Life Science — What-If Scenario Simulation for Strategic Planning

Overview

The primary purpose of this DAG is to facilitate the simulation of 'what-if' scenarios for demand and supply in the pharmaceutical industry. By integrating external variables such as promotional activities and market trends, this workflow leverages advanced predictive modeling to generate actionable insights. The data ingestion pipeline begins with the collection of historical sales data, promotional calendars, and market trend reports. These inputs are processed through a series of analytical s

The primary purpose of this DAG is to facilitate the simulation of 'what-if' scenarios for demand and supply in the pharmaceutical industry. By integrating external variables such as promotional activities and market trends, this workflow leverages advanced predictive modeling to generate actionable insights. The data ingestion pipeline begins with the collection of historical sales data, promotional calendars, and market trend reports. These inputs are processed through a series of analytical steps that include data cleansing, feature engineering, and scenario modeling. The processing logic employs statistical algorithms to forecast demand under various conditions, allowing teams to visualize potential outcomes based on different market strategies. The outputs of this DAG include detailed simulation reports, recommended inventory levels, and strategic action plans tailored to optimize stock levels and minimize costs. Monitoring key performance indicators (KPIs) such as forecast accuracy, inventory turnover rates, and cost savings is essential to ensure the effectiveness of the simulations. Overall, this DAG provides significant business value by enabling pharmaceutical companies to proactively manage their supply chains, enhance decision-making, and improve financial performance.

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

Use cases

• Improved accuracy in demand forecasting
• Enhanced ability to respond to market changes
• Reduction in excess inventory and associated costs
• Informed decision-making through data-driven insights
• Increased agility in strategic planning processes