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    Aimed at the hotel, restaurant and cafeteria (HORECA) sector, the Smart Coffee project is an industrial research project that aims to improve the traditional processes of the coffee sector through the implementation of digital technologies such as the Internet of Things and Artificial Intelligence, among others, to any professional coffee maker, regardless of the manufacturer or model of coffee maker.
     
    Smart Coffee represents an opportunity to convert traditional procedures (poor demand forecasting, companies without the ability to anticipate incidents, manual resource management, etc.) to digital environments, and access to the latest technological trends applied to the coffee sector The Foodservice Cluster (coordinator of the project), Galca Atlantic, Cafés Pont, Dribia Data Research, Atlatis IT and the GAIA Cluster participate in the project.
     
    The project is funded by the Ministry of Industry, Trade and Tourism through its Innovative Business Group Support (AEI) programme.

    The agrifood sector must take advantage of the advances that the era of digitalization is bringing to improve its competitiveness and efficiency, to make better use of resources, and to improve response and adaptation time in the face of unforeseen events. In that sense, digitalization enables us to carry it out in a more intelligent and automated way, paving the way towards the complete autonomy of the systems.

    However, different stakeholders in the food supply chain, and the products themselves, have changing conditions that greatly impact the logistical stages of product distribution.

    There is a clear need to apply technology to help address these situations and foster disruptive advances in the sector, based on applied Artificial Intelligence techniques, Big Data analysis and/or other data-based approaches.

    For this reason, ATLANTIS IT has decided to develop Atlantis Food, an innovative solution focused on the application of Artificial Intelligence to provide advice and support in each scenario, including conflictive ones.

    The results of the action are a platform with prediction methodologies (time, cost, demand, e.g.), classification (orders, incidents, etc.), structure identification (group groups of similar orders and try to improve their management). , anomaly detection (detect unforeseen geographic and/or temporal events), which allows the optimization of resources/processes, the generation of knowledge, and reduce the response time in case of an incident or specific need.