There is room for improvement in every industry, in practically every department – whether it’s automating certain tasks to save time, leveraging Machine Learning and Big Data to fine-tune production, or optimizing supply chain processes with superior forecasting, digitalization is a great way to reduce waste to an absolute minimum.
This is especially the case with Predictive Maintenance, where reducing downtime and ensuring continuous equipment availability and peak performance are an absolute priority. A study from 2014 attributed 25% of overall manufacturing operating costs to maintenance-related issues. Furthermore, the study estimates that 30% of maintenance costs are related to unnecessary expenditures caused by bad planning, overtime, overstocking situations, etc…
In order to optimize maintenance processes, technologies such as Machine Learning and advanced data analytics can be used to predict maintenance needs before production is disrupted.
In this blog, we’ll take a look at examples of Predictive Maintenance in four different industries, which may give manufacturers an idea of what they can expect to implement in the near future.
Potential problems: Flight delays or cancellations due to mechanical problems or failures that cannot be repaired in time. Aircraft component failures that disrupt scheduling and operations.
Solution: Predictive maintenance tools notify users if a mechanical failure is about to occur ahead of time. Gathering intelligence on component reliability reduces maintenance and repair costs substantially, minimizing downtime.
Potential problems: ATM machines break down from time to time and require multiple interventions and component replacements on a yearly level. Paper jams, defunct security mechanisms, damage caused by thieves and other issues are not uncommon.
Solution: Predictive maintenance systems let users know how many hours a component has left in its lifespan. Additionally, rather than allowing an ATM to fail midway through a transaction, the software can deny customers service, which is a somewhat more acceptable alternative.
Potential problems: Wind turbines are highly expensive to fix and involve high capital costs. If the generator motor breaks down, the turbine becomes ineffective.
Solution: Predictive maintenance tools can gather information about the turbine’s KPIs, such as time to failure. Notifications about failure probabilities can inform technicians when to act and schedule time-based maintenance regimes.
Transportation and logistics
Potential problems: Wheel failures in the global rail industry account for 50% of all train derailments and can cost manufacturers billions of dollars.
Solution: Railways can use predictive maintenance to monitor the performance and condition of wheels to ensure minimum downtime and let technicians know it’s time for a preventive replacement. A just-in-time approach to component replacements save time and resources.
Predictive Maintenance is just getting started
There are numerous ways Predictive Maintenance can enhance your operations. As explained by one of our blogs:
AI, machine learning and advanced analytics can be combined into a powerful tool for predictive maintenance. Analyzing components’ attributes and production parameters during different timeframes, the system is able to gain insights about when a critical failure might occur. Predictive maintenance enables manufacturers to save huge amounts of resources by reducing downtime to a minimum.
Check out this page to take the first step towards implementing an intelligent Predictive Maintenance system and future-proofing your manufacturing operations.