Packaging Material
B2B Demand Forecasting, Sales Alerts and Profitability Management System
AI-powered analytics platform leveraging machine learning to optimize inventory, forecast revenue, identify sales gaps, and improve margins through SKU-level forecasting and proactive sales interventions.
100% improvement in forecasting accuracy
Improvement
The Problem
The client aimed to optimize inventory and working capital, forecast revenue and analyze gaps, and improve margins by tracking high-margin sales drops and optimizing bidding strategies.
Key Challenges
- Optimizing inventory levels and unlocking working capital.
- Providing actionable insights to sales teams for timely order tracking and gap closure.
- Forecasting revenue for upcoming quarters.
- Tracking drop in high-margin sales and alerting sales teams by optimizing bidding strategies and proactive interventions.
Our Solution
Built on a robust AI-powered analytics platform leveraging advanced machine learning across modules: Sales Prediction and Gap Analysis, and SKU-Level Forecasting.
- Machine Learning
- Time-series Models
- Tableau
- Power BI
Implementation Approach
- Sales Prediction and Gap Analysis: Aggregated historical sales, customer orders, and procurement notes to predict sales orders using machine learning models. Lead scoring prioritized high-impact opportunities. Compared predicted sales with actuals to highlight discrepancies. Built dashboards and weekly reports in Tableau/Power BI for proactive sales alerts.
- SKU-Level Forecasting: Forecasted 16,000 SKUs with time-series ML models, incorporating seasonality, market trends, and order probabilities to achieve 100% accuracy improvement. Implemented dynamic inventory norms for depots and finished goods to optimize raw material procurement and reduce wastage.
Results & Impact
- 100% Improvement in Forecasting Accuracy: Achieved SKU-level accuracy for precise revenue forecasting and optimized resource allocation.
- Proactive Sales Interventions: Proactive alerts helped sales teams identify gaps and improve order tracking efficiency.
- Reduced Working Capital and Wastage: Enhanced forecasting and inventory norms minimized excess stock and raw material costs.
- Improved Profitability: Data-driven bidding and prioritization increased ROI, improved bid success rates, and delivered sustainable growth.