A method for modelling and quantitatively evaluating the global quality level expected of an ophthalmic lens in real time, said lens having particular lens and environment parameters, and being produced by a digital lens manufacturing process, said modelling and evaluation method being computer-implemented and comprising the following steps: - based on a first selected, representative learning set of measured lenses produced by the manufacturing process, setting up, with a view to automatically and with high repeatability reproduce an expert's evaluation work, a single computable criterion of the global manufacturing quality of each lens, providing for every manufactured lens a global quality quantification that is evaluated from the deviation map between the reference or theoretical respectively through or surface optical power map and the actual measured and corrected through or surface respectively optical power map, said correction of the actual measured power map being performed based on the knowledge of the systematic deviations expected during one or more of the manufacturing process steps, independently controlled and evaluated, the deviation map corresponding to the evaluation of said deviations in an appropriate and sufficiently large number of measurement points spread over the lens surface; - based on a second selected representative learning set of measured lenses produced by the manufacturing process, obtaining and optimizing by a learning method a mathematical model providing a transformation between the lens and environment parameters of each lens manufactured and the expected global quality criterion if this lens had been produced using the manufacturing process in a given, generally stable, adjusted and fixed state, by minimizing the difference between the model output for said global quality criterion of the lens and the actual computed value of the global quality criterion of the lens, based on said second selected learning set of measured lens, said learning method comprising, based on said second selected learning set of measured lens, the identification of relevant input lens and environment parameters playing a role on the quality of the lens produced during normal production, so that the model output of the unique measurable global quality criterion of each lens will be based on a set of parameters selected from among the relevant input lens and environment parameters. - based on a first selected, representative learning set of measured lenses produced by the manufacturing process, setting up, with a view to automatically and with high repeatability reproduce an expert's evaluation work, a single computable criterion of the global manufacturing quality of each lens, providing for every manufactured lens a global quality quantification that is evaluated from the deviation map between the reference or theoretical respectively through or surface optical power map and the actual measured and corrected through or surface respectively optical power map, said correction of the actual measured power map being performed based on the knowledge of the systematic deviations expected during one or more of the manufacturing process steps, independently controlled and evaluated, the deviation map corresponding to the evaluation of said deviations in an appropriate and sufficiently large number of measurement points spread over the lens surface; - based on a second selected representative learning set of measured lenses produced by the manufacturing process, obtaining and optimizing by a learning method a mathematical model providing a transformation between the lens and environment parameters of each lens manufactured and the expected global quality criterion if this lens had been produced using the manufacturing process in a given, generally stable, adjusted and fixed state, by minimizing the difference between the model output for said global quality criterion of the lens and the actual computed value of the global quality criterion of the lens, based on said second selected learning set of measured lens, said learning method comprising, based on said second selected learning set of measured lens, the identification of relevant input lens and environment parameters playing a role on the quality of the lens produced during normal production, so that the model output of the unique measurable global quality criterion of each lens will be based on a set of parameters selected from among the relevant input lens and environment parameters.