Manufacturers struggle with production delays caused by machine downtime. It’s an issue that constantly plagues operations, leading to frustrated workers and a negative impact on the business’s bottom line.

What many don't know is this: The issue can be effectively addressed with the help of artificial intelligence, which connects documents, files, videos and images across company data sources to give a holistic view of the manufacturing process — down to specific parts. As a result, organizations can detect patterns and be proactive when it comes to machine maintenance, limiting equipment downtime and saving costs.

Predictive maintenance uses data-driven techniques to help determine the condition of machines and equipment. These techniques help pinpoint when failures might occur or when maintenance should be done. The goal is to identify issues before they happen.

Machine checks are typically done on a schedule, whether daily, weekly, monthly or yearly. Problems occur when something goes wrong with a piece of machinery in between those inspections.

The answer lies in sensors that are capable of monitoring conditions automatically and continuously. They alert the proper department whenever there’s a deviation from the standard. When the temperature of a machine goes out of tolerance, for example, maintenance can address the issue immediately, rather than discovering the problem on a routine check, after it’s become a more serious issue.

Sensors collect loads of data about specific machines, but additional data exists within corporate documents such as inventory lists, factory blueprints and instruction manuals. These files aren’t likely to be sitting in the same data source; they tend to be scattered among multiple applications, drives, archives and the cloud. By connecting all of the data generated by machine sensors, and with the help of AI, maintenance teams gain access to the full scope of information at their fingertips.

For example, a repair team might be notified that the temperature of a tank has exceeded the optimal level. Workers responding to the alert can access every piece of relevant information through a single search across the enterprise’s systems. AI crawls information from every data source, and eliminates the need for repair teams to perform specialized fixes from memory or with inadequate intelligence. They get a full view of the picture in milliseconds, and the problem can be solved in a matter of minutes. That’s far preferable to a component within the tank becoming unusable, delaying operations for weeks.

Machine operations can be further improved through use of a digital twin, a virtual representation of a real-world system. The digital twin is enriched with real-time data, giving maintenance teams the capacity to interact with machines in a way that won’t sacrifice the machine itself. The team can obtain a 3D view of the tank they’re working on, and even see inside to identify any potential issues. Equipped with real-time data, they can gather insights on the best way to open the tank and address deviations in temperature.

The availability of data from sensors, corporate documents and digital twins keeps maintenance teams efficient and effective. Smart maintenance can be the answer to keeping the production process running at an optimal level.

Daniel Fallmann is founder and chief executive officer of Mindbreeze.