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In today’s data-driven businesses, accessing enterprise-wide information is a must to maximize operational efficiencies and discover new opportunities. Indeed, nine out of ten IT leaders consider data silos as the main cause of significant business problems for most enterprises.

Competing in today's global scene, developing products that stand out is every day more challenging. Manufacturing companies rely on the increased quality, high performance, and more significant innovation at a lower cost to differentiate their products and beat the competition.

Our BPMN editor Cardanit was part of a project used to optimize the railway system model of Trieste port in Italy to find out current bottlenecks. Documental procedures & train movements became business processes to analyze and scrutinize in order to further develop the project.

The idea behind Machine Learning is that systems can learn from data, identify patterns on their own and make decisions with minimal human intervention. The same applies to engineering and CAE-based product design.

Engineering problems can involve a large number of design variables. But not all of the inputs have an equal effect on the output. Considering them all can dramatically reduce the efficiency of Design of Experiments (DOE) or optimization studies.