Machine Learning for Predictive Intelligence
150+ facilities across 40 countries, managing 50,000+ SKUs with annual revenue exceeding $25 billion
42% improvement (68% → 96.5%)
$127M from reduced inventory costs
89% reduction in incidents
Reduced from 3 weeks to 2 days
485% within 18 months
Critical supply chain inefficiencies resulting in $300M annual losses from stockouts, excess inventory, and suboptimal production scheduling. Legacy ERP systems couldn't handle the complexity of global demand forecasting with 2,000+ variables.
Built distributed ML pipeline on AWS SageMaker processing 2TB daily data with ensemble forecasting combining XGBoost, LSTM networks, and Prophet. Created feature engineering pipeline with 3,500+ features including weather data, economic indicators, and social media sentiment.
Distributed ML pipeline on AWS SageMaker
Apache Kafka for real-time data ingestion (500K events/sec), Spark clusters for distributed preprocessing
Hierarchical time series forecasting with reconciliation, Multi-objective optimization using genetic algorithms
REST APIs for ERP integration (SAP, Oracle), GraphQL for frontend applications
Transformed global supply chain operations with 42% improvement in forecast accuracy, $127M annual savings, and 485% ROI within 18 months.
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