Industry 4.0 & artificial intelligence: 8 manufacturing applications for AI

If you made a list of the most overused buzzwords in manufacturing today, artificial intelligence (AI), machine learning (ML), and Industry 4.0 (i4.0) would be right at the top. Runners-up would include the Industrial Internet of Things (IIoT), smart factories, and cyber-physical systems, with an honorable mention for blockchain. It’s unfortunate, because these are Runners-up would include the Industrial Internet of Things (IIoT), smart factories, and cyber-physical systems, with an honorable mention for blockchain. It’s unfortunate, because these are more than just buzzwords, and understanding the concepts behind them is crucial to staying competitive in modern manufacturing process optimization.

What do these buzzwords mean?

You’ve probably heard some of these terms before and know that they are important when it comes to modern manufacturing. But do you know what they actually mean? Let’s take a closer look at some of these buzzwords and how they relate to manufacturing data systems:

  • Artificial intelligence is not about robots and androids. Artificial intelligence is not about robots and androids. AI refers to human-like reasoning in computational systems. These systems are intelligent and they are able to solve problems much like a human brain would, but at a considerably greater speed and with a far higher level of accuracy. For example, using real time manufacturing analytics software, AI can classify items based on a certain set of features in a dataset.

  • Machine learning is a type of AI, as it uses algorithms or statistical models to perform a task without explicit instructions. For example, machine learning can be used to differentiate between faulty and flawless parts coming off an assembly line, without being supervised (unsupervised machine learning).

  • Unsupervised machine learning is an algorithm that can group data points and features without instructions on what to look for. The model can map the underlying structure of a dataset, even without a specific target to predict. For example, using anomaly detection software, it can identify anomalies in production data that result from subtle changes conventional quality processes may miss, such as tool wear.

  • Industry 4.0 is also known as the 4th Industrial Revolution. It is driven by data and advanced technologies, including cyber-physical systems, virtual and augmented reality, the cloud, the Industrial Internet of Things, artificial intelligence, and machine learning. Innovations in industry 4.0 are helping modern manufacturers implement sophisticated automation solutions and smart machines to improve production quality and throughput in their industry 4.0 advanced manufacturing facilities.

  • Industrial Internet of Things is the combination of sensors, gauges, instruments and other data collection devices on a production line, connected by an ML/AI-enabled network. It is the driving force behind today’s smart factories. This connectivity facilitates the collection, storage and analysis of massive amounts of data which, using manufacturing analytics software, can be used to identify problems and opportunities on an i4.0 production line.

  • Big data refers to the massive volumes of data, usually associated with manufacturing, that is difficult to store and handle using traditional manufacturing data systems. Machine learning has the power to analyse the data and make predictions for the future, based on applied input and past experience. Data analytics in manufacturing using an ML/AI solution, can deliver insights into a plant’s big data, helping engineers and quality teams make better decisions.

  • Blockchain is a powerful technology that is being leveraged by manufacturers to securely analyze their big data in order to get better insights into their supply chains and track assets with greater precision. This enables them to transform and streamline their data processing operations.

According to a recent Vantage Market Research report, global AI in manufacturing is expected to grow by a CAGR of 51.5% over the next six years, reaching a market value of US$17.9 billion by 2028. A Forbes survey on AI found that 44% of respondents from the automotive manufacturing industry classified AI as “highly important” to production, while 49% said it was “absolutely critical to success.” And yet, more than half of the respondents (56%) from the automotive industry said they plan to increase AI spending by less than 10%.

What is going on?

Not long ago, the manufacturing sector was swept up in the excitement of AI. Then came the predictable sceptical backlash of comparisons of artificial intelligence to snake oil. As a result, manufacturers may comparisons of artificial intelligence to snake oil. As a result, manufacturers may have experienced a sort of conceptual whiplash. However, along came Covid-19, with all of its associated disruptive forces, and suddenly AI was being seen as a cure-all.

Is AI a manufacturing cure-all or a quack remedy?

It should come as no surprise that we see it as neither. Artificial intelligence is a tool, and like any tool its usefulness depends on the context in which it’s being applied. Here is a list of eight applications for AI that can benefit today’s manufacturers.

8 manufacturing applications for AI

1. Predictive Analytics

LinePulse Manufacturing Application

We might as well start with what we know best. The basic idea is to leverage the data generated before, during, and after the production process to derive insights into product quality or predictions about future product failures. This is most definitely a job for AI, as the sheer volume of manufacturing data being generated makes it impossible for human minds to grasp all the various and sundry relationships between signals.

Our clients have used predictive analytics for manufacturing to identify faulty transmissionspredict gearbox failures, and detect anomalies in engine misfires. All of these cases involve models based on machine learning — a subset of artificial intelligence — and in each one, the ML/AI models were able to deliver highly accurate results even with minimal training data. This capacity for generalization is a hallmark of AI.

2. Predictive maintenance and quality control analytics

Scheduled Maintenance System

Although predictive data analytics in manufacturing and predictive maintenance and quality control analytics are often lumped into the same category, there are important differences between them. The premise of predictive maintenance is to use data from the production line to anticipate when manufacturing equipment is likely to fail, and then intervene to repair or replace the equipment before that happens. Although it’s not a perfect analogy, one could think of the relationship between predictive maintenance and predictive data analytics as akin to the one between quality assurance and quality control analytics: the former focuses on process, the latter on product.

Nevertheless, as with predictive data analytics, predictive maintenance and quality control analytics depend on being able to synthesize insights from massive data sets, often with minimal training data. Examples of predictive maintenance and quality control analytics using AI solutions for manufacturing include machine tool builders forecasting machine spindle issues before they happen, and General Motors using image classification to identify robotic arm failures.

3. Industrial robotics and AI solutions for manufacturing

Robots and AI go together like apple pie and ice cream, peanut butter and chocolate, or Wookies and Ewoks: good on their own, but amazing in combination. Although they’ve already been in use for more than half a century, industrial robots have been changing their image in recent decades, from coldly competing against human workers, supplanting them with ruthless efficiency, to friendly helpers that can make line workers’ lives easier rather than stealing their livelihoods. At the center of this shift are collaborative robots, or cobots, which are designed specifically to work with humans.

For example, AI-enabled cobots in the automotive industry can do repetitive, heavy-lifting jobs, like positioning and fastening engine hoods to the body assembly, giving line workers more time to concentrate on jobs that cobots can’t do. Adding AI to cobotsAdding AI to cobots enables manufacturers to deploy them faster, monitor production line workspaces for changing conditions and adapt to them.

Regarding industrial robots more generally, AI can improve robot accuracy and reliability as well as enable more advanced forms of mobility. Perhaps most significantly of all, AI can play a key role in reducing the programming and engineering effort required to create and implement industrial automation.

4. Computer vision applications for AI/ML in manufacturing

Closely tied to industrial robotics, computer vision applications for AI in the industrial space most often involve visual inspections. Computer vision, aided by AI in automotive manufacturing, has two obvious advantages over humans when it comes to visual inspection, namely speed and accuracy. A computer vision system using cameras that are more sensitive than the naked eye and augmented with AI can identify microscopic defects that human inspectors might miss, at a rate they cannot hope to match.

For example, Audi used an AI vision system to identify cracks in the sheet metal from its press shop.Audi used an AI vision system to identify cracks in the sheet metal from its press shop. Because this solution was based on deep learning — a subtype of machine learning often applied to large, unstructured data sets, such as images — Audi’s engineers spent months training their artificial neural network using several million test images. That initial effort paid for itself however, since the system was able to learn independently from the examples and can now detect cracks in entirely novel images.

5. Inventory management using AI solutions for manufacturing


Inventory management may not be the most exciting application for AI/ML in manufacturing, but it is a valuable one. According to at least one estimate, inventory amounts to $1.1 trillion in capital. That’s an enormous amount of value that could be unlocked with better inventory management, and artificial intelligence is the key to that. There are myriad ways that AI manufacturing solutions can reduce the costs of maintaining inventory, from optimizing what’s kept on-hand to anticipating gaps before they happen.

Once again, it’s the ability to take in staggering amounts of data and find the patterns hidden within, that make AI solutions for manufacturing such a natural fit. Although it’s not a manufacturer, Amazon is perhaps the largest and best-known example of applying AI to inventory management.

6. Implementing AI solutions for sustainable manufacturing

There are AI solutions for manufacturing that can create more efficient systems to help reduce energy use on the production line. Most sustainability initiatives necessitate the overhauling of the production line, investing in green energy sources, or implementing more efficient technologies – AI manufacturing solutions can deliver production improvements without changes to the manufacturing process.

Computing power and algorithms are becoming more readily available, and data processing and storage costs are dropping, making AI-enabled solutions more common in manufacturing. AI manufacturing solutions are delivering tangible results, such as designing and implementing optimum operating parameters that will reduce energy consumption without adversely affecting production throughput.

For example, Google’s AI-enabled NEST thermostat can efficiently control the heating and cooling of homes and businesses to conserve energy. AI solutions for manufacturing can scale this technology to cover the entire shop floor of large factories, helping manufacturers become more energy efficient.

7. AI solutions for manufacturing to reduce waste

Even if the best practices in manufacturing are followed, human error will always be a factor in the manufacturing process. A defect, or anomaly, on the production line could be missed by the line worker, which could lead to a defective product passing through. Undetected, these minor anomalies can snowball into major faults and wasted materials – impacting negatively on the cost of production for the manufacturer.

AI enabled quality control programs for manufacturing using anomaly detection software can help manufacturers reduce waste, improve product quality, and increase throughput. Most innovative manufacturing companies use ML/AI-enabled anomaly detection solutions to identify manufacturing defects, instead of traditional image-processing methods, as they can pinpoint defects by quickly leveraging large manufacturing datasets.

For example, in the automotive industry, quality control programs for manufacturing that include anomaly detection software can be used to identify faulty door panels and generate an alert when the panel on the production line does not meet predetermined specifications. This enables the engineer and/or line worker to address the problem, thus preventing subsequent door panels from ending up as waste.

8. Supply chain management using ML/AI solutions

An ML/AI-enabled supply chain management solution can help manufacturers improve their supply chain and logistics operations. Manufacturers are also able to save money through reduced operational redundancies and risk mitigation, and improved supply and demand forecasting, while enhancing their business planning and forecasting capabilities.

A supply chain management solution that incorporates ML/AI can collect and analyze a great deal more inputs and signals than a human is able to process, to deliver accurate and timely decisions faster. The ML/AI-enabled solution is able to adapt to changing conditions in near-real-time and improve its knowledge by processing more data and exposing hidden anomalies in the supply chain better than any human can.

The future of AI solutions for manufacturing

For most innovative manufacturing companies, the number of applications for ML/AI will no doubt continue to increase, as computational resources become less costly and domain knowledge proliferates. In any case, one thing is certain, it is an exciting time to be working at the intersection of ML/AI and the automotive industry.

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