Predictive maintenance is a valuable investment — how do you time it right?

More and more companies understand that to get the most out of their equipment and machinery and extend its useful life, maintenance has to happen before the wear caused by sustained work sets in. 

Between corrective and predictive maintenance, the first acts by fixing faults when they occur and the second analyzes equipment working conditions and mechanical and operational performance, and acts on a plan, based on analysis of current data and each machine's history. 

The advantages of predictive maintenance are several: it prevents equipment depreciation, extends useful life, allows maintenance to be planned so that rotation guarantees full equipment availability, and avoids unnecessary teardowns and stoppages in the workflow caused by unforeseen breakdowns. 

Preventing failures also minimizes the damage an incident on one machine can cause to another, allowing targeted maintenance based on knowing which operational element is most exposed to wear or where the performance irregularity sits. With predictive maintenance, fewer components are replaced and the work can be done at times of lower demand. That way, maintenance does not interrupt the flow of operations. 

The data analysis and tracking that equipment undergoes under predictive maintenance is more rigorous, so maintenance managers gain a better understanding of its condition and performance, which also protects processes and operational quality. Together, these benefits lower costs — tied, among other factors, to longer equipment life and better use of operating time — and therefore improve productivity.

How do you invest in preventive maintenance?

The answer is to digitize the information coming from the machinery or equipment and use technology to capture and monitor it, so you know for certain the equipment's current condition and performance and can predict its needs based on the work you are doing. 

Field operations management technologies, like the ones we design at GoDoWorks, connect maintenance managers with the equipment in their charge, making it easier to gather information. That connection — mobile, real-time and handling very large volumes of data (Big Data) — is built around operational needs and therefore takes in the work associated with maintenance and the mechanical and performance condition of each machine. 

Corrective maintenance run at the right moment by management software means that, through notifications, forms, databases or data monitoring, the maintenance manager stays aware of each machine's history and its maintenance needs. The software capturing and holding that information helps by producing forecasts, through its artificial intelligence, that complete the maintenance manager's technical work with valuable information and reports on data and performance indicators. 

It becomes possible to trace the work each machine is put through by operators, to make specific technical knowledge more available to those operators, and above all to generate the timely notifications that allow preventive maintenance to be scheduled and performance to be monitored constantly, in real time. 

That monitoring detects anomalous performance and behavior in equipment, which safeguards the correct use of valuable resources such as fuel and the safety of technicians, operators and equipment. Having that data also makes it possible to measure the impact maintenance is having on equipment and operations, and so improve decisions about maintenance itself or about other operational changes. 

In short, running scheduled maintenance in time to prevent wear and failures is an investment that improves how equipment works and creates operational flexibility and coordination that raise productivity and lower costs. To get the timing right on this, you need digital tools like the ones we design at GoDoWorks, which not only enrich and help coordinate the work of maintenance managers but become important assets for understanding and improving how the work our equipment carries out performs.