High Tech — What-If Scenario Simulation for Forecasting

Overview

The purpose of this DAG is to enable high-tech companies to conduct what-if scenario simulations that assess the impact of variable adjustments on market forecasts. The architecture consists of a data ingestion pipeline that collects relevant data from various sources, including sales data, market trends, and promotional activities. The initial step involves ingesting data from ERP transaction logs, market analysis reports, and promotional calendars. Following ingestion, the data undergoes proce

The purpose of this DAG is to enable high-tech companies to conduct what-if scenario simulations that assess the impact of variable adjustments on market forecasts. The architecture consists of a data ingestion pipeline that collects relevant data from various sources, including sales data, market trends, and promotional activities. The initial step involves ingesting data from ERP transaction logs, market analysis reports, and promotional calendars. Following ingestion, the data undergoes processing where input variables are adjusted to create different scenarios. Each scenario is then simulated to generate forecast outputs, which are compared against baseline forecasts to evaluate potential impacts. Quality control measures are implemented throughout the process to ensure data integrity and accuracy. The results of these simulations are stored in a centralized database for future analysis and can be accessed through an interactive dashboard, allowing users to visualize outcomes and make informed decisions. Key performance indicators (KPIs) such as forecast accuracy, scenario impact assessment, and user engagement metrics are monitored to gauge the effectiveness of the simulations. This DAG delivers significant business value by enabling companies to proactively adapt strategies based on simulated market conditions, ultimately enhancing decision-making and optimizing resource allocation.

Part of the Market & Trading Intelligence solution for the High Tech industry.

Use cases

• Enhances forecasting accuracy through scenario analysis.
• Enables data-driven decision-making in dynamic markets.
• Improves resource allocation based on simulated outcomes.
• Increases agility in responding to market changes.
• Strengthens competitive positioning through informed strategies.