Why Does AI Have An Important Role In IoT-Driven Manufacturing?

IoT adoption is surging in manufacturing. As Industry 4.0 picks up, more manufacturers are implementing IoT connectivity into their workflows, but not all these projects reach their full potential. That’s partly because organizations miss the importance of AI in smart manufacturing.

IoT and AI projects are more closely related than many professionals may realize. Recognizing how the former helps get more value from the latter is key to capitalizing on both technologies. Here’s a closer look at the many roles of AI in IoT-driven manufacturing.

Turning IoT Data Into Insight

Across all use cases, AI in smart manufacturing makes it easier to act on IoT data. IT/OT convergence provides vast amounts of data, but this information is only useful if businesses put it to work. As a result, over half of organizations’ data is dark: generated and stored but not used.

AI can analyze the data IoT devices produce to turn information into insight. That may be as simple as interpreting equipment information to summarize a machine’s health status. In other cases, it could be as complex as comparing data across the supply chain to recommend when to order more of a part.

Human analysts can interpret IoT data this way, but it’s a time-consuming and error-prone process. AI can do it faster and more accurately.

Enabling Predictive Maintenance

Predictive maintenance is one of the most widespread specific instances of this analysis. These tech-centric repair strategies often come up in discussions about IoT and AI projects because they rely on both technologies.

It starts with IoT sensors collecting real-time data on equipment health indicators like temperatures or vibrations. This information alone can enable condition-based maintenance, but it takes AI to push it further. Machine learning models must analyze this data to discover trends that inform predictions about when machines will need repair in the future.

Bringing these technologies together produces remarkable results. Manufacturers can experience up to 20% increased equipment uptime and 10% lower maintenance costs. Sectors with particularly costly or common downtime events, like automotive manufacturing, will find these improvements particularly valuable.

Creating Digital Twins

Combining the IoT and AI in smart manufacturing also lets manufacturers create digital twins. These are virtual models of real-world objects or systems. Unlike conventional digital models, they can update in real-time as their authentic counterparts evolve, thanks to IoT data.

In this use case, as with predictive maintenance, IoT devices provide the data that AI then acts on. Smart sensors throughout a production line let it create a highly detailed and up-to-date digital twin of a workflow. Alternatively, manufacturers could use AI to form digital twins of specific products based on IoT-derived information.

Whatever the specific application, these simulations have impressive benefits. Some manufacturers use them to test new products with fewer resources and in less time. Others model entire facilities to highlight inefficiencies to optimize ongoing operations.

Optimizing Supply Chains

IoT and AI projects can take on a larger scope than just a single manufacturing facility. Organizations can also use them to optimize their larger supply chains. IoT tracking is already common in logistics, but combining this real-time information with AI unlocks further benefits.

Over time, IoT shipment-tracking information could reveal larger trends in supply chain efficiency. Machine learning models can analyze this information to uncover patterns and suggest possible solutions. Similarly, AI could interpret real-time data from suppliers to warn manufacturers of potential incoming disruptions.

Supply chain complications can be highly disruptive, but every manufacturer must grapple with them. The only way to effectively manage these challenges is to respond quickly to changing events, which requires in-depth analysis of real-time information. That’s precisely what AI and the IoT provide.

Making Cobots More Effective

The intersection between AI and the IoT also creates the ideal collaborative robot (cobot) environment. Cobots offer the efficiency of automation with the flexibility of human-centric workflows, as they work alongside employees instead of replacing them. However, this is only practical with AI-driven IoT analytics.

Cobots must be able to respond to real-time data to adapt to changing circumstances or avoid running into human co-workers. IoT sensors can provide situational awareness, and AI algorithms can react to this information to direct cobots. Network technologies like 5G are important here. 5G is up to 100 times faster than 4G LTE, enabling faster responses.

This connectivity could let cobots adjust their workflows in response to disruptions earlier in the production line to minimize time or material losses. Alternatively, it could foster more precise navigation throughout the facility, helping cobots avoid obstacles and employees. As a result, these machines become safer and more efficient, leading to quicker ROIs.

Securing IoT Data

All these other IoT and AI projects increase manufacturers’ reliance on digital data. Consequently, they make cybersecurity a more prominent concern. AI can help in this area by enabling faster and more accurate network monitoring.

IoT security is challenging for many organizations. As helpful as these devices are, they increase businesses’ attack surfaces. Many also feature minimal built-in protections, meaning hackers could use weak points to infiltrate a network before moving to more sensitive systems and data.

AI can help by monitoring IoT traffic in real-time. When something falls outside of ordinary behaviour, these algorithms can flag it as a potential breach and automatically isolate the compromised system to stop the damage. These defences save $1.76 million on average by enabling faster, more effective cyberattack responses.

Don’t Overlook the Value of AI in Smart Manufacturing

AI in smart manufacturing lets organizations respond to IoT data in new ways. In other cases, it enables faster and more accurate data-driven actions where manual alternatives may be slow and error-prone. These advantages apply to a vast amount of specific use cases, so manufacturers shouldn’t overlook this opportunity.

The IoT is an indispensable resource for modern manufacturers. However, it’s best when it doesn’t stand alone. Pairing this technology with the analytical powers of AI maximizes its benefits. IoT manufacturing projects will be safer, faster and more cost-effective as a result.