33% Revenue Growth Through AI-Powered Demand Forecasting

Problem Statement

THE CHALLENGE

The supply chain team relied on traditional forecasting methods that generated consistently high error rates, leading to production misalignment and inventory imbalances. Overproduction tied up working capital in unsold stock, while underproduction resulted in frequent stockouts and lost sales opportunities. The inability to accurately predict demand across a high SKU count portfolio meant the team operated reactively rather than strategically. Critical external market factors-seasonal demand shifts, raw material price fluctuations, competitor movements-were not captured in legacy forecasting models, compounding forecast inaccuracy.

Challenges

High Forecasting Error Rate

18-22% average error in demand predictions using traditional statistical methods.
Production Planning Inefficiency

Frequent overproduction and stockouts due to demand-supply mismatch across product lines.
Complex SKU Portfolio

High number of chemical product variants made manual forecasting unscalable and error-prone.
Missing External Factors

Market trends, seasonal patterns, economic indicators, and competitor activity not integrated into forecasting models.

Solution

THE SOLUTION

10-year historical data integration from SAP and Tally systems covering all SKU sales patterns.
Comprehensive data engineering including missing data imputation, outlier removal, and feature extraction for internal and external variables.
Hybrid LSTM-based forecasting model designed to capture temporal dependencies and seasonal demand patterns across product categories.
Real-time SAP dashboard integration delivers rolling forecasts updated automatically as new sales data flows in.

WHAT CHANGED AFTER