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Labor and capital equipment are usually the two most expensive manufacturing operating costs. And in terms of expenditures, capital equipment can often run into the millions. Machinery is considered a long-term investment that will remain in service for years or even decades.
With so much on the line, it would make sense that every manufacturer would drill down as deeply as possible to ensure their machine assets remain in perfect working order throughout the equipment’s lifecycle.
But despite time-honored methods and strategies like preventive maintenance, many manufacturers fall short in monitoring and maintaining their equipment to the highest standard. Some may not initially put the investment dollars required into long-term maintenance. Others fall victim to manual data collection, preventive maintenance programs that rely on averages and result in over or under-maintained equipment, or uncertainty over how to take their asset management to the next level.
You can’t maintain what you can’t see. When manufacturers fail to automate their data collection at the machine level, they miss crucial data that could reveal trends and actionable insights. These insights include:
Capturing machine data by cause of downtime allows manufacturers to identify the root cause of the problem and reduce downtime or prevent it from happening in the future. Without precise data, they may guess why a machine was down, incur machine damage or high-off-quality material, or miss the event altogether.
Monitoring energy usage lets manufacturers identify improvements, reduce their carbon footprint, and save on energy costs. Older equipment may be able to be retrofitted with new electronics or wiring to minimize energy consumption, something that is missed without monitoring machine data.
Quality problems are often the result of equipment problems. Making quality control data by cause available to maintenance teams helps them identify equipment issues that can directly cause quality fallout before they become more significant problems.
Preventive maintenance was traditionally an improvement over “run to failure” methods. But every manufacturer’s products differ, and machines may need more or less than the recommended preventive guide dictates. Predictive maintenance allows a proactive approach using machine data to deliver the required service at the right time and address problems before they occur.
Failing to capture and digitize machine data negatively impacts its health. If manufacturers aren’t collecting data on machine downtime and categorizing it by cause, they may not discover the cause soon enough or at all.
Similarly, if they aren’t analyzing quality data to identify machine issues, off-quality production may increase.
Machine data is at the heart of the digital revolution in manufacturing. Companies that automatically capture data and automate their processes build a robust digital foundation that drives their internal supply chain.
Capturing and digitizing machine data from planning through execution is essential for greater operational efficiency and overall equipment effectiveness (OEE). Real-time data is critical to making informed decisions and driving better machine health.
Asset Performance Management (APM) software automatically collects and analyzes data to uncover real-time trends, performance issues, and other actionable insights. Since it captures a much more comprehensive range and volume of data, manufacturers gain complete visibility of machine health over its entire lifespan. This visibility enables them to maintain equipment at peak performance long past when other companies’ equipment would fail.
The advantages of using APM software include:
Your machines are one of the most valuable investments you have. With APM software, you can maximize the ROI with digital, automated data collection and analytics.
Contact us to find out how APM software can help you.