AI-Enhanced Sales Forecasting System
Company Overview
The client is a mid-sized retail company operating across multiple regions and channels. With thousands of SKUs and rapidly changing consumer demand, accurate sales forecasting was critical to ensure product availability, optimize inventory, and avoid revenue loss. Their existing forecasting relied on spreadsheets and manual adjustments, which lacked consistency and scalability.
Problem
The company’s traditional forecasting approach couldn’t keep pace with business growth.
Forecasts were:
Forecasts were:
Slow
reports took days to prepare, delaying decisions.
Inconsistent
accuracy varied widely by product category and region.
Opaque
business leaders couldn’t interpret why models produced certain results.
As a result, inventory imbalances led to stockouts in peak demand periods and overstock in slower months, increasing operational costs and lost sales opportunities. The company needed a scalable, explainable forecasting system powered by AI.
Solution
We designed and delivered an AI-driven forecasting platform that combined advanced ML with natural language explainability. The solution automated the full forecasting cycle — from ingesting raw sales and promotion data to producing business-ready predictions.
Key Technologies Used
Python, MLflow
experiment tracking, model lifecycle management.
Cloud Data Warehouse
centralized scalable data storage and processing.
LLMs (Large Language Models)
natural language layer to interpret model results.
BI Dashboards
interactive visualizations for demand forecasts and scenario testing.
Team Composition
2 Data Scientists — developed and tuned ML models, set up MLflow pipelines.
1 Data Engineer — designed ETL processes and ensured reliable data ingestion.
1 Project Manager — coordinated tasks, managed delivery milestones.
1 Data Engineer — designed ETL processes and ensured reliable data ingestion.
1 Project Manager — coordinated tasks, managed delivery milestones.
Responsibilities & Execution
Consolidated sales, promotion, and seasonal data into a centralized warehouse
Automated preparation of demand drivers (holidays, marketing campaigns, external factors).
Built and tuned multiple ML models for SKU- and region-level demand prediction.
Implemented MLflow to compare models, manage versions, and streamline retraining.
Used LLMs to translate model outputs into plain-language insights (“Sales of Product A are projected to rise by 12% due to regional promotions”).
Built dashboards to monitor forecast accuracy, confidence intervals, and “what-if” scenarios.
Results
- 40–60% faster setup and tuning of forecasting models.
- Improved accuracy across core SKUs, reducing stockouts and surplus inventory.
- Automatic forecast interpretation made outputs understandable for non-technical teams.
- Scalable foundation — new product lines and data sources could be onboarded in hours, not weeks.
- Better business alignment — leadership teams gained confidence in AI-driven forecasts and could make proactive decisions.