System and method for predicting average inventory with new items

A system for predicting a stock on hand for predefined markdown plans, comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
• aggregating, by a processor of a computing device, sales data of a plurality of items in a category from a database, the items comprising training items and new items, and the sales data of each of the items comprising attributes and an item-based Point-of-Sale (PoS) data;
• identifying, by the processor using a set of predefined rules, a data set of similar items on the training items for each of the new items, the set of predefined rules comprising a first stage similarity module, a second stage similarity module, and a second stage classification module;
• obtaining target metrics for each of the new items, the target metrics being turn predictions from the data set of the similar items;
• calculating mean errors of the turn predictions to identify a set of turn predictions with mean errors lower than a dynamic threshold;
• obtaining an ultimate turn prediction for each of the new items by averaging the set of turn predictions;
• and
• predicting an average inventory for each of the new items based on the ultimate turn prediction.

A non-transitory computer-readable storage medium having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:

• aggregating, by a processor of a computing device, sales data of a plurality of items in a category from a database, the items comprising training items and new items, and the sales data of each of the items comprising attributes and an item-based Point-of-Sale (PoS) data;
• identifying, by the processor using a set of predefined rules, a data set of similar items on the training items for each of the new items, the set of predefined rules comprising a first stage similarity module, a second stage similarity module, and a second stage classification module;
• obtaining target metrics for each of the new items, the target metrics being turn predictions from the data set of the similar items;
• calculating mean errors of the turn predictions to identify a set of turn predictions with mean errors lower than a dynamic threshold;
• obtaining an ultimate turn prediction for each of the new items by averaging the set of turn predictions;
• and
• predicting an average inventory for each of the new items based on the ultimate turn prediction.