Predicting supply chain policies using machine learning

A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to predict supply chain policies using machine learning, wherein, when executed, the instructions cause the processor to:

• generate, using simulation software, simulated training data by performing simulations, for a first set of products and variety of replenishment policies, to generate simulated supply chain metric data labels;
• generate mixed training data by combining the simulated trained data with historic training data, wherein the historic training data corresponds to historic supply chain metric data labels for a second set of products;
• train, via supervised learning, a machine learning model, using the mixed training data via the simulated supply chain metric data labels and the historic supply chain metric data labels, to generate supply chain predictions for one or more products;
• generate, by the trained machine learning model, a plurality of supply chain metric predictions for a first product using a plurality of candidate replenishment policies for the first product,
• wherein, for each of the candidate replenishment policies, the trained machine learning model generates a set of supply chain metric predictions that represents predicted performance of the first product, and
• wherein the simulation software performs the simulations without generating supply chain predictions via the trained machine learning model;
• select a candidate replenishment policy with a corresponding supply chain metric prediction that meets a criteria;
• and
• implement the selected replenishment policy for the first product within an inventory system, wherein one or more physical locations are restocked with the first product based on restocking parameters defined by the selected replenishment policy.

A system for predicting supply chain policies using machine learning, the system comprising:

• a processor;
• and
• a memory storing instructions for execution by the processor, the instructions configuring the processor to:
• generate, using simulation software, simulated training data by performing full-scale simulations, for a first set of products and variety of replenishment policies, to generate simulated supply chain metric data labels;
• generate mixed training data by combining the simulated trained data with historic training data, wherein the historic training data corresponds to historic supply chain metric data labels for a second set of products;
• train, via supervised learning, a machine learning model, using the mixed training data via the simulated supply chain metric data labels and the historic supply chain metric data labels, to generate supply chain predictions for one or more products;
• generate, by the trained machine learning model, a plurality of supply chain metric predictions for a first product using a plurality of candidate replenishment policies for the first product,
• wherein, for each of the candidate replenishment policies, the trained machine learning model generates a set of supply chain metric predictions that represents predicted performance of the first product, and
• wherein the simulation software performs the simulations without generating supply chain predictions via the trained machine learning model;
• select a candidate replenishment policy with a corresponding supply chain metric prediction that meets a criteria;
• and
• implement the selected replenishment policy for the first product within an inventory system, wherein one or more physical locations are restocked with the first product based on restocking parameters defined by the selected replenishment policy.