Believe it or not, manufacturing operations worldwide typically maintain their equipment in one of two ways. The longstanding default maintenance strategy, preventive maintenance. Or a much newer, technologically advanced option in predictive maintenance.
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.
The maintenance philosophy that rivals preventive maintenance is known as predictive maintenance. Rather than offering blanket maintenance throughout the year on each piece of equipment, this approach relies on integrated systems that collect performance data from each piece of equipment to determine the most optimal maintenance schedule. While this approach is certainly more effective in regards to an organization’s maintenance resources, it has its flaws. Namely the costs associated with implementing these systems into equipment.
As with anything that has such valuable capabilities, the cost is often what keeps organizations from defaulting to predictive maintenance strategies. With preventive maintenance, they’re able to save valuable capital. The organizations that have made the switch continue to contribute to the proficiency of these systems. As more technologies are added to the Internet to Things, the more accurate the interpretation and analysis of the data can become. With more data comes improved predictability, meaning machine failure would inevitability decrease. If an organization is prioritizing efficiency and limited equipment downtime, investment into these systems can be essential.
A misunderstanding that many owners and managers have come to realize, however, is that predictive maintenance systems are not meant to save a struggling business. Not only are the barriers to entry higher than most organizations can handle, they also require a great deal of investment in the technology platforms that employees must use in order to utilize the systems. Following said investment, organizations have to be prepared to train their employees to master these systems which often requires them completely rethinking what they previously knew about equipment maintenance. There are bound to be significant challenges even after making the initial investment into the systems. If your organization is capable of investing the capital required and feels comfortable facing these challenges, predictive maintenance might be an excellent fit.
If you’re still pondering about the differences between these two maintenance approaches, you can continue your search in the infographic paired alongside this post. Courtesy of Industrial Service Solutions.
