AI-Driven Demand Forecasting System for a Retail Chain

Project Overview

Our client, a leading retail enterprise with over 250 stores in the Middle East, sought to improve their demand forecasting capabilities. Facing issues with inaccurate sales predictions, frequent stockouts, and promotional inefficiencies, they turned to Infoslab to design and implement an AI-powered demand forecasting solution.

Industry

Retail and E-commerce

Challenge

The client's traditional forecasting models could not keep up with:

• Fast-changing consumer behavior

• Regional demand differences

• Holiday and promotion impacts

• External factors such as weather and economic shifts

Requirement

This challenge requires a dynamic, AI-based forecasting engine to accurately predict store- and product-level demand across multiple regions and time horizons.

Solution & Integration

Infoslab's AI and Data Engineering teams collaborated to deliver a custom solution:

• Data Consolidation

  Merged sales, pricing, promotions, store details, weather, and event data.

• Advanced Feature Engineering

  Created 100+ predictive features capturing seasonality, promotions, regional events, and weather effects.

• Model Development

  Built ensemble models using XGBoost, Temporal Fusion Transformers (TFT), and Prophet for multi-horizon forecasting.

• AutoML Pipelines

  Automated model retraining and hyperparameter tuning using Databricks and MLflow.

• API-Based Deployment

  Deployed real-time prediction APIs for marketing, inventory, and store operations teams.

• Monitoring & Alerting

  Implemented drift detection and automated retraining pipelines to ensure forecast relevance

Business Value Delivered

• +28% improvement in forecast accuracy

• 15% stockout reduction, 12% overstock reduction-

• 7% increase in quarterly revenue

• Real-time daily forecast updates vs monthly legacy reports

• Better promotion planning and campaign optimization

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