Darwin, the intelligent machine benchmark, learns a virtual ideal process on the basis of high-frequency PLC data from several machines and thus provides a target value how fast each machine could be and how to get there. Based on this, Darwin continuously generates concrete recommendations for reducing the cycle time. Maintenance departments are empowered by Darwin to avoid breakdowns before they happen thanks to early warnings.
Darwin supports productivity optimization in the field of consumer goods by optimizing the cycle times of cyclic production machines, like injection moulding or extrusion in e.g. white appliance production or e.g. in household products production.