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Knowledge, awareness, and prediction of man, machine, material, and method in manufacturing

The KAP research project aims to provide manufacturing standards to ensure that every existing resource can be used as efficiently as possible through the effective coordination of man, machine, material, and method.

Manufacturing is the driving force behind Europe’s economy, providing over €6,553 billion of GDP. Against a background of climate change legislation, volatile energy prices, and increased environmental awareness, modern manufacturing must encompass a focus on sustainability and eco-efficiency. Given the current economic situation, this must be achieved without the need for large capital expenditure. The application of information technology to an already-existing production facility is a cost-effective investment.

KAP stands for knowledge of past performance and awareness of the present state, enabling the prediction of future outcomes. By creating transparency through the usage of data generated on the shop floor, every existing resource can be used as efficiently as possible. This guarantees the effective coordination of man, machine, material, and method.

To achieve this, the KAP research project will focus on production performance indicator definitions, including aspects of sustainability and energy-efficiency. Techniques like complex event processing and data stream analysis will compute these indicators on-the-fly to provide real-time monitoring. Data mining in combination with OLAP can support problem diagnosis and resolution. Perceptually efficient visualisations will communicate the production performance indicators to decision-makers in a format which can reduce cognitive workload and improve situational awareness.

Within the research project, a well-balanced consortium of research centres and academic as well as industry partners provides the ideal opportunity to develop research outcomes proposed in the project. The research partners estimate reductions of over 5% per annum in waste and energy and 10% in time to market if the research prototype is potentially used in the future.