AssetsMaestro automates work orders and workflows, schedules labor, and manages materials to enhance operational efficiency. By employing predictive, preventative, and corrective maintenance strategies through automation, IoT data, a computer vision model, and digital twin technology, AssetsMaestro optimizes resource utilization using deep learning for both mass and ad hoc scheduling. Additionally, it improves safety stock management with a reliable forecasting model based on parts velocity.
Maintaining assets and managing schedules is streamlined with AssetsMaestro, which leverages automated workflows and advanced deep learning technologies to create work orders and allocate resources for preventative and predictive maintenance. The platform intelligently recommends scheduling and rescheduling based on various dependencies to enhance resource allocation. Utilizing digital twin technology, it ensures precision in ordering and issuing the necessary parts and tools for work orders, while its template-based maintenance approach allows service providers to manage multiple conditional maintenance plans for each asset effectively.
