5 Industry Applications of Machine Learning

5 Industry Applications of Machine Learning

The mentions of machine learning are everywhere. Whether it’s voice assistants or self-driving cars, ML is becoming everyone’s favorite no matter what the industry – Health care, finance, education, automobiles, technology, and many others

There are good chances you are using ML on a daily basis without even realizing it. For instance, when you are using Google maps to find your way or when you’re socializing on Facebook. Even if you conduct a Google search on the keyword “cybersecurity consulting” and navigate through different blogs, the process is powered by Machine Learning. 

What is Machine Learning?

Not sure what machine learning means? Let’s have a brief introduction to this term. It’s basically an umbrella term used for a set of tools and techniques that allow computers to learn and adapt. The ML algorithm is used in conjunction with Artificial Intelligence. In fact, it helps AI to learn how to perform an action without writing a program. However, sample inputs are required for ML’s algorithm to predict and perform tasks. This technology has proven to be a savior in several fields.

The Use of ML in the Industrial Sector

Many companies are using their ML algorithms along with other IT tools for preventing fraud and improving Performance. In The Industrial setting, this technology is allowing us to identify the heart of the matter in production operations. It uses data and teaches the computer what to expect from production machines.

It relies on pattern recognition and then develops algorithms for making decisions and predictions without having to write a code. ML collects training data, processes it, and evaluates it in the form of sequence steps.

Let’s have a look at the industrial applications of ML:

1: Predictive Maintenance 

Imagine if you could predict faults or disruptions in the production line in advance? This would save time and keep the operations running. Using ML, managers can predict when machines require maintenance. They can schedule the downtime at an advantageous time and eliminate operational delays. 

Sudden downtimes can hit the profit margins heavily. You may even end up losing your customer base. In severe cases, it can disrupt the supply chain. 

Using machine language to apply predictive maintenance measures, you can boost your profit margins big time. 38 percent of manufacturers are already enjoying this benefit. 

2: Cost Reduction 

Using AL and machine learning algorithms, many manufacturing units have been able to generate sales by developing innovative products tailored to customer specifications and delivering them in a short lead-time. It can bring flexibility in the entire production process and bring cost reduction benefits for organizations in the manufacturing industry.

3: Network Security

Machine Learning is likely to change the standard operating producers in the manufacturing industry. This is particularly true in the case of organizational makeup. By co-locating a computer network with operational sensors on the machinery, ML can collect data and set it to the data warehouse as training data.

However, it requires a reliable network because if the network gets hacked or is inflicted by a DDoS attack, this would stop the entire production. 

4: Digital Win Development

The ultimate objective of using AI and ML together is to enable digital twin development. For this, an MBSE would be required. This digital twin serves as a platform to run multiple what-if scenarios and learn new things. It can also play the role of an end-to-end model for designing advanced reliability parts and adjust the interactions between machines for improving the production performance. The possibilities are never-ending.

Other Benefits

Machine Learning has thousands of other impacts on industrial processes and manufacturing. Some other applications of ML in industrial production include:

  • Optimizing the manufacturing process by using root cause analysis for reducing the testing code
  • Measuring the availability, performance, and quality of equipment by using improved neural networks for identifying weaknesses in the machines and minimizing the impact on the production process.
  • Building a connected supply chain. 
  • Improving inventory management procedures


Machine Learning is all set to introduce a wave of digital disruption and enterprises are preparing for it as well. The companies that were early adopters are enjoying its benefits. It is only a matter of time that we will see new applications of ML in the manufacturing industry.

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