Predictive and Prescriptive Maintenance
A preventative maintenance strategy is the most commonly used strategy in the food and beverage industry. It relies on a maintenance schedule to clean, repair, or replace the equipment and its components to prevent them from breaking down unexpectedly and causing unscheduled downtime. The problem is that things break down due to several factors that might not be considered in the preventative maintenance strategy. Other strategies can be implemented that expand on the preventative maintenance strategy.
Predictive maintenance uses a condition-monitoring approach to equipment maintenance. By adding monitors, thermometers, and sensors to equipment, you can identify when a part will fail before it does and replace or repair it during planned downtime. The concept here is to monitor the performance of the equipment to detect potential breakdowns before they happen, reducing the maintenance cost. Predictive maintenance works hand-in-hand with a preventative maintenance plan and shouldn’t replace it.
The food and beverage industry has traditionally adopted a predictive maintenance strategy slower than other industries. Cost is the most significant factor, but that’s starting to change. The monitors, probes, and other hardware costs are decreasing, making them less cost-prohibitive. However, due to regulatory standards, hygienic standards, and safety considerations, many organizations still feel that their current strategy is working, so they don’t want to make the change. That philosophy is changing now that more food and beverage companies are adopting the predictive maintenance strategy. While it can be complex to implement, there is an increase in ROI and a significant decrease in maintenance costs with a predictive maintenance strategy.
Implementing a predictive maintenance strategy requires a good computerized maintenance management system (CMMS) to track all the equipment and the condition-monitoring resources and to process the data provided. By tracking the utilization of your equipment and monitoring the data from the sensors, the CMMS software can automatically send an alert or generate a maintenance order if it finds that an asset is not running within its normal conditions. This can save countless hours of unexpected repairs and reduce the risk of contamination in a food and beverage production environment.
Prescriptive maintenance takes predictive maintenance one step further. Prescriptive maintenance makes predictions about upcoming maintenance requirements by taking the data collected from the condition-monitoring devices and running them through advanced analytics.
It will even tell you why something is happening so that you can plan to avoid the problem in the future. In addition, prescriptive maintenance will look at different maintenance options and determine all possible outcomes to make a better recommendation to reduce risk and limit downtime. Concepts are even more critical in the food and beverage industry.
Prescriptive maintenance requires implementing machine learning capabilities to analyze the data coming out of the monitoring devices. It is costly and not usually immediately effective since it needs time to learn. In many cases, system failures help teach the machine learning platform. Due to this, prescriptive maintenance is probably not a good fit in many food and beverage environments with regulatory standards. Prescriptive maintenance is being used today, but it is still in its early stages and probably isn’t cost-effective for most organizations, especially those in the food and beverage industry.
The most important thing to do is determine which maintenance strategies work best for your organization. In the food and beverage industry, waiting for equipment to break down is not an option. Avoiding unplanned downtime and meeting all regulatory requirements is critical to the industry. We all need to be proactive in our approach to equipment maintenance; keeping the downtime limited and ensuring safety is the goal. And it doesn’t hurt the bottom line either.