Method for an electronic system controller for dosing devices with optimised dosing of raw materials, comprising bulk material or other material, to form mixed material by means of a dosing device, wherein the dosing device comprises raw material silos, conveying devices, weighing devices and a mixer and/or collecting container for the mixed material, wherein the raw material is transported to the mixer and/or collecting container for the mixed material via the conveying devices, wherein the amount of raw material conveyed by means of the conveying devices is measured by means of the weighing devices, and wherein one or more electronic control units connected to the weighing devices regulate the conveying quantity of the conveying devices based on dosing system parameters, characterised by: measuring relative dosing errors given by the measured deviation of an average across one time unit of the weight of the conveyed raw material per weighing device from a target weight value, measuring allocated frequency distributions of the scaled relative dosing errors per weighing device and conveyed raw material, measuring dosing capabilities overtime per conveying device and conveyed raw material as a time series of measurements of dosing capabilities, and measuring dosing speeds depending on the time per conveying device and conveyed raw material, detecting first anomaly and dosing errors of the dosing device based on the measured relative dosing errors, wherein the detection of anomaly and dosing errors is triggered by exceeding the measured deviation of a defined threshold value per weighing device and conveyed raw material, detecting second anomaly and dosing errors of the dosing device, wherein the topological distance between the measured times series of the dosing capabilities over a period of time is determined as a distance matric by means of dynamic temporal standardisation, and the measured time series are clustered into disjoint clusters based on the measured distance matrix by means of a statistical data mining unit, and wherein measured time series of a first cluster index a dosing operation in a standard range and measured time series of a second cluster index a dosing operation outside the standard range, and if first and/or second anomaly and dosing errors (11121/11122) of the dosing device are detected, transmitting the measured dosing speeds depending on the time as input data patterns to a machine-learning unit and adapting the dosing system parameters of the dosing device by means of the electronic system controller based on the starting values of the machine-learning unit, wherein the machine-learning unit classifies the input patterns by means of learnt patterns and generates corresponding dosing parameters. measuring relative dosing errors given by the measured deviation of an average across one time unit of the weight of the conveyed raw material per weighing device from a target weight value, measuring allocated frequency distributions of the scaled relative dosing errors per weighing device and conveyed raw material, measuring dosing capabilities overtime per conveying device and conveyed raw material as a time series of measurements of dosing capabilities, and measuring dosing speeds depending on the time per conveying device and conveyed raw material, detecting first anomaly and dosing errors of the dosing device based on the measured relative dosing errors, wherein the detection of anomaly and dosing errors is triggered by exceeding the measured deviation of a defined threshold value per weighing device and conveyed raw material, detecting second anomaly and dosing errors of the dosing device, wherein the topological distance between the measured times series of the dosing capabilities over a period of time is determined as a distance matric by means of dynamic temporal standardisation, and the measured time series are clustered into disjoint clusters based on the measured distance matrix by means of a statistical data mining unit, and wherein measured time series of a first cluster index a dosing operation in a standard range and measured time series of a second cluster index a dosing operation outside the standard range, and if first and/or second anomaly and dosing errors (11121/11122) of the dosing device are detected, transmitting the measured dosing speeds depending on the time as input data patterns to a machine-learning unit and adapting the dosing system parameters of the dosing device by means of the electronic system controller based on the starting values of the machine-learning unit, wherein the machine-learning unit classifies the input patterns by means of learnt patterns and generates corresponding dosing parameters.