High prediction accuracy
Optimized manufacturing
Cost savings
Improved output quality
The challenge
Precision in fertilizer manufacturing
A Fortune 100 fertilizer manufacturer wanted to ensure that its products met strict quality standards for essential nutrients like potassium, nitrogen, and phosphorus. The final product’s quality is heavily influenced by critical process parameters (CPPs) such as temperature, electric current, and flow volume. Predicting output based on CPPs is crucial for maintaining consistency within acceptable tolerance levels.
Key challenges
Too many CPP parameters for manual monitoring
Required a robust predictive approach
Needed a precise yet scalable model
The solution
Optimizing fertilizer production
Data-driven quality control
Identified key process drivers
Built random forest models
Unified data
Predictive accuracy and refinement
Validated model accuracy on test data
Iterated and refined models
Optimized predictions
Implementation approach
1
Data integration
Unified CPPs and lab data
Standardized inputs
Built a reliable pipeline
2
Model development
Created random forest models
Applied feature engineering
Optimized accuracy
3
Optimization and deployment
Manufacturing insights
Performance tuning
Real-world testing
The impact
High accuracy
98.4% accuracy in water-soluble fertilizers
99.9% accuracy in other nutrients
Cost and efficiency gains
Multi-million-dollar savings
Optimized manufacturing
Reduced waste
Data-driven insights
Identified process parameters
Improved output control
Smarter decision-making
Looking ahead
Innovation focus
Continuous improvement in product accuracy
Sustainability goals
Enhance cost savings and waste reduction
Data-driven growth
Leverage insights for smarter decision-making
