High Tech — Demand Forecast Model Training Pipeline

Overview

The purpose of this DAG is to train advanced demand forecasting models tailored for the high-tech industry, leveraging extracted features from various data sources. The architecture consists of an ingestion pipeline that collects data from diverse inputs, including historical sales data, market trends, and customer behavior analytics. The processing steps involve feature extraction, model training, and validation, where models are evaluated against validation datasets to measure their performanc

The purpose of this DAG is to train advanced demand forecasting models tailored for the high-tech industry, leveraging extracted features from various data sources. The architecture consists of an ingestion pipeline that collects data from diverse inputs, including historical sales data, market trends, and customer behavior analytics. The processing steps involve feature extraction, model training, and validation, where models are evaluated against validation datasets to measure their performance using metrics such as Mean Absolute Percentage Error (MAPE). Quality controls are integrated to monitor model effectiveness, and if performance falls below acceptable thresholds, a retraining mechanism is activated to incorporate new data and refine the models. The outputs of this DAG include trained forecasting models, performance reports, and updated feature sets. Monitoring key performance indicators (KPIs) such as MAPE and model accuracy ensures continuous improvement and alignment with business objectives. The business value lies in providing high-tech companies with accurate demand forecasts, enabling better inventory management, optimized resource allocation, and enhanced strategic planning.

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

Use cases

• Enhances inventory management with accurate demand predictions.
• Reduces costs by optimizing resource allocation.
• Improves strategic planning with data-driven insights.
• Increases responsiveness to market changes and trends.
• Boosts customer satisfaction through better product availability.