A method for monitoring a production plant for the treatment of beverages, comprising the following steps: - selecting a type-specific data set with initial property data of a beverage to be treated and target status data of the production plant; - determining at least three batch-specific data sets with actual status data of the production plant and with actual property data of the beverage, in each case by their sensory measurement during the treatment of a batch of the beverage; - electronically evaluating the type-specific and batch-specific data sets in real time in a prediction model based on at least one self-learning algorithm; and - outputting user information comprising a remaining production time predicted according to real-time evaluation until the end of the batch and/or until an intermediate cleaning or final cleaning is likely to be required, wherein an electronic evaluation unit for executing the prediction model is programmed with the at least one self-learning algorithm, so that the respectively selected type-specific data set and the batch-specific data sets obtained during the treatment of the batch are calculated together in real time and the user information is output on this basis, wherein the evaluation unit accesses a database with historical batch-specific data sets with which batch-specific data sets determined during the treatment of different batches of the same beverage are compared in order to derive characteristic trends and to train the self-learning algorithm, and wherein the type-specific and batch-specific data sets (12, 15) relate to an open primary circuit for the beverage and are determined by measurement with an inlet-side pH sensor, an inlet-side Brix sensor, an inlet-side conductance sensor, at least one flow meter, at least one pressure sensor upstream of a heating stage of the production plant, at least one temperature sensor upstream and downstream of the heating stage and/or at least one temperature sensor upstream and downstream of a holding section of the production plant. - selecting a type-specific data set with initial property data of a beverage to be treated and target status data of the production plant; - determining at least three batch-specific data sets with actual status data of the production plant and with actual property data of the beverage, in each case by their sensory measurement during the treatment of a batch of the beverage; - electronically evaluating the type-specific and batch-specific data sets in real time in a prediction model based on at least one self-learning algorithm; and - outputting user information comprising a remaining production time predicted according to real-time evaluation until the end of the batch and/or until an intermediate cleaning or final cleaning is likely to be required, wherein an electronic evaluation unit for executing the prediction model is programmed with the at least one self-learning algorithm, so that the respectively selected type-specific data set and the batch-specific data sets obtained during the treatment of the batch are calculated together in real time and the user information is output on this basis, wherein the evaluation unit accesses a database with historical batch-specific data sets with which batch-specific data sets determined during the treatment of different batches of the same beverage are compared in order to derive characteristic trends and to train the self-learning algorithm, and wherein the type-specific and batch-specific data sets (12, 15) relate to an open primary circuit for the beverage and are determined by measurement with an inlet-side pH sensor, an inlet-side Brix sensor, an inlet-side conductance sensor, at least one flow meter, at least one pressure sensor upstream of a heating stage of the production plant, at least one temperature sensor upstream and downstream of the heating stage and/or at least one temperature sensor upstream and downstream of a holding section of the production plant.