Machine breakdowns are abrupt and can be operationally catastrophic, involving time-consuming tasks such as identifying the malfunctioning machine, pinpointing the broken part, checking inventory logs for spare parts, and waiting for shipping.

Embracing predictive maintenance enables leveraging machine data to anticipate which machine, and even what machine part, requires repair before it disrupts operations

In the long-term perspective of your operation, responding to machine failures has a more significant impact on ROI compared to predicting and planning for them. The immediate consequences of a critical asset failure, whether it halts a production line or hinders team productivity, immediately impacts operational ROI

Unexpected failures incur additional costs, such as emergency specialist visits, overnight shipping for spare parts, and other unforeseen expenses. Implementing predictive strategies helps mitigate these challenges, contributing to improved long-term operational efficiency and ROI

Transition from the limitations of managing and scheduling work orders in a singular Excel sheet. Embrace a data-driven operation that moves beyond isolated systems, fostering a process centered on improving communication and scheduling efficiency

Sensors measuring vibration, thermal, and ultrasonic pulses contribute valuable data that all give input on a comprehensive picture of machine health

By leveraging this sensor data, we establish a baseline for machine health. Comparing it to common issues in similar machines enables us to predict potential failures and identify specific parts that might cause issues. This proactive approach minimizes surprises in maintenance planning, allowing for more informed and effective strategies