Supply Chain Analytics

Increasing Manufacturing Throughput through Machine Learning

An Early Warning System based on Logistic Regression was created to predict downtime in processing line for a leading snack foods company. This resulted in $2 million savings in cost per fryer and potential save of $50 million after rollout to 30 plants across the US.

Machine Learning Driven Sales Trend Detection at Scale

A Machine Learning Rules & Notification engine was built for a leading transnational consumer goods company, which helped make timely market interventions in supply chain planning, reducing the stock-out rate by 5% and inventory costs by 2%.

Precise Demand Forecasting for Jewelry SKUs

Baseline forecasts for 3000 SKU’s were generated for the world’s third-largest jewelry manufacturer. New SKUs were automated. This led to a 13% increase in overall demand forecast accuracy and reduced supply planning costs by 2%.

Data Harmonization for Efficient Digital Supply Chain

A data ecosystem from multiple disjointed unstructured and structured data sources was built for the world’s largest entertainment production company, which resulted in a savings of half a million dollars per year from revenue leaks.

Demand Forecasting to Improve Service Delivery

Combined forecasts from different algorithms were done to improve demand forecasting accuracy for a leading home appliances and repair services provider. Demand forecasting accuracy went up to 92% from 65%, reducing lead time by 20%. A 4% increase in First Time Completes resulted in incremental revenue of $5 million per annum due to reduced cancellations.

Scroll to Top