Predictive maintenance strategies offer manufacturers an opportunity to use deep data analysis to reduce downtime, improve productivity and drive efficiency gains. Such a strategy can take the principles of preventive maintenance and add a layer of precision that would be unattainable without modern data analytics solutions. This is all made possible by the internet of things, but the IoT is not the catalyst. Enterprise resource planning systems take that IoT data and make it actionable for predictive maintenance programs.
How Predictive Tactics Fit into the Maintenance Pipeline
Most manufacturers have traditionally used a blend of preventive and reactive maintenance strategies. In a preventive strategy, an organization establishes benchmarks for when parts and equipment need work and schedules maintenance activities accordingly. For example, one machine may require a part replacement once every 500 production cycles, another part replacement on an annual basis and a full maintenance check every 2,000 production runs. A maintenance management system would take all of these dates and organize them into a coherent schedule. The preventive strategy is limited in that it works off of best practice estimates. You could replace a part periodically to prevent downtime, but end up spending heavily on replacement components because you aren't maximizing the life of a part. The goal is to avoid component or equipment failure, ensuring the cost savings of avoiding downtime overcome the expenses of replacing parts that still have some life in them.
A reactive maintenance program, on the other hand, is built around responding to a maintenance event - such as equipment performing in a sub-optimal state or failing outright - as efficiently as possible. Blending preventive and reactive tactics is critical in reducing downtime and responding to emergencies in a cost-effective way. A predictive maintenance program fits into this broader ideal, but brings in deeper data analysis to make preventive maintenance more efficient and reduce the need for reactive efforts.
How Predictive Maintenance for Manufacturers Programs Work
Imagine that conventional wisdom indicates a machine component will require maintenance every 1,000 times it is used. The problem is that your maintenance teams often check on the equipment accordingly only to find it doesn't need much work. Historically, businesses have struggled to adapt to these types of observations because they lack the concrete data to adjust beyond the best practice. Predictive maintenance changes that.
This change begins by implementing IoT devices to monitor equipment disposition. Whether the sensors are performing vibration analysis, tracking temperatures or simply performing a count of production runs, the clear data is invaluable. This information can be logged in an ERP system, providing near real-time tracking on a piece of equipment. Because of this, you can more precisely predict when a component may fail. Instead of having to use a general figure, such as 1,000 production cycles, you can gather and use historic data to determine signs of failure and predict when maintenance will be needed.
For example, consider a machine part that depends on special bearings to keep vibrations under control. When those components are in good working order, the equipment will vibrate at a certain rate per second during normal use. That rate will change as the bearings wear down, and a certain amount of vibration will eventually lead to inefficient machine performance and create a risk of breakdown. With historic data, you can accurately predict when that breakdown is likely to occur in your specific environment. From there, the IoT devices and ERP systems using raw data to create vibration analysis reports can track real-world component performance so your maintenance team knows exactly when maintenance needs to be performed.
In simplest terms, predictive maintenance blends historic data patterns with real-world performance information to precisely estimate when risk of failure occurs and anticipate the best time for maintenance accordingly. Data-based predictive maintenance strategies let you more accurately assess when maintenance is needed, helping you maximize the life of your equipment without increasing the risk of downtime. While IoT devices and the data they generate are vital for predictive maintenance, ERP solutions capable of applying that information across lines of business are instrumental to creating value opportunities.
Putting Predictive Maintenance Into Practice With an ERP
A predictive maintenance program often looks like a wide array of interconnected parts operating together in seamless cohesion. It is like a machine unto itself, and a business's ERP platform is the brain for the machine. At the base, you have IoT devices gathering information. Network infrastructure then transmits that data to back end servers where, in most cases, a computerized maintenance management system tracks the data. Analytics programs can use that information to perform predictive monitoring - tracking historic data patterns from monitoring devices to anticipate when maintenance is needed - and report back to the computerized maintenance management software to schedule maintenance accordingly.
Maintenance software on its own is useful for predictive maintenance, but organizations will get more value if that maintenance system can operate with tight integration with an ERP system. An ERP platform designed for deep integration can track data across maintenance, inventory, purchasing and vendor management systems, ensuring cohesion.
For example, imagine your predictive maintenance tracking efforts indicate that a part will need to be replaced in a week. With a tightly integrated system, your maintenance platform would create a work order, triggering an inventory check to verify the proper supplies are in stock. If that inventory report indicates that available supply will decline below a certain threshold, the purchasing software can set up an order for the new supply while the vendor management solution identifies the best vendor for that order based on cost and shipping timelines. What's more, sales and customer management systems within the ERP software can assess how maintenance will impact production and create alerts so your teams are aware of any disruption.
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The interconnectedness created by the ERP platform makes it easier to act on predictive maintenance workflows by ensuring every part of the business is equipped to respond to the new maintenance reality. This is particularly vital for custom manufacturers, as they depend even more on production optimization to create strong customer experiences. The abas ERP platform empowers organizations to get more value from predictive maintenance through deep integration and powerful workflow optimization tools.
Contact abas ERP today to learn how.