The document discusses a predictive maintenance solution for heavy machinery that uses machine learning and artificial intelligence. It ingests operational data from cranes, builds ARIMA models to forecast faults across components, and generates proactive alerts to stakeholders to enable timely maintenance actions. This reduces downtime delays and improves utilization by 7%. The solution analyzes historical fault data, predicts imminent issues, and provides alerts by component and machinery to facilitate effective maintenance scheduling and reduce downtime.