerken van het beschikbare hout (kleur, vorm, nerfrichting, houtsoort) als ‘data’ gebruikt om met ontwerpalgoritmes objecten te ontwikkelen met unieke kenmerken. Deze data-gedreven ontwerpmethode dient toepasbaar te zijn op een willekeurige batch hout die door robots geïnventariseerd en gesorteerd is. Het automatiseren van het ontwerpproces voor hoogwaardige producten creëert nieuwe circulaire toepassingsmogelijkheden voor afvalhout. In 2018 was het ontwerp van de stoel niet gebaseerd op de specifieke stukken hout waarvan hij werd gemaakt. Pas na het ontwerp werden stukken afvalhout handmatig geselecteerd, op maat gezaagd en verbonden tot een omhullende vorm, die door de robot 3D gescand is en waar vervolgens door de robot de stoel uit gefreesd is. In Circl-Wood echter wordt een geavanceerd ontwerpproces ontwikkeld: de gegevens van een beschikbare hoeveelheid resthout worden gebruikt om verschillende specifieke ontwerpen te maken met kleurpatronen, vormen en structuren gerelateerd aan het beschikbare hout. Het doel is om haalbare ontwerpen te berekenen op basis van het beschikbare hout. Het project demonstreert hoe numerieke ontwerpgereedschappen bij kunnen dragen aan een creatieve en efficiënte benutting van resthout van houtverwerkende bedrijven zoals Fijnhout voor interieur toepassingen (door bedrijven als Nijboer).
