Predictive maintenance involves using data to anticipate issues that may arise – and allows businesses to address them before they cause further problems for the organization. The predictive maintenance process allows management to identify the problems in operations or systems and prevent breakdowns in services.
This process utilizes automated machine learning and AI to analyze actual measured usage and equipment feedback to predict when specific machines may break down. These valuable insights allow organizations to save on maintenance costs and minimize the risk of downtime by implementing regular tune-up schedules.
Predictive maintenance uses the Internet of Things (IoT) sensors that consistently collect a variety of data from equipment and machines. The data that is collected ranges from temperature to specific vibrations and oil levels – virtually anything that tells you exactly how that machine is running.
When this information is processed by the machine learning model, you can gain invaluable insights as to how equipment is performing and gauge the wear and tear that is happening. This gives you a head start on a potential issue so you can fix it before it causes operational delays.
Incorporating predictive maintenance processes within your organization enables you to optimize service schedules for your equipment to make sure that your critical assets remain operational as long as possible. This data helps your company be proactive instead of reactive to issues with machinery, preventing unplanned downtime or unnecessary inspections.
The goal of predictive maintenance is to accurately predict when equipment might fail so that you can perform maintenance or order parts before it actually breaks down. This can translate into significant cost savings, as a study conducted by McKinsey found that these tools can increase machine life by 20% to 40% while reducing downtime by up to 50%.
Not only does predictive maintenance save your business money, but it also improves operations by ensuring supply chains remain intact and increasing safety.
Predictive maintenance requires a significant amount of data to be analyzed and collected to be effective. At LogicPlum, we use some of the most sophisticated machine learning models in the industry to ensure that you can get the most out of the data you are working with.
Our data tools allow you to transform your business into a data-driven organization and puts you in complete control of the important you discover.