Computerized systems and methods for predicting and managing scrap

A computer-implemented method for predicting and managing predicted scrap items, comprising:

• receiving a request to initiate a process related to predicting scrap likelihood associated with a plurality of stock keeping units (SKUs);
• in response to receiving the request, preparing input data associated with each SKU of the plurality of SKUs;
• preparing information related to the request by:
• inputting the input data associated with each SKU of the plurality of SKUs into a trained prediction model;
• outputting, from the trained prediction model, a prediction value for each SKU of the plurality of SKUs;
• measuring the trained prediction model by calculating a set of values;
• and
• generating a matrix comprising at least a subset of the calculated set of values;
• selecting a first value of the calculated set of values corresponding to a threshold prediction value of a plurality of threshold values;
• comparing, for each SKU of the plurality of SKUs, the prediction value output from the trained prediction model to the threshold prediction value;
• determining, based on the comparison, a scrap likelihood for each SKU of the plurality of SKUs;
• and
• based on the scrap likelihood indicating likely to be scrapped, performing at least one of a plurality of preventative actions.

A computer-implemented system for predicting and managing predicted scrap items, comprising:

• one or more memory devices storing instructions;
• one or more processors configured to execute the instructions to perform operations comprising:
• receiving a request to initiate a process related to predicting scrap likelihood associated with a plurality of stock keeping units (SKUs);
• in response to receiving the request, preparing input data associated with each SKU of the plurality of SKUs;
• preparing information related to the request by:
• inputting the input data associated with each SKU of the plurality of SKUs into a trained neural network;
• outputting, from the trained neural network, a prediction value for each SKU of the plurality of SKUs;
• measuring the trained neural network by calculating a set of values;
• and
• generating a matrix comprising at least a subset of the calculated set of values;
• selecting a first value of the calculated set of values corresponding to a threshold prediction value of a plurality of threshold values;
• comparing, for each SKU of the plurality of SKUs, the prediction value output from the trained neural network to the threshold prediction value;
• determining, based on the comparison, a scrap likelihood for each SKU of the plurality of SKUs;
• and
• based on the scrap likelihood indicating likely to be scrapped, performing at least one of a plurality of preventative actions.