Boosted scrap price forecasting by +35% for a U.S. metal recycler
Built a predictive analytics + real-time dashboard platform to forecast pricing, plan inventory, and optimize haulage routes.
Metal recycling + industrial haulage Case Study
THE CHALLENGE
What was holding them back
Operational pain
Volatile scrap prices + inconsistent inbound demand.
Business risk
Margin leakage from reactive pricing and inefficient pickups.
Why tools failed
Manual data handling could not produce timely, usable insights.
CLIENT SNAPSHOT
About the client
THE SOLUTION
Our Metal recycling + industrial haulage Solution
Unified Data Lake + Automated ETL
- Consolidated yard, inventory, and haulage signals into one source.
- Automated cleansing for grades, weights, pricing, and locations.
Price Trend Forecasting Engine
- ML models predicted short-term scrap price movement patterns.
- Confidence-based outputs to support buy/sell pricing decisions.
Inventory + Margin Intelligence
- Forecasted inbound volume by grade and site.
- Highlighted low-margin materials and stock imbalances early.
Route Planning Optimization
- Optimized pickup routes using demand and historical patterns.
- Reduced deadhead miles and improved driver utilization.
Real-time Dashboards + KPI Alerts
- Power BI dashboards for ops, yard managers, and leadership.
- Alerts for exceptions: price swings, capacity risk, route inefficiencies.
THE IMPACT
Measurable Results
Forecasting: +35% accuracy for scrap price predictions
Cost Efficiency: -20% operating cost via smarter routes
Inventory Planning: Better grade-level stocking and yard capacity
Decision Speed: Real-time KPIs replaced manual spreadsheet updates
Profitability: Improved pricing discipline and margin visibility
Time to Value: Insights available daily, not quarterly reports
TECH STACK
