Four Key Technologies Driving Data Analytics in Industrial IoT

The Internet of Things has become a game-changer, especially at the industrial level. Industrial IoT analytics can allow executives to learn which aspects cause bottlenecks, quality control issues or accidents. Conversely, they can pinpoint which improvements would most likely boost productivity or decrease equipment downtime. However, making the most of the IoT in an industrial setting requires people to know about and deploy the essential technologies that unlock the capabilities of data analytics tools in a busy environment.

Cloud Computing

When leaders begin focusing on industrial IoT analytics, they must determine how to store and access all the data they already have or will collect soon. Fortunately, cloud computing is ideal for meeting those needs and others that may arise once parties start analyzing data in earnest.

A typical manufacturing plant could have hundreds or thousands of connected assets, each containing data a decision-maker could use to better understand what is happening at any given time. Consider the example of a huge consumer packaged goods company, where executives wanted to increase IoT utilization associated with several of its global brands. This rollout involved connecting a staggering 2.8 million IoT devices to a centralized, cloud-based platform.

Besides offering excellent scalability for massive projects like this one, cloud computing supports distributed workforces and sites, allowing people to connect data-collecting devices in multiple locations. As a case in point, this company’s IIoT efforts involved internet-linked products in 97 countries. Additionally, one of the cloud tools the company’s executives chose can handle billions of IoT devices and does not require people to engage in infrastructure management.

Another benefit of including cloud computing in your industrial IoT data analytics plans is that authorized parties can log in from anywhere with internet access and pull the latest statistics from their tablets, smartphones or computers. That access-anywhere capability supports collaboration between experts located in many places, which can improve product design or processes. In one case, people relied on data analytics to make concrete that was approximately 30% stronger than its counterparts, showing how a collaborative, purposeful approach can pay off.

Equipment Sensors

Although industrial leaders must consider the individual needs of their facilities before implementing the IoT, many understandably look at what others have already achieved to get inspiration. Many then realize that attaching connected sensors to critical equipment makes good business sense. Doing that lets them get alerts of problems that may degrade quality control or cause preventable asset downtime.

In one example, executives from a conveyor belt company deployed IIoT sensors and a complementing platform that allowed customers to engage in continuous monitoring. That decision arose once the leaders realized that conveyor belts were most prone to wear-and-tear, and that misalignment or belt damage can be significantly disruptive to customers who depend on this material-movement equipment in their critical operations.

One application involved applying equipment sensors to oversee the belt blades for conveyors used in the mining industry. The hardware gathers real-time data and compares it with historical performance information, allowing the system to flag abnormalities. Users can then rely on that data to make more appropriate decisions that keep production running smoothly.

The ongoing data stream also supports planning by helping managers identify the best times to take machines offline for essential maintenance. The alternative is that machines may break down unexpectedly, resulting in lost time and increased costs.

Moreover, monitoring specific characteristics enables people to create a baseline to establish overall equipment health and typical performance. One common option is to conduct vibration tests as part of a predictive maintenance strategy. Connected sensors can analyze the intensity or frequency of specific vibrations to identify potential abnormalities. However, environmental factors can affect how much an object vibrates. Fortunately, sensors can reveal contributing characteristics, such as a facility’s humidity and temperature, making it easier to assess the extent of unusual vibration patterns.

Artificial Intelligence

Artificial intelligence has undoubtedly taken industrial IoT analytics to the next level. That improvement has primarily occurred because AI can detect patterns in vast amounts of data, letting people draw conclusions much faster than they could without technology’s assistance.

Customer order forms, equipment statistics, social media comments and computer vision images could all hold clues about how a manufacturing site could enhance quality control measures while increasing overall output and optimizing processes.

However, trying to make sense of all that data manually would likely prove too time and labor-intensive to be worthwhile. AI algorithms make data processing happen more efficiently, which is ideal for organizations with large and ever-growing information repositories.

Many AI applications complement other technologies. For example, it is increasingly common for people to use artificial intelligence-powered equipment sensors. Such hardware may also use edge computing infrastructure to substantially shorten the transfer distance when moving the data to the cloud for processing. Some compatible edge devices even have on-device processing, strengthening sensitive data security.

Chatbots

Some people have also investigated how generative AI could complement these use cases. This is a type of artificial intelligence that spans beyond more traditional cases and allows people to interact with tools while using natural language, similar to talking to a friend or colleague. Many of the most popular commercial generative AI tools are chatbots.

In one example, an individual applied information from a customer’s corrective action request about welding rods found without the necessary material lot numbers. They asked a chatbot to provide five questions the organization could use to determine the root cause of this issue.

The tool’s response featured questions formatted to follow the well-known “five whys” technique, which assists with effective problem-solving. It involves asking several questions that get progressively closer to the heart of the matter. For example, the chatbot’s first suggested question asked why the welding rods lacked the required number. However, its fifth and last one was why the company did not follow a process for ensuring the appropriate parties added the numerical identifiers.

Since the company almost certainly had an established system that broke down at some point to cause this outcome, the chatbot encouraged people to examine what went wrong and why. Decision-makers could gather data linked to this AI-assisted process to track trends and ensure the missing numbers were outliers, and not signs of a previously unrecognized larger issue.

Additionally, some vendors are developing generative AI products that can answer questions based on companies’ internal data, providing analytical benefits. For example, a user might ask, “How many of our printed circuit boards failed quality control checks in the past six months?” That is an emerging example of AI data analysis outside conventional methods.

Industrial IoT Analytics Need Supporting Technologies

These examples highlight why people will get the best results with their industrial IoT analytics efforts by choosing complementary technologies to meet their needs. Those above will encourage executives to consider the possibilities and get excited about how IoT investments could connect to overarching organizational goals.