Equipment’s Productivity

Understanding The Impact Of Maintenance On Equipment’s Productivity

As many business owners and managers know, maintenance can be tricky. While it’s imperative to the success of your business, it can be a touchy subject for many organizations. Namely because each organizations’ needs are so unique, developing a maintenance strategy to meet these needs is a challenge. This is why most organizations settle for a preventive maintenance approach. Those that don’t have often gone the more dynamic route of predictive maintenance. This post should clear up any confusion regarding these two strategies, in addition to providing some additional context to how they benefit organizations. 

Preventive maintenance is the most typical of the two. While it might be the safest route, it’s also much less effective than its predictive counterpart. The standard way in which this strategy is executed is through routine maintenance on all equipment throughout regular intervals in the year. Meaning certain equipment may be under maintained throughout the year assuming as a result of this system. On the other hand, other equipment will be over maintained where it doesn’t need to be. Sure, this sort of blanket approach is safest on average, but it can be stressful keeping up with a maintenance schedule spread out so sporadically throughout the year. Not to mention, emergency maintenance is much more common. 

Where preventive maintenance fails is where predictive maintenance succeeds. The philosophy of this approach is to let the machinery do the talking. With integrated Internet of Things technologies, the equipment becomes the source of information regarding required maintenance. Through collection, analyzation and interoperation of output and external data sources, these technologies are able to most accurately indicate when equipment requires a tune up. These technologies are also capable of indicating imminent failure, meaning downtime can be decreased significantly. Much like anything with these benefits, however, comes a fairly steep cost. 

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While the cost stops a majority of organizations, those that can afford these systems have not only simplified the implementation of them, but also continue to contribute to their efficacy. As more and more Internet of Things technologies are connected to manufacturing equipment and machinery, the more the systems are able to learn as a result of contributing data. Meaning maintenance detection can continue to be improved, as can the analysis and reporting of the data these systems collect. As if they weren’t already the most effective method of reducing downtime and eliminating equipment failure, they will continue to improve. 

What some organizations fail to realize, however, is that predictive maintenance will not solve all of an organization’s problems. These systems can contribute to improving maintenance resource output, but they will not address administrational issues. Unfortunately, most organizations won’t ever be able to afford these systems. Those that can and choose to invest in them will also have to overcome the challenge of retraining their employees regarding the technological platforms that will have to be integrated into an organization’s operations. Meaning it won’t be smooth sailing in the beginning with some organizations facing serious challenges. Organizations capable of investing such capital with extreme trust and confidence in their employees will benefit the most from transitioning to a predictive maintenance strategy. 

Organizations searching for additional information regarding these two maintenance approaches should check out the resource accompanying this post. It can provide some valuable information regarding which strategy is right for their business. Courtesy of Industrial Service Solutions.

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