Method and apparatus for controlling production lines based on prediction of product lifetime total sales volume through hybrid machine learning model

An apparatus for controlling production lines based on prediction of a product lifetime total sales volume through a hybrid machine learning model, the apparatus comprising:

• one or more processors;
• and
• a memory which stores one or more programs executed by the one or more processors to perform:
• (a) constructing a first model configured to predict a product lifespan on the basis of modeling data in a machine learning model;
• (b) constructing a second model configured to predict a product total sales volume on the basis of product lifespan information predicted by the first model and the modeling data in the machine learning model;
• (c) predicting a lifetime lifespan of test data and creating lifetime lifespan prediction data by inputting the test data into the first model;
• (d) predicting a lifetime total sales volume by inputting the lifetime lifespan prediction data and the test data into the second model;
• (e) transmitting a control signal to the production lines, such that a production volume of a new product is directly controlled, in real time, based on the predicted lifetime total sales volume;
• and
• (f) repeating steps (a) through (e) as the modeling data being updated in real time.

A computer-implemented method for controlling an inventory management scheduler based on prediction of a product lifetime total sales volume through a hybrid machine learning model, in the method, a memory stores one or more programs executed by one or more processors to perform the steps of:

• (a) constructing a first model configured to predict a product lifespan on the basis of modeling data in a machine learning model;
• (b) constructing a second model configured to predict a product total sales volume on the basis of product lifespan information predicted by the first model and the modeling data in the machine learning model;
• (c) predicting a lifetime lifespan of test data and creating lifetime lifespan prediction data by inputting the test data into the first model;
• (d) predicting a lifetime total sales volume by inputting the lifetime lifespan prediction data and the test data into the second model;
• (e) transmitting a control signal to the inventory management scheduler, in real time, based on the predicted lifetime total sales volume;
• and
• (f) repeating steps (a) through (e) as the modeling data being updated in real time.