Improve demand forecasts, planning accuracy, and increase the visibility of inventory information across the supply chain.

Track and improve KPIs across the supply chain, such as - inventory turnover, days held, fill rate, order accuracy, wastage, and inventory turnover ratios.

Implement algorithms for predictive analytics and automated replenishments. 

Monitor, measure, and improve IoT-enabled predictive maintenance programs to decrease downtime and improve productivity.

User maintenance and service request data to reduce field service costs, improve product design, and risk assessment.

Monitor assets to improve availability and reliability.

Monitor quality assurance processes and track quality metrics across manufacturing processes.

Identify process gaps, deviations and predict the impact on quality using data science algorithms to reduce defect rates.

User ML and AI techniques to predict defect rates and identify potential root causes across manufacturing processes from sourcing to dispatch

Improve throughput and yield across processes and assets by tuning manufacturing parameters.

Capture data using integration and IoT enablement to gain insights into manufacturing bottlenecks and identify areas of improvement.

Institutionalize manufacturing KPIs by creating dashboards, reports, and alerts.