Examples of IIoT In The Workplace

By | Blog

The Industrial Internet of Things (IIoT) is influencing the manufacturing industry in a big way. In short, the IIoT makes use of networks, like the internet, to aggregate data from connected devices. The data is centralized on a platform where it can be processed and translated into meaningful insights. There are many examples of IIoT being used in the workplace. The platforms and services available to manufacturers can help with everything from machine performance monitoring to predictive maintenance. Many of these systems also incorporate machine learning and other subsets of artificial intelligence to optimize performance and more accurately make predictions.

In this post, we will look at some of the best examples of how IIoT improves the way a factory operates.

Real-Time Performance

One of the best uses for IIoT in a manufacturing plant is for the real-time performance tracking capabilities. When a factory connects their machines to an IIoT platform, the machines will start reporting data for how they are operating. The platform will provide a view that shows every input and corresponding output for all of the machines that are in operation.

This functionality offers huge benefits for a company. With real-time performance tracking, a plant manager or employee can monitor the entire factory and get immediate notifications when a machine or group of machines are not performing at their expected or optimal level. This will ultimately lead to quicker fixes, increased efficiency, and higher quality products.

Factory Integration

Another one of the examples of IIoT in the workplace revolves around the way in which the factory’s machines are enabled with the tracking and communication features. In most cases, the machines already have ports where external devices can be plugged in.

Oden Technologies offers simple data collection device that can be plugged into any machine or PLC and can communicate wirelessly to their platform. This simple integration is what makes some services stand out from others. Factory integration is big part of IIoT but it should be a short process and shouldn’t require factories to purchase new machines and equipment.

Predictive Maintenance

The last example of IIoT in manufacturing is in a system’s ability to make predictions on when maintenance should take place for a machine. This is part of the IIoT incorporates machine learning algorithms. These algorithms look through large amounts of data to recognize patterns, which help the AI form models that represent the correlation between inputs and outputs. Over time, the machine learning algorithms will have enough ‘training’ to be able to make predictions about what may happen based on given data.

In the example of predictive maintenance, the machine learning algorithms will be able to take the performance data from the machines and figure out the likelihood that this machine will need maintenance and by what time. The more data that a factory has at its disposal, the more accurate these predictions will be. This is an approach that leads to cost savings over time and higher productivity.

The Smart Factory

All of these examples of IIoT in the manufacturing industry come together to form what is referred to as a “Smart Factory.” A smart factory has a sort of sensory system that allows it to monitor itself and communicate this information through a unified network. When paired with AI and machine learning, a smart factory can start to make predictions that lead to smarter business decisions for a manufacturer.

The IIoT brings tremendous value and is very easy to take advantage of. It is as simple as getting in contact with the right IIoT platform and integrating this technology into your business. It is important to start collecting this data today so that you can benefit from now and in the future.

Using Big Data in Manufacturing

By | Blog

During the 90s and early 2000s, the big trend in many industries was transferring company operations onto digital mediums, such as computers and wireless networks. This provided managers with a better understanding of their organization and the ability to stay informed. Information was distributed more effectively in an electronic format, and basic information like current sales, general production speed, etc. was more readily available.

With these tools, managers had the necessary information to make informed and sound decisions. The ways in which big data is currently being leveraged is very similar. What makes big data (and data analytics) different, however, is that it is much more powerful. If used properly, big data can help solve real-time problems, swiftly detect bottlenecks, make extremely accurate projections, and much more.

Big data can be likened to the “next wave” of innovation, and it is clearly changing the way that people are making decisions in the manufacturing industry, among others. Although this sounds exciting, it’s definitely not the whole story. There’s plenty of confusion around the way in which big data can be applied to a factory’s operations. Our goal with this article is to clear the air a bit and talk about the details. By the end of this article, you’ll have a better understanding of exactly what big data enables you to do in your factory.

Leveraging Big Data for Dynamic Bottleneck Detection

Data gathered on the actual performance of a machine (speed, length, unit variation, etc.) is not just limited to calculating defect rates. In fact, this data can be leveraged to determine bottlenecks in the entire factory. In some cases, a large majority of defects may come from a single machine, recipe, or small group of machines that are underperforming. By leveraging big data properly, these underperforming machines or processes can be found quickly and accurately, which saves money in the short and long-run.

Prior to the adoption of big data and analytics, it was difficult for manufacturers to detect bottlenecks and get accurate data to confirm where they were occurring. Manufacturers had to manually check the performance of each machine and spot check any issues. This process would take hours or even days. Big data and analytics platforms can now shorten this process to mere minutes. Manufacturers can react faster and have more ability to prevent future problems from occurring.

Unit Consistency Using Big Data

Consistency is an absolute necessity in any manufacturing plant. Over time, it’s inevitable that machines will warp and produce units with decreasingly accurate dimensions. In fact, this is a common problem that manufacturers deal with. Big data helps combat this problem via IoT devices that gather comprehensive data points on the machine’s status, productivity, and more. By having access to all of this information, manufacturers can make predictive decisions to avoid product defects rather than waiting until they become an actual problem.

Having a solution like this will also help prevent other complicated problems from occurring, such as customer dissatisfaction, faulty units, and machine downtime. Big data is invaluable even for quality issues that cannot be avoided preemptively. Having large amounts of machine data allows managers and engineers to quickly detect and make decisions based on productivity trends. For example, if a unit’s dimension variation falls out of spec, the manufacturer can be prompted to re-calibrate the necessary machine. This can be done by operators and engineers with ease.

Big Data to Avoid Organizational Siloing

Using the right data analytics tool gives plant managers a better understanding of the overall productivity of a given factory. For example, Oden’s innovative analytics platform gives managers, engineers, and operators the ability to limit or distribute analytics information to their team members accordingly. Everyone from the assistant manager to the CEO can have access to information pertinent to their job and can make decisions based on that information. Rather than having to rely on reports performed by fellow employees, an organization can now distribute that information in real-time, anytime, and to anybody who needs it. This reduced siloing makes for a more dynamic factory that can react to and prevent problems quickly.

Big data is a powerful tool that manufacturers can use to make decisions. The insights that come from data lead to decisions that maximize an organization’s productivity and minimize its inadequacies. As data analytics tools become finer tuned, and IoT devices are integrated into more factories, big data will subsequently become even more powerful.

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