Life Science — Pharmaceutical Demand Forecasting Model Training Pipeline

Overview

The primary purpose of this DAG is to manage the training of machine learning models aimed at predicting the demand for pharmaceutical products. It leverages historical sales data, market trends, and external factors such as seasonal influences and regulatory changes to enhance the accuracy of demand forecasts. The data ingestion pipeline begins with the collection of various input sources, including ERP transaction logs, market research reports, and competitor analysis data. Once ingested, the

The primary purpose of this DAG is to manage the training of machine learning models aimed at predicting the demand for pharmaceutical products. It leverages historical sales data, market trends, and external factors such as seasonal influences and regulatory changes to enhance the accuracy of demand forecasts. The data ingestion pipeline begins with the collection of various input sources, including ERP transaction logs, market research reports, and competitor analysis data. Once ingested, the data undergoes preprocessing steps, including data cleaning, normalization, and feature engineering to prepare it for model training. The DAG applies cross-validation techniques to evaluate model performance, ensuring that the models are robust and generalizable. The outputs of this process are accessible via an API, allowing planning teams to retrieve real-time demand forecasts and adjust their strategies accordingly. Key performance indicators (KPIs) such as forecast accuracy, model precision, and recall are monitored to continuously improve model performance. This workflow ultimately delivers significant business value by enabling pharmaceutical companies to optimize inventory levels, reduce stockouts, and enhance overall supply chain efficiency.

Part of the Market & Trading Intelligence solution for the Life Science industry.

Use cases

• Improves demand planning accuracy in pharmaceutical supply chains
• Reduces costs associated with overstock and stockouts
• Enhances responsiveness to market changes and trends
• Increases operational efficiency through data-driven decisions
• Supports regulatory compliance with accurate forecasting