A computer implemented method for generating a mold model for production predictive control, the method comprising performing, by a computer device, once a mold has been inserted in a first injection molding machine, in order to obtain, injected given parts, or molded pieces, the following steps: a) receiving i. a first group of parameters from a plurality of injection molding machine sensors about performance of a plurality of injection cycles of said first injection molding machine, wherein said injection cycles are performed with the first injection molding machine in a set of configurations providing at least different qualities of the injected given parts or different operation points, and wherein said parameters of the first group include at least one of Hydraulic Pressure, Screw Position, Screw Speed or Rotational Speed; and ii. a second group of parameters from a plurality of mold sensors relating to a mold cavity shaped for molding an injected given part in said first injection molding machine, said parameters of the second group at least including pressure and/or temperature evolution of the mold cavity and of the mold along each of the injection cycles in the mold cavity; b) classifying each injection cycle of said plurality of injection cycles considering at least the received first and/or second group of parameters and quality or characteristics of the injected given parts; c) processing the received first and second group of parameters by implementing therein one or more algorithms to remove undesired or irregular data values in said parameters; d) merging the processed first group of parameters with the processed second group of parameters providing a global group of processed parameters; e) executing a machine learning algorithm on the global group of processed parameters generating an extended mold model; and f) using said generated extended mold model for further monitoring and control of the mold in further injection processes in the first injection molding machine and/or for optimizing a production process of the mold in the first molding machine. a) receiving i. a first group of parameters from a plurality of injection molding machine sensors about performance of a plurality of injection cycles of said first injection molding machine, wherein said injection cycles are performed with the first injection molding machine in a set of configurations providing at least different qualities of the injected given parts or different operation points, and wherein said parameters of the first group include at least one of Hydraulic Pressure, Screw Position, Screw Speed or Rotational Speed; and ii. a second group of parameters from a plurality of mold sensors relating to a mold cavity shaped for molding an injected given part in said first injection molding machine, said parameters of the second group at least including pressure and/or temperature evolution of the mold cavity and of the mold along each of the injection cycles in the mold cavity; i. a first group of parameters from a plurality of injection molding machine sensors about performance of a plurality of injection cycles of said first injection molding machine, wherein said injection cycles are performed with the first injection molding machine in a set of configurations providing at least different qualities of the injected given parts or different operation points, and wherein said parameters of the first group include at least one of Hydraulic Pressure, Screw Position, Screw Speed or Rotational Speed; and ii. a second group of parameters from a plurality of mold sensors relating to a mold cavity shaped for molding an injected given part in said first injection molding machine, said parameters of the second group at least including pressure and/or temperature evolution of the mold cavity and of the mold along each of the injection cycles in the mold cavity; b) classifying each injection cycle of said plurality of injection cycles considering at least the received first and/or second group of parameters and quality or characteristics of the injected given parts; c) processing the received first and second group of parameters by implementing therein one or more algorithms to remove undesired or irregular data values in said parameters; d) merging the processed first group of parameters with the processed second group of parameters providing a global group of processed parameters; e) executing a machine learning algorithm on the global group of processed parameters generating an extended mold model; and f) using said generated extended mold model for further monitoring and control of the mold in further injection processes in the first injection molding machine and/or for optimizing a production process of the mold in the first molding machine.