Since the financial and administrative liberalisation from the government in the late 1980s and the 1990s, the Dutch housing associations have been very dynamic, regarding the considerable extension of both commercial and social activities, the increased reliance and dependence on market circumstances, and the large number of amalgamations, creating bigger organisations. In recent years the Dutch social housing sector is under increased pressure as a consequence of the credit crunch, increased tax levies and the national implementation in the sector of EU regulations on ‘Services of General Economic Interest’. Factors like these are likely to have an effect on the organisational strategies of housing associations, the main providers of social housing in the Netherlands. The direction and the size of these effects, however, are not well known. A recent inquiry among housing associations sheds more light on this. In this paper, we make use of a classification including a socialcommercial dimension and a dimension between so-called ‘prospectors’ and ‘defenders’. This classification proves to be an adequate tool to describe the recent developments in the sector. It is concluded that, in general, housing associations are focussing more on traditional social housing tasks and ‘defending’ strategies, implying a shift back compared to the trend in recent decades.
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This paper addresses new funding issues faced by SMEs. Over a period of nine months, the authors conducted a preliminary study into the problems surrounding stacked funding faced by SMEs and their financial advisers. The study includes a short literature review, the outcomes of three round table discussions and the identification of problems and possible solutions.
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Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
