KPI-based optimization in traffic flow control, management of electrical networks and in bus and tram depots with Qualicision AI software, and value-added business process data analysis by self-learning preparation of historicized data with Deep Qualicision AI software.
Qualicision technology stands for qualified decision support in the optimization of business processes. Today, many companies use decision-making software to optimize business objectives such as short throughput times, a consistent utilization of resources or a high adherence to schedules, which are managed as key performance indicators in the companies. With the help of suitable AI methods, the associated software automatically detects interactions between these performance indicators and balances conflicting goals in real time, considering adjustable optimization priorities.
This results in decision support that optimizes business objectives from a combination of process data and process knowledge. Since Qualicision can generally include contextually open performance indicators, sustainability indicators can be added directly as optimization goals on an equal footing with profitability indicators and integrated into companies' optimization strategies.
The evaluation of data streams in business processes as part of the Deep Qualicision AI framework prepares raw business process data for process owners in an understandable form. This is done by qualitatively evaluating data that can be directly measured in the business processes with KPIs and learning relations on this data. In this way, business process data is continuously qualitatively characterized (AI term "labeled") by means of KPIs (Key Performance Indicator) and prepared for the use of further AI methods.
The start into the application of AI methods also for small and medium-sized enterprises is thus very easy. The KPI Relation Analysis helps self-learning to classify the business process data in such a way that relations are learned from raw data, which enable the further use of the data understandable for humans in the sense of Explainable AI and with interpretable KPI labels can initiate a business process data optimization itself and control it in a value-added as well as resource-saving and sustainable way.
KPI-based optimization in traffic flow control, management of electrical networks and in bus and tram depots with Qualicision AI software, and value-added business process data analysis by self-learning preparation of historicized data with Deep Qualicision AI software.
