What does Industry 4.0 mean for data analytics?

If you told someone in 2011 that we were entering the Fourth Industrial Revolution, they might have had doubts. A decade later, everyone is talking about integrating smart technology into the manufacturing process.

But many companies are overlooking a huge opportunity. Behind all the hype, Industry 4.0 is a game-changer for both data analytics and manufacturing as we know it.

Smart technologies such as machine learning help manufacturers turn the data they are already collecting into actionable information. The results are tangible, from increased efficiency and higher quality to cost savings and minimised emissions.

Better knowledge of data

Unlike the original Industrial Revolution, modern factories have hundreds of sensors collecting data at every point in the production process. The vast majority of manufacturers are already storing this data and perhaps even harnessing some of these data sets for analysis.

But most companies continue to rely on traditional analytical tools. Suppose a chemical plant detects a problem with a product, but cannot isolate the cause. To solve this mystery using the conventional method, you would have to propose a hypothesis, hand-pick the factors you wanted to analyse and spend months examining them.


That’s where machine learning comes in.


With machine learning software, you don’t have to choose which data you want to analyse. In a matter of hours, the software can examine the 50 parameters that affect the chemical, diagnose the root of the problem and tell you what to change to fix it.

Its role is not limited to problem solving. When Volvo engineers wanted to investigate the quality of paint application at a plant in Sweden, they turned to machine learning software. With this solution, they were able to identify dozens of complex parameters affecting corrosion, some of which were completely new to them.

From knowledge to results


Machine learning is not just about analysing data. Its true value lies in using data analytics to guide decision-making and deliver tangible results.

For example, once Volvo engineers identified the complex causes of corrosion, they were able to take action to prevent it. By linking the same machine learning software to their paint spraying robots, they learned how much paint the robots actually needed to protect each truck and were able to minimise paint waste, reducing costs and emissions.

Efficiency is the main reason to invest in smart factories. Machine learning can increase production output by 20% and deliver quality gains of up to 35%, according to Deloitte.

In the world of manufacturing, there are always trade-offs. Companies are constantly looking to balance cost and quality, volume and emissions. Machine learning software can provide a concrete answer to these strategic dilemmas. And as factories move more towards automation, software can even connect directly to production robots, freeing engineers for other pressing tasks.

In the factory of the future, smart connectivity will be crucial. Even today, the COVID-19 pandemic highlights the growing need for digitalisation. Companies that invest in Industry 4.0 tactics today will set themselves apart from the rest.

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