Gross margin recovery with supervised machine learning technique

A specialized computing device comprising:

• a processor;
• a data bus coupled to the processor;
• and
• a non-transitory, computer-readable storage medium coupled to the data bus, and storing computer program code, wherein the computer program code interacts with a plurality of computer operations and comprises instructions executable by the processor such that the processor is configured to:
• generate an internal factors gross margin prediction using one or more product attributes;
• generate an external factors gross margin prediction using one or more customer attributes and customer sales pipeline information;
• train a plurality of demand forecast models using a training dataset, the training dataset comprising product demand data across a plurality of periods of time, the product demand data across the plurality of periods of time allowing the plurality of demand forecast models to be trained to generate respective forecasted demand;
• select a demand forecast model from the plurality of demand forecast models based upon the respective forecasted demand generated using the product demand dataset, the demand forecast model providing a best mean average percent error (MAPE);
• and,
• generate the predicted gross margin for the organization using the demand forecast model, the demand forecast model the internal factors gross margin prediction and the external factors gross margin prediction;
• and wherein
• the generating the internal factors is performed by an internal gross margin processor of a gross margin system, generating the external factors is performed by an external gross margin processor of the gross margin system and generating the predicted gross margins is performed by a gross margin processor of the gross margin system, the gross margin system is specifically configured to perform a gross margin predictions operation;
• and,
• the generating the internal factors, generating the external factors and generating the predicted gross margins are performed via a supervised machine learning operation, the supervised machine learning operation using internal parameters and external parameters when generating the predicted gross margin for the organization using the demand forecast model.

A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

• generating an internal factors gross margin prediction using one or more product attributes;
• generating an external factors gross margin prediction using one or more customer attributes and customer sales pipeline information;
• training a plurality of demand forecast models using a training dataset, the training dataset comprising product demand data across a plurality of periods of time, the product demand data across the plurality of periods of time allowing the plurality of demand forecast models to be trained to generate respective forecasted demand;
• selecting a demand forecast model from the plurality of demand forecast models based upon the respective forecasted demand generated using the product demand dataset, the demand forecast model providing a best mean average percent error (MAPE);
• and,
• generating the predicted gross margin for the organization using the demand forecast model, the demand forecast model the internal factors gross margin prediction and the external factors gross margin prediction;
• and wherein
• the generating the internal factors is performed by an internal gross margin processor of a gross margin system, generating the external factors is performed by an external gross margin processor of the gross margin system and generating the predicted gross margins is performed by a gross margin processor of the gross margin system, the gross margin system is specifically configured to perform a gross margin predictions operation;
• and,
• the generating the internal factors, generating the external factors and generating the predicted gross margins are performed via a supervised machine learning operation, the supervised machine learning operation using internal parameters and external parameters when generating the predicted gross margin for the organization using the demand forecast model.