As part of an ongoing series of Industry 4.0 articles to help manufacturers embrace the fourth Industrial Revolution, we will be going through some concepts, theories, and practical examples. Some of the articles include those about Industry 4.0 success stories, Equipment-as-a-Service, Industrial Internet of Things, Big Data, and more. To keep up with the series, sign up for our newsletter.
Every company in every industry purports to use Machine Learning (ML) in their solutions. As an Industry 4.0 technology, Machine Learning has become one of the most important methods that technologies use to prop up their new applications.
What is Machine Learning?
A quick recap: from our Industry 4.0 glossary, Machine Learning is:
A method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
What this means is that instead of machines having to be programmed specifically for that command or logic, machines can now be programmed once to learn from data, which will help them to spot trends automatically. An interesting example would be the Vestri robot we highlighted last year, which was created and programmed by researchers from the University of Berkeley, California, to learn about its environment through ‘motor babbling’.
Industry 4.0 and Machine Learning
How does Machine Learning factor into Industry 4.0, alongside cyber-physical systems, Industry Internet of Things (IIoT), Big Data, and Augmented and Virtual Reality (AR and VR)?
Machine learning is an integral part of Industry 4.0. In order for Internet of Things (IoT) applications to work, oftentimes machine learning is required. From learning how to identify insights, to being able to predictively monitor equipment, machine learning uses Big Data to implement solutions for practical problems using hyper-connectivity.
Machine learning is applied in a multitude of different industries; from automotive to the semiconductor industry, Artificial Intelligence (AI) technology such as Machine Learning is highly-sought after.
How does Machine Learning Benefit Manufacturers?
The first step in improving OEE (Overall Equipment Effectiveness) is to ensure that we have all the data we need. This is where intelligent monitoring and pattern recognition come in.
Machine learning has been used, especially for Industry 4.0 adopters, in the design of new machines. Oftentimes, it has been combined with IoT devices and applied extensively on the shop floor itself. An article from Forbes discusses how machine learning can lead to a 30% increase in semiconductor manufacturing yields, because the data gleamed from the connected machines on the factory floor can be the key to increasing efficiency and reducing operational downtime and scrap rates.
After that, the machines can be programmed or redesigned to use that historical data in a more effective way. For example, smart applications made with machine learning capabilities could help in pattern recognition, and then predict upcoming maintenance needs or equipment failure possibilities. That allows manufacturers to be able to better manage their resources, especially if maintenance is a big part of their business.
Research from McKinsey actually predicted in 2018 that supply chain forecasting errors would be reduced by 50% and that would lead to a 65% reduction in lost sales, just purely through manufacturers having more insight into their machines. Supply and demand planning will become much accurate, since it’s based on concrete data gleamed from Industry 4.0 technologies such as machine learning.
Manufacturers would be able to program machines to learn from their environment; if there’s a change in the parts produced, it wouldn’t then require a total replacement, as the machine would more easily be able to learn a new process.
Another example would be in the sense of autonomous vehicles; if vehicles can use deep learning to recognise threats in their surroundings, they would be able to change course on the fly instead of sticking only to their programmed routes.If this isn’t enough to convince you to embrace machine learning, IDC estimates “machine learning and AI spending to increase from $19.1 billion in 2018 to $52.2 billion by 2021.” That’s a lot of manufacturers who are investing in machine learning.