System and method for mitigating risk associated with a machine-generated forecast

A system for mitigating risk associated with a machine-generated forecast comprising:

• a memory;
• and
• a processor coupled to the memory, wherein the processor is configured to execute program instructions stored in the memory to:
• receive data comprising at least one of product attributes, historical demand for a set of Stock Keeping Units (SKUs), a machine-generated forecast for the set of SKUs, and historical forecast adjustment for the set of SKUs;
• determine one or more metrics based on the received data, wherein the one or more metrics comprises an accuracy, a bias, a difference between accuracy of an adjusted forecast of an SKU when compared to the machine-generated forecast of the SKU;
• training a risk analyzer model using a machine learning algorithm, wherein the machine learning algorithm is trained based on two or more of the determined metrics and labeled data of the set of SKUs categorized into one or more risk classes comprising a high risk and a low risk, and wherein the risk analyzer model is configured to generate a Demand Planning Risk Index (DPRI) score indicating a risk quotient for respective SKUs of the set of SKU;
• generate a Demand Planning Risk Index (DPRI) score for one or more SKUs from the set of SKUs using the risk analyzer model;
• identifying one or more SKUs with high-risk quotient based on respective DPRI scores exceeding a threshold risk quotient;
• and
• recommend a forecast adjustment for the identified one or more SKUs with a high risk quotient to mitigate the risk associated with the machine-generated forecast of those SKU.

A non-transitory computer program product having embodied thereon a computer program for mitigating risk associated with a machine-generated forecast, the computer program product storing instructions for:

• receiving data comprising at least one of product attributes, historical demand for a set of Stock Keeping Units (SKUs), a machine-generated forecast for the set of SKUs, and historical forecast adjustment for the set of SKUs;
• determining one or more metrics based on the received data, wherein the one or more metrics comprises an accuracy, a bias, a difference between accuracy of an adjusted forecast of an SKU when compared to the machine-generated forecast of the SKU;
• training a risk analyzer model using a machine learning algorithm, wherein the machine learning algorithm is trained based on two or more of the determined metrics and labeled data of the set of SKUs categorized into one or more risk classes comprising a high risk and a low risk, and wherein the risk analyzer model is configured to generate a Demand Planning Risk Index (DPRI) score indicating a risk quotient for respective SKUs of the set of SKU;
• generating a Demand Planning Risk Index (DPRI) score for an SKU from the set of SKUs using the risk analyzer model;
• identifying one or more SKUs with high-risk quotient based on respective DPRI scores exceeding a threshold risk quotient;
• and
• recommending a forecast adjustment for the identified one or more SKUs with a high risk quotient to mitigate the risk associated with the machine-generated forecast of those SKUS.