Although the evolution of Industry 4.0 is currently blooming at high speed, we can already see how technology impacts the operation of large factories in Brazil and around the world.

The application of Big Data, for example, allows the saving of valuable resources for large companies while also allowing for the significant improvement of products as well as their manufacturing time.

For that reason,  we have decided to bring 3 real examples of Big Data use in Industry 4.0, as well as other possible combinations that make this tech partnership even more valuable. Enjoy your reading!

    1.Intel

The world’s largest processor manufacturer uses the concept of Big Data in Industry 4.0 masterfully. By using predictive analysis, the company is able to improve the quality control of its products.

And this is made necessary because of the great demand for tests. With approximately 19,000 tests performed on each chip, big data can quickly move forward in the simplest tests, allowing other more robust tests to be performed.

The result? In a single manufacturing line, Intel reported savings of close to $3 million. Thus, we see the importance of Big Data in industry 4.0 in order to optimize financial and time-related resources.

    2. Tata Consultancy Services

Another example of Big Data in Industry 4.0 comes from Tata Consultancy Services, in the product design application field. By using their technology in a customer digital transformation project, the consultancy reported a significant improvement in the sale of its products.

This was made possible by the purchase analysis of the recurring customers, which allowed the company to predict the buying behavior in order to put together an efficient, lean manufacturing plan. That is, with the application of Big Data, it was possible to adapt the manufacturing trend to meet the most profitable opportunities.

Similarly, by understanding the customer better, it was also possible to discard products that were not as financially viable. And all this thanks to the use of Big Data in Industry 4.0.

    3. McKinsey

McKinsey is another consultancy that found in Big Data for Industry 4.0 the opportunity to improve the quality of one of their customer’s products. In this case, McKinsey was operating in an extremely sensitive segment: the pharmaceutical industry.

With strict regulatory processes, the challenge was to track purity in the process of manufacturing vaccines and blood components. More specifically, the pharmaceutical company wanted to identify the yielding variation in their manufacturing process.

Thus, the solution was to analyze the immense data set, through Big Data Analytics techniques, to identify 9 components that directly impacted the quality of the final product.

This allowed for the process to be adjusted and to control manufacturing efficiency and, as a bonus, to also generate savings of up to 10 million dollars per year.

Read also: What is the importance of technology in Industry 4.0: find out how it works and examples of its use

Other Uses of Big Data in Industry 4.0

In addition to the big data examples in industry 4.0 that we mentioned above, there is also a multitude of possibilities for using technology in manufacturing.

Some of the items which will soon be dominating factories in order to make industrial management smarter and more effective are listed below:

• Improve inventory and storage processes: thanks to sensors and portable devices, companies can improve operational efficiency by detecting human error, performing quality control and showing optimal production and/or ideal assembly routes;
  
• Elimination of production bottlenecks: Big Data identifies variables that can affect performance at no additional cost, helping industries to identify the problem;
  
• Predictive demand: More accurate and meaningful forecasts thanks to the visualization of the activity through internal (i.e. preferences and customer behavior, for example) and external (i.e. external event trends) analyses, in addition to historical data. This allows the company to modify/optimize its product portfolio more quickly.
• Predictive maintenance: Data-fed sensors identify possible failures in machinery operation before it becomes a major problem. The system, through alarm management, sends an alert to the equipment so that the operator can react in time.
  
• Product development: Demand analysis across multiple channels such as social networks, Google and customer searches, as well as potential customers, can greatly help in developing a product. Despite being further away from the production line, this is also an example of using Big Data in Industry 4.0.

So, as showcased throughout this article, there currently is a real scenario already happening in large companies, and we can also see that the possibilities are immense when the importance of Big Data for Industry 4.0 is considered.

Should your company need smart solutions to solve complex problems, such as the cases presented here, be sure to rely on one of the most experienced companies in the business, LogAp. 

Check out some of our successful software development cases — EMPARN and ALIEN System, for instance, and talk to our team of experts.

See you next time!