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 system and method for automatically predicting deviation on a metric of a use-case and deriving interconnections between metrics for generating action recommendations is provided. The system includes a deviation management system 104 which captures data from a plurality of external sources and internal sources and comprises of a deviation management platform 106 and a deviation management environment 108. The system includes various computation modules which work the deviation management platform 106 to provide a deviation management service to a set of clients that are associated with that service. The service and its users are specific to use-case, wherein the use-case is specified by a client device 116 inside the system. The system comprises of external data which is horizontal across a plurality of deviation management services and internal data which is specific to every deviation management service.