This content is part of the Essential Guide: IIoT use cases put spotlight on IoT benefits, challenges

smart manufacturing (SM)

Contributor(s): Ed Burns

Smart manufacturing (SM) is a technology-driven approach that utilizes Internet-connected machinery to monitor the production process. The goal of SM is to identify opportunities for automating operations and use data analytics to improve manufacturing performance.

SM is a specific application of the Industrial Internet of Things (IIoT). Deployments involve embedding sensors in manufacturing machines to collect data on their operational status and performance. In the past, that information typically was kept in local databases on individual devices and used only to assess the cause of equipment failures after they occurred. Now, by analyzing the data streaming off of an entire factory's worth of machines, or evven across multiple facilities, manufacturing engineers and data analysts can look for signs that particular parts may fail, enabling preventive maintenance to avoid unplanned downtime on devices. Manufacturers can also analyze trends in the data to try to spot steps in their processes where production slows down or is inefficient in their use of materials. In addition, data scientists and other analysts can use the data to run simulations of different processes in an effort to identify the most efficient way of doing things.

As smart manufacturing becomes more common and more machines become networked through the Internet of Things, they will be better able to communicate with each other, potentially supporting greater levels of automation. For example, SM systems might be able to automatically order more raw materials as they get low on supplies, allocate other equipment to production jobs as needed to complete orders and prepare distribution networks once orders are completed.

A lack of standards and interoperability are the biggest challenges holding back greater adoption of smart manufacturing. Technical standards for sensor data have yet to be broadly adopted, which inhibits different kinds of machines from sharing data and communicating with each other effectively. In the United States, the National Institute of Standards and Technology (NIST) is investigating opportunities to develop and promote standards with various industry stakeholders, including technology companies and manufacturers. The process is ongoing. Other challenges include the cost of implementing sensors broadly and the complexity of developing predictive models.

This was last updated in March 2016

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