Using Artificial Intelligence in Predictive Analytics

Artificial intelligence is one of the technologies of the moment. As one of the things that permeate all the pillars of Industry 4.0, AI is part of several important routines in a production process.

As it is present in many activities, through the integration of systems and the internet of things, AI can get a lot of data at all times. From this data and the use of technology, information is transformed into knowledge.

And it is from this knowledge that the area of ​​predictive analytics is being noticed today. An industry that has a well-organized predictive analytics area and the right technologies, can achieve incredible results.

Therefore, in today’s article, we will show how managers can use artificial intelligence as one of the industry 4.0 strategies, especially in the area of ​​predictive analytics.

Thus, problems are avoided in machines, productivity, and product quality increases, generating very positive results from the beginning to the end of the production chain.

Understand the concepts of Artificial Intelligence and Predictive Analytics simply and clearly. Also, we show examples of everyday life and the industrial environment to better understand their applications. Finally, check out the benefits of using AI in predictive analytics.

The concept of Artificial Intelligence

AI is a technology that has enabled and still enables breakthroughs in industries and also as a development strategy in the area of ​​predictive analytics, for example.

This is because artificial intelligence is one of the pillars of Industry 4.0, along with several other Big Data, Cloud Computing, and Simulations.

However, it is not only in industry 4.0 that AI is used. In our daily lives, we have a very close relationship with this technology.

Because of the advanced software and algorithms, machines begin to have learning capabilities.

If you use Google for internet searches, you may have noticed that many times Google itself ends up completing the searches you are doing, right?

And it is with the same premise that Spotify, Netflix robots and so many other services like these work. They learn each time you push a button or make a decision within the app.

But how does it really happen?

This is because each information you provide to the system yourself is used in predictive analytics. With this, this machine can predict user behavior.

In the case of industries, this happens in predicting equipment problems, for example.

The concept of Predictive Analytics

A predictive analytics is a set of data, statistical algorithms, and machine learning techniques that identify the likelihood of future events.

Everything happens based on facts and data collected by machines through sensors and receivers positioned in various places or devices.

Predictive analytics along with artificial intelligence are used for various purposes. One is to predict the amount of raw material needed for next year. Or to know when a machine might stop due to mechanical problems or problems in spare parts.

Besides, because we are extremely connected to networks today, predictive analytics can also detect standard behaviors to prevent fraud in both corporate and personal networks.

Considering this, the same logic is used to set the scores of customers in banks, to provide credit for them. This is done from information obtained in the bank accounts such as late payments, credit card installments or problems to keep money in the account.

This way, users end up being jeopardized when they need a loan or financing if their scores are not good.

The history of predictive analytics

Predictive analytics is not big news, but industry managers have been paying more attention to it lately.

This is happening because this way there is a possibility of increasing profitability and getting ahead of competitors.

And the fame of predictive analytics has come because more and more:

  • the data obtained are more reliable;
  • the histories are more consistent;
  • technology is cheaper and easier to use;
  • and you can even do simulations before putting a new idea into practice.

Thus, an area that was once exclusive to professionals who understood mathematics, statistics, and technology is now much more accessible.

Benefits and applications of artificial intelligence and predictive analytics in industry

Predictive maintenance is one of the areas that most benefits from the combination of predictive analytics and artificial intelligence. In industry 4.0, these are very important strategies for positive results to be achieved.

One of the consequences of predictive maintenance in industries is the generation of value in each production process, with benefits such as:

  1. Reduction of corrective maintenance costs;
  2. Elimination of equipment disassembly steps to perform preventive inspections;
  3. Agility in the production process;
  4. Increased machine life;
  5. Damage reduction and resource loss due to production line failures.

Actually, predictive maintenance is a mix of more traditional approaches such as preventive maintenance or corrective maintenance. Ways of work have evolved and today it is possible to be much more strategic at all levels of a factory.

Preventive and corrective maintenance already helped to improve companies’ indicators. But it is with the ability to predict problems before they happen that predictive maintenance has been so well accepted in industries.

Industry Applications

One of the applications of maintenance and predictive analytics is the use of a speed changer.

The technology and artificial intelligence of this type of spare part gathers data of different behaviors that the engine has. When the speed changer identifies that working conditions have changed, an alert is sent to investigate the situation more closely.

These alerts can be set in several ways. It is important that they are very visual alerts and preferably sent on more than one platform, to more than one person.

They can be done in dashboards, indicator panels and also via email alert.

Thus, time and money are not wasted. And parts are only changed when it is really necessary for the equipment and the production line to be preserved.

A second application of AI in the industry involves using system integration to control a company’s profitability, for example.

With this, sensors and algorithms assist in data collection and decision making according to clear indicators and based on what was collected by AI at the factory itself.

As you might imagine, many industries use these technologies in their work routines. The Salt River Project is the second-largest utility in the United States, and one of Arizona’s largest water suppliers.

The company uses predictive analytics to predict when the generating turbines need maintenance so that they do not cause downtime in the process.

Another company that makes use of this combination of AI and predictive analytics is Lenovo, which uses them to better understand warranty claims. This has led to a 10-15% reduction in the company’s costs.

Conclusion

Finally, it must be remembered that the different pillars of industry 4.0 have many advantages and challenges in their applications.

After all, industries are sometimes quite old fashioned, which makes it difficult for the innovation work needed for industry 4.0 to happen.

Therefore, the use of artificial intelligence and predictive analytics is not yet ideal in Brazil, but the numbers are growing. And that way companies are making more money.

Are you interested in better understanding how the other pillars of industry 4.0 are happening? Keep reading our content here on the blog to better understand the opportunities and challenges with all of this. In addition to finding out how companies use them to improve their processes.

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