Abstract: A system and a method for configuring an AI pipeline. The system may receive raw information from a database. Further, one or more performance attributes are extracted from the raw information. A meta dataset is generated from the raw information to train a meta learning model. The meta learning model is trained based on the meta dataset, simulation data, and one or more machine learning algorithms. Subsequently, a configuration dataset is predicted based on the meta learning model. Further, a configuration file is generated based on the configuration dataset, the meta learning model, and an error minimization strategy.

Abstract: A system and a method for mitigating risk associated with a machine-generated forecast. The system may 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. Further, the system may determine one or more metrics based on the received data. The one or more metrics are fed to a risk analyzer model. A Demand Planning Risk Index (DPRI) score for an SKU from the set of SKUs may be generated using the risk analyzer model. The DPRI score indicates a risk quotient for the SKU. Further, a forecast adjustment for the SKU with a high risk quotient may be recommended to mitigate the risk associated with the machine-generated forecast of the SKU.

Abstract: A method for integrating a machine learning (ML) model that impacts different factor groups for generating a dynamic recommendation to collectively optimize an objective factor is provided.

Abstract: A method for estimating metric forecasts associated with a plurality of related entities with more accuracy by training and applying a metric forecast entity relationship machine learning (ML) model is provided. The method includes obtaining a first primary and a first secondary entity metric forecast based on historical data of a primary entity metric obtained from primary entity metric device and historical data of secondary entity metric obtained from secondary entity metric device at different instances of time, training metric forecast entity relationship ML model based on relationship between first primary and first secondary entity metric forecast to obtain a trained metric entity relationship ML model that accounts for the relationship between the first primary entity metric forecast and the first secondary entity metric forecast, and estimating a second primary entity metric forecast and a second secondary entity metric forecast based on the trained metric entity relationship ML model.