AI-Enabling Technologies In Manufacturing

John Clemons is a Solution Consultant for Rockwell Automation. He’s been working in the field of Manufacturing Technology for over 30 years.

Artificial intelligence (AI) is becoming more prevalent in manufacturing, producing bottom-line returns and helping reduce costs and increase capabilities. But AI can’t take all the credit. Other technologies are being used with AI, enabling it to succeed. Without these enabling technologies, AI’s success in manufacturing would be far more limited.

So far in this series, we’ve discussed AI’s impact on manufacturing, its real-world benefits and the most common AI tools. In this article, we’re going to highlight the technologies that are working together with AI, driving its success in manufacturing. It’s not necessary to have all these technologies to make progress with AI, but these technologies are enablers to AI and help companies get more out of their AI implementations.

Industrial Internet Of Things

People often view the industrial Internet of Things (IIoT) as being synonymous with smart manufacturing. It’s hard to find smart manufacturing solutions that don’t include the IIoT as a key component. The same is true for AI solutions.

One key application of AI in manufacturing is delivering accurate data, providing data context and making sure it’s consistent and relevant. But before AI can perform these tasks, the data must be collected. That’s where the IIoT comes in.

The IIoT forms the backbone for data collection in manufacturing. Most devices on the shop floor are smart devices. This includes smart sensors, smart equipment and any device with a local control system.

These networks of smart devices create large data streams and provide real-time information on shop floor equipment, environmental conditions, product quality and more. The IIoT communicates with these smart devices, collecting and storing the data. At that point, AI takes over and begins processing the data, cleansing it, verifying its accuracy, adding context and making sure that the data is consistent and relevant.

Cloud And Edge Computing

Cloud and edge computing provide the necessary infrastructure for the IIoT and for AI. Edge devices, located on premises close to the manufacturing equipment, support the IIoT in collecting and processing data close to the source.

Ultimately, the edge devices act as a bridge to the cloud, transmitting the data—or at least the most relevant data—collected by the IIoT.

Cloud computing provides AI with the scalable capabilities for data storage and data processing. Cloud infrastructure is a practical way for manufacturers to handle the large volumes of data generated in their operations.

Many AI applications also rely on the elastic compute capability available in the cloud to train models. The training requires access to vast amounts of data that can best be stored in the cloud. Once trained, those models are then deployed into edge environments to minimize latency and ensure connectivity and security.

Big Data Analytics

Data analytics and data management solutions have been around for a long time. Back in the first industrial revolution, people were already analyzing data from manufacturing operations. Today, with big data analytics, it’s all about tracking and analyzing big datasets that would be impossible to handle without significant computing power. Big data analytics, data management and AI have merged to the point where the analytics and data management must include AI tools. These tools with AI enable large-scale data processing, which allows manufacturing companies to analyze both real-time and historical data for information and insights.

Cybersecurity

AI tools are being coupled with cybersecurity to support manufacturing operations. AI tools process large amounts of sensitive manufacturing data on performance, equipment, recipes, specifications, quality, failure and so on. Cybersecurity with data analysis is essential. With or without AI tools, it is risky to manage all that sensitive data without cybersecurity in place.

But beyond processing all that data, people are now understanding the benefit of AI coupled with cybersecurity. AI-based threat detection is emerging as a powerful application of AI and cybersecurity. AI is better and faster at detecting data patterns that indicate the possibility of an intrusion, especially in the early stages of a cyberattack, before an intrusion occurs when the threat actors are probing for weaknesses and vulnerabilities. AI is especially good at detecting these threats well in advance of them being threats. Likewise, AI-based threat response joins the power of AI with cybersecurity to respond faster to threats once they’ve been detected.

IT/OT Systems

Manufacturing operations all use various information and operations technology. Some people might think that AI is at odds with the information technology (IT) and operational technology (OT) world, or vice versa. But nothing could be further from the truth. IT/OT systems in manufacturing are not going away in the foreseeable future. They will still be responsible for everything from closed-loop regulatory control at the process level to accounting and financial control at the business level—and all sorts of transactions and components in between.

The smart play is integrating IT/OT systems with AI tools to make them more powerful and provide them with greater data analysis capabilities and new and better capabilities than they have by themselves. Whether it’s HMI/SCADA, MES/MOM, ERP or anything else in the IT/OT world, adding AI tools simply makes them better. It could be anything from data collection, cleansing, or context to accuracy, consistency, relevance, big data analytics, cybersecurity, equipment optimization, production line efficiency, performance optimization, or a whole host of other applications. The list goes on and on. The bottom line is that IT and OT systems are getting better at using and integrating AI tools.

Conclusion

AI is all over the place in manufacturing, producing bottom-line results and helping reduce costs and increase capabilities. But AI doesn’t do these tasks by itself.

AI is being coupled with other manufacturing technologies, and it’s this coupling that enables AI to do what it’s doing in manufacturing. Whether it’s the IIoT, cloud and edge technologies, big data analytics, cybersecurity or IT/OT systems, they are all more powerful with AI. It’s this combination that’s driving real results in manufacturing.

Look at these enabling technologies and use them to supercharge your AI initiatives. The benefits are well worth it!

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