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Executive Brief: 4 Ways Predictive Maintenance Streamlines Manufacturing

New Internet of Things solutions are helping manufacturers improve product quality, increase productivity, decrease costs, and make smarter business decisions.

The Internet of Things (IoT) is ushering in a new Industrial Revolution that will transform how products are made. Already, fabrication facilities are becoming “smart factories,” where vast quantities of sensor data are continuously analyzed to increase productivity and efficiency.

According to Markets and Markets, manufacturers will spend $74.8 billion per year on smart factory technology by 2022. And McKinsey & Company estimates that by 2025, the total economic impact of smart factories could reach $3.7 trillion per year.

One of the first ways that smart factories are benefiting from the IoT is through predictive maintenance. These solutions collect equipment data and analyze that information with sophisticated algorithms. Using historical performance as a guide, the software forecasts how monitored equipment and the production line as a whole will behave.

For example, IoT sensors might monitor the temperature of a key piece of equipment. If the temperature begins to rise, a predictive maintenance solution can take actions to avoid equipment or product damage and notify staff of the problem.

The challenge is figuring out how to implement this technology. Because every factory is different, predictive maintenance must be customized for each facility. This involves myriad decisions about everything from how data should be gathered to where data should be analyzed — in the cloud or at the edge of the network. Making these choices can be difficult because IoT solutions require expertise in both information technology (IT) and shopfloor operational technology (OT) — and these two disciplines historically have had little in common.

To overcome these challenges, Intel, IBM, ADLINK, and PrismTech, an ADLINK company, combined their expertise in number crunching, big data, and industrial automation.