Method for improving the production process in a technical installation, in which a process-engineering process having at least one process step runs, wherein data records characterising an iteration (PR1, PR2) of a process step and containing values of process variables (pv1, pv2, ..., pvn) are captured on a time-dependent basis (t0, t1, ..., tN) and are stored in a data memory, characterised in that - multivariate trend data of multiple iterations of a process step is used to train a model to detect anomalies, - for each process step the data records of an iteration (PR1) are selected as a test phase (tp) and the data records of at least one further iteration (PR2) as a reference phase (rp), - for each pair of iterations, a deviation between the process values of test and reference phase is determined for each time stamp of the test phase using the model for detecting anomalies and is weighted with an anomaly detection tolerance, - anomaly states are determined from the weighted deviations between the process values of test and reference phase and are evaluated, - a phase similarity measure of the test phase compared to a reference phase is calculated by means of the evaluated anomaly states and is used to analyse and subsequently optimise the process. wherein data records characterising an iteration (PR1, PR2) of a process step and containing values of process variables (pv1, pv2, ..., pvn) are captured on a time-dependent basis (t0, t1, ..., tN) and are stored in a data memory, characterised in that - multivariate trend data of multiple iterations of a process step is used to train a model to detect anomalies, - for each process step the data records of an iteration (PR1) are selected as a test phase (tp) and the data records of at least one further iteration (PR2) as a reference phase (rp), - for each pair of iterations, a deviation between the process values of test and reference phase is determined for each time stamp of the test phase using the model for detecting anomalies and is weighted with an anomaly detection tolerance, - anomaly states are determined from the weighted deviations between the process values of test and reference phase and are evaluated, - a phase similarity measure of the test phase compared to a reference phase is calculated by means of the evaluated anomaly states and is used to analyse and subsequently optimise the process. - multivariate trend data of multiple iterations of a process step is used to train a model to detect anomalies, - for each process step the data records of an iteration (PR1) are selected as a test phase (tp) and the data records of at least one further iteration (PR2) as a reference phase (rp), - for each pair of iterations, a deviation between the process values of test and reference phase is determined for each time stamp of the test phase using the model for detecting anomalies and is weighted with an anomaly detection tolerance, - anomaly states are determined from the weighted deviations between the process values of test and reference phase and are evaluated, - a phase similarity measure of the test phase compared to a reference phase is calculated by means of the evaluated anomaly states and is used to analyse and subsequently optimise the process. <EOS>