Lakhasly

Digital quality system:

The DQS employs sensors, software, and data analytics for real-time monitoring and management of product quality during production.If issues are detected, maintenance notifications will trigger timely inspections and repairs, minimizing unplanned downtime and optimizing production efficiency with scheduled maintenance.Notifications of anomalies are sent to operators, enabling them to take corrective actions

Predictive Maintenance:

Predictive Maintenance is a proactive strategy involving data analytics, IoT sensors, and machine learning in predicting the failure of equipment to create time for maintenance.

Digital quality system:

The DQS employs sensors, software, and data analytics for real-time monitoring and management of product quality during production. It allows manufacturers to gather and analyze data at every manufacturing stage, ensuring consistent quality standards. For instance, in car manufacturing, IoT sensors track temperature and component alignment, while AI analytics detect potential defects. Notifications of anomalies are sent to operators, enabling them to take corrective actions

Predictive Maintenance:

Predictive Maintenance is a proactive strategy involving data analytics, IoT sensors, and machine learning in predicting the failure of equipment to create time for maintenance. It minimizes downtimes, reduces maintenance costs, and prolongs the life of equipment. Predictive maintenance in an automotive manufacturing plant involves the use of IoT sensors on a robotic arm, monitoring factors such as temperature and vibration. It uses machine learning algorithms to interpret the data for patterns indicative of impending equipment failures. If issues are detected, maintenance notifications will trigger timely inspections and repairs, minimizing unplanned downtime and optimizing production efficiency with scheduled maintenance. This minimizes costs attributed to undue repairs and minimizes disruptions [13].