Dairy cow welfare is increasingly a subject of public concern. A recent European report of leading scientists concluded that lameness and mastitis of cows were the most important factors in reducing the welfare of dairy cows due to the pain associated with these conditions. Unfortunately, the Farm Animal Welfare Council in the UK also reports that the dairy industry has made little progress in addressing these problems, mainly due to a reduction in profitability affecting investment and the lack of welfare surveillance systems available. A major challenge in improving the welfare of food production animals is in developing methods of automating the detection of such welfare problems. Such detection systems should be able to operate as early warning systems and detect the early signs of disease or illness within dairy herds and individual cows. Thanks to new technological developments there are potential solutions. Until recently, it has not been logistically possible to monitor the complex behaviour associated with animals kept in large social groups, such as sheep, pigs or cows. However, novel local positioning wireless sensors such as those designed by our project partner, Omnisense, can be deployed over large networks of animals and give accurate positioning information for individuals over long periods of time. For the first time we will be able to record large quantities of data regarding the behaviour and social interactions in a whole herd of dairy cows. Research studies have shown that diseases such as lameness in dairy cattle can affect general behaviour, such as how long cows spend lying down. Similarly, social interactions between individual animals, such as how much time they spend close to each other or how closely they synchronise their behaviour, have been suggested as possible measures of animal welfare. However, it is a non-trivial problem to determine and quantify changes in individual and social behaviour and subsequently to use such changes to predict the onset of disease. In this project we will be using automated data collection techniques to record patterns of space use, movement, and social interactions within commercial dairy herds. In the first year, the behaviour of animals with lameness, mastitis or metabolic disease will be compared with healthy animals to determine differences in behaviour. In year two, a full dairy herd will be monitored for an extended period from calving to measure changes in their behaviour with the natural onset of disease in order to identify early changes that might be used to subsequently predict disease occurrence. In year three, the study will be repeated on three other farms to test whether such predictions are still relevant on different intensive dairy units. The behavioural data will be analysed using cutting-edge mathematical and statistical techniques. Using information about the observed changes in both individual cow behaviour and herd social structure we will develop a predictive model for the onset of disease and other welfare changes within individual cows. This will lead to the development of an on-farm automated 'early warning' system for disease detection. Such a system would be invaluable for improving the welfare and productivity of dairy cows. Using the techniques we develop for predicting the onset of disease we will also determine if it is possible to use behavioural changes to identify other important welfare changes in dairy cattle, in particular the onset of oestrus and the time of calving.

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Due to the proliferation of ICT Technologies, manufacturing industry is undergoing substantial transformation in terms of HW but also in terms of Cyber-Physical Production Systems and the SW and services used within production environments. In parallel, the manufacturing processes of the future are changing and need to be highly flexible and dynamic in order to satisfy customer demands for, e.g. large series production, mass customization, or changing orders. To keep pace with the needs of the manufacturing industry of the future, in Manufacturing 4.0 companies need to flexibly react to these demands and be able to offer production capacities in a rapid way. Thus companies looking for manufacturing capacity need to be supported by the means to find these capacities, configure them, and integrate them into their own manufacturing processes. To achieve this, one obvious approach is to port successful concepts from the field of Everything-as-a-Service (XaaS) and Cloud computing to manufacturing to mirror agile collaboration through flexible and scalable manufacturing processes: • Leasing and releasing manufacturing assets in an on-demand, utility-like fashion • Rapid elasticity through scaling leased assets up and down if necessary • Pay-per-use through metered service Applying these principles, Cloud manufacturing can move manufacturing processes from production-oriented to service-oriented networks by modelling single manufacturing assets as services in a similar way as SaaS or PaaS solutions. By modelling all process steps and manufacturing assets as services it is possible to realize cross-organization manufacturing orchestrations and integrate distributed resources and ultimately manufacture products more efficiently. While the theoretical foundations for Cloud manufacturing are manifest there are no proven tools and technologies exist in the market - CREMA aims to change this fact by providing Cloud-based Rapid Elastic Manufacturing based on SaaS and Cloud model