Despite the goal of all maintenance efforts being the same, many manufacturing organizations will reach these goals in different ways. This is largely related to the fact that each of these organizations’ needs are going to be different. It’s imperative for each organization to develop and tweak their maintenance strategy in a way that grants them the greatest advantage. Typically, this maintenance effort for organizations will either come from a preventive or predictive strategy. Throughout this post, these two strategies will be broken down in their entirety.

Beginning with the former, preventive maintenance is a very traditional approach for organizations around the world. This strategy includes performing maintenance to all required machinery or equipment in an organization’s fleet at regularly scheduled intervals throughout the calendar year. Now, it’s critical to understand that each piece of machinery will likely receive maintenance at different times of the year. The reason behind this is that each of these machines differ in characteristics like age, average run time and even how much available down time is permitted for a particular component of an organization’s fleet. As such, this approach can create a challenge of keeping up with a messy maintenance schedule throughout the year but will always keep an organization’s equipment in solid shape.

Predictive maintenance, on the other hand, is a newer strategy that has begun creeping its way into the manufacturing industry. This strategy is much more dynamic than its preventive counterpart in that it uses data collected directly from an organization’s equipment to determine the optimal maintenance schedule. So, rather than having set intervals for maintenance on all pieces of equipment throughout the year, this approach determines when maintenance is truly necessary. While this strategy is much more efficient in regards to deploying maintenance resources, it is far more expensive to implement than a preventive maintenance strategy.

One thing to note about predictive maintenance, however, is the cost. Much more expensive but is becoming simpler to implement as the years go on. As more and more technologies find their way into the Internet of Things ecosystem, the possibilities continue to expand. The moment these predictive maintenance systems are installed, they’re able to capture and record performance and external data of an organization’s fleet. This data can then be analyzed to provide insight into when a particular machine may require maintenance in addition to revealing the problem area that requires maintenance. Which in turn leads to greater efficiency as a result of less downtime required for critical machines.

However, as with anything that seems to be a sure thing, simply integrating these predictive maintenance systems into your organization is not a foolproof way to improve your organization. In fact, integrating these systems mean even more change for your employees to keep up with. Re-training long-lasting personnel with a completely different outlook on maintenance is bound to be a challenge. Not only this, new employees with next to no knowledge regarding these systems wouldn’t be able to latch on to an existing employee to learn the ropes while these systems are so fresh. If your organization has the available capital and the utmost trust in your employees, with enough dedication these systems can prove beneficial.

If this post hasn’t made it clear, it’s no easy task to develop a maintenance strategy that is perfect. The truth is, businesses will only ever get close to perfect, there will always be challenges within the maintenance process. For more information on how to avoid these challenges, take a minute to check out the infographic coupled alongside this post. Infographic courtesy of Industrial Service Solutions.