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How Manufacturers Can Use AI/ML to Improve Operations and Predict the Future

The Internet of Things (IoT) has exploded, creating unprecedented demand for IoT-enabled devices, services, and solutions. The Internet of Things (IoT) has exploded, creating unprecedented demand for IoT-enabled devices, services, and solutions – with global spending expected to exceed one trillion U.S. dollars by 2022.

Manufacturers are becoming increasingly connected in an effort to streamline processes, increase productivity, stay competitive and prepare for the future. As more assets become connected as part of the Industrial Internet of Things (IIoT), the data they generate continues to grow exponentially. Locked within this data is business intelligence that, if read, applied, and delivered in a way that is easy to understand, can provide actionable insights to improve operations and deliver long-term business value. But this data is complex in nature, and given the sheer volume of data generated, is not easily gathered and analyzed. It is a challenge traditional manufacturing systems are not designed for – and manufacturers are missing out on valuable insights as a result.

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Manufacturers seeking to gain and maintain a competitive edge should take a comprehensive approach to machine learning and analytics, integrating equipment, systems and people into a highly collaborative environment that rapidly adapts to changing operational requirements and operates on a scale much larger than simple IoT applications. With the right strategy, industrial manufacturers can capitalize on the opportunity to generate business insights from data, capturing sustainable economic value.

Predictive maintenance is a transformative application with the IIoT with enormous advantages. Here are three benefits that can serve as differentiators for industrial manufacturers.

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Optimize Business Processes

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Predictive analytics automatically deliver proactive information to decision-makers to improve quality and performance of complex manufacturing processes and transition reactive maintenance processes into predictive maintenance before failure occurs. Using ML technologies, personnel can evaluate hundreds or thousands of individual production runs based on product, operator and environmental conditions to identify the optimal process capable of producing the maximum throughput. Operations managers can use information, based on current conditions, to discover new insight, optimize business processes and equipment performance, and maximize yield. Additionally, operators can implement AI and model predictive control techniques to automatically set up the correct machine parameters. This allows operators to keep the manufacturing line running optimally because they are able to concentrate their efforts on more pressing demands. 

Predict Unwanted Events Such as Downtime or Required Maintenance 

Losing the function of critical assets can be devastating, causing unplanned downtime that can cost billions of dollars per year. Predictive maintenance enables technicians to detect issues in advance and resolve problems before equipment failure occurs. Real-time monitoring helps to minimize and avoid downtime or failures by enabling detailed monitoring of equipment conditions and operating parameters such as vibrations, temperature and sound. Anomalies automatically trigger alerts and proactively initiate responses from maintenance teams or service networks as soon as a problem occurs. When machine conditions exceed machine-learned thresholds, plant personnel are automatically notified through email/SMS. This allows manufacturers to react quickly to otherwise unknown events thus improving overall operations.

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Improve Worker Safety

Maintenance work can be dangerous, especially when assets fail in unexpected or catastrophic ways. Manufacturing is one of the highest risk industrial sectors to work in with over 300 major injuries occurring each year. Manufacturers can leverage predictive maintenance to reduce or even eliminate unexpected failures by using the data from AI/ML technologies to determine expected life spans for all facility assets and proactively schedule maintenance around this information. This helps industrial manufacturers predict when a malfunction may occur so they can complete maintenance work before a machine becomes dangerous, reducing the risk of severe injury to workers.  

In summary, manufacturers that leverage connected analytics enterprise-wide can employ predictive maintenance into their digital transformation strategies can proactively identify potential issues, reduce the occurrence and length of unplanned downtime, minimize risk, and get the most value from assets and budgets.

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