Consumer Products — Consumer Products Demand Forecast Feature Generation

Overview

The primary purpose of this DAG is to extract and generate features from various data sources to support demand forecasting models in the consumer products industry. The data ingestion pipeline begins with the collection of relevant datasets, including sales transactions, inventory levels, and promotional activities. Once ingested, the data undergoes a series of processing and transformation steps, which include data cleaning, feature extraction, aggregation, and normalization. During the transf

The primary purpose of this DAG is to extract and generate features from various data sources to support demand forecasting models in the consumer products industry. The data ingestion pipeline begins with the collection of relevant datasets, including sales transactions, inventory levels, and promotional activities. Once ingested, the data undergoes a series of processing and transformation steps, which include data cleaning, feature extraction, aggregation, and normalization. During the transformation phase, the DAG applies specific algorithms to derive meaningful features such as seasonality indices and trend indicators, which are crucial for accurate demand predictions. Quality controls ensure that the features generated meet predefined standards for consistency and reliability. The outputs of this DAG are stored in a data warehouse, making them readily accessible for model training and further analysis. Key performance indicators (KPIs) include the number of features generated and the processing time, allowing for effective monitoring of the DAG's performance. By providing high-quality features, this DAG significantly enhances the predictive capabilities of demand forecasting models, leading to improved inventory management and optimized supply chain operations.

Part of the Scientific ML & Discovery solution for the Consumer Products industry.

Use cases

• Improved accuracy in demand forecasting models
• Enhanced inventory management through better predictions
• Increased operational efficiency in supply chain processes
• Reduced stockouts and overstock situations
• Data-driven decision-making for marketing strategies