How Best Practices in Data Analytics Drive Maintenance Maturity

Insights that convert into actions can improve equipment reliability, span the enterprise, and boost the bottom line.  

TAKE AWAYS:
● Using data and analytics for condition monitoring can eliminate unplanned downtime and allow for improved equipment reliability.
● Centralizing maintenance roles and utilizing remote support can help manufacturers alleviate labor scarcity and maximize their technical teams.
● Machine health monitoring can trigger corrective actions to be scaled across multiple production lines or multiple sites. 

Manufacturers are acutely aware of how machine health affects production throughput, particularly plants operating in a throughput-constrained mode. Without sustainable equipment uptime, schedules are missed, orders go unfulfilled, revenue is lost, and unplanned labor and repair costs are incurred.

A significant factor impeding the achievement of operational goals is the widespread, protracted shortage of asset reliability and maintenance talent. Fortunately, technology can alleviate this challenge.

The burgeoning depth and breadth of condition monitoring analytics technologies offered by countless solution providers aims to eliminate unplanned downtime. The core value of machine condition monitoring is twofold: (1) drive best practices in reliability and maintenance; and (2) mitigate the skills gap now and into the future. Its primary goal is harvesting analytical insights directly from machines to identify the opportune time to service degrading critical equipment and components — not too early, nor too late.

Mastering how the condition data is harnessed, analyzed, and operationalized is key. With digitalization boosted by the industrial IoT, such as wireless condition monitoring sensors, plants can collect and centralize for analysis unprecedented quantities of real-time, streaming asset condition and performance data, along with batches of intermittently connected or locally captured data. Gaining maximum value from this approach requires putting the data and analytics to good use.