We're facing supply chain issues across various sectors. A key reason behind the supply problems is production downtime. It's estimated that factories may lose up to 20% of their productivity due to downtime.

The concept of predictive maintenance dates back to the 1990s. The unavailability of sensors and the lack of computational resources made its implementation challenging at the time. The introduction of the Internet of Things (IoT), machine learning, cloud computing, and big data analysis has made predictive maintenance mainstream. Particularly, IoT is crucial for predictive maintenance. It can convert the physical actions of machines into digital signals, such as vibration, temperature, and conductivity, for processing and analysis.

As research data shows, the financial impact of unplanned downtime is severe. The report finds that large factories lose 323 production hours annually due to unplanned downtime. The average cost of income loss, restarting production lines, financial penalties, and idle employee time reaches $523,000 per hour. The traditional method of using tools, materials, checklists, and clipboards for equipment maintenance is inefficient. In the IoT era, you can focus on solving problems when they occur, rather than searching for symptoms.

Predictive maintenance based on the Internet of Things provides the data needed to understand equipment and environmental conditions, enabling informed investigations by the appropriate personnel. It saves time that would otherwise be spent on manual equipment checks, record-keeping, and managing solutions.

Therefore, when the storage unit's temperature is too high, it can be adjusted remotely. If the device's vibration and heating indicate a problem, you can use data to determine whether immediate maintenance or a later time is required. By eliminating the hassle of detecting device issues, IoT monitoring saves time for more meaningful, non-automated tasks.