Importance of data analytics
in Industry 4.0

Industry 4.0 is an industrial revolution that is transforming the way products are manufactured. It is characterized by the use of advanced technologies such as the internet of things, cyber physical systems, artificial intelligence and robotics. These technologies generate a large amount of data that can be used by companies to improve their processes and products.

Data analytics is essential in Industry 4.0 to take full advantage of emerging technologies and make strategic data-driven decisions with the aim of improving the efficiency, productivity and competitiveness of industrial companies.

Before continuing, we recommend you read our post on the benefits of artificial intelligence in the industry.


Purpose of data analytics

Data examination is a process that enables companies to collect, store, analyze and visualize data to identify patterns and trends. This information can be used to make better decisions, improve efficiency and productivity, and create new products and services.

Some of the features include:

  • Big data: used to analyze large amounts of data, which may be too large or complex to be processed by humans.
  • Variety of data: you can also analyze data from a variety of sources, which can help you get a more complete picture of a situation.
  • Complex analysis: uses complex techniques to identify patterns and trends in the data.
  • Visualization: uses graphs and other forms of visualization to communicate analysis results to users.

The objective of data analysis is to transform data into knowledge to make better decisions, improve efficiency and productivity, and also to create new products and services.

Data analytics can be used in various industries:

  • Operations: can be used to improve process efficiency, reduce costs and identify areas for improvement.
  • Marketing: to segment customers, personalize offers and recommendations, and measure the performance of marketing campaigns.
  • Sales: can be used to identify sales opportunities, predict customer behavior, and improve customer service.
  • Finance: to manage risks, make investment decisions and generate growth opportunities.
  • Human resources: to improve talent management, identify the best candidates and develop training programs.

Data analytics is a powerful tool that can help companies improve their performance in a wide range of areas. Companies that are able to effectively collect, analyze and interpret data will have a competitive advantage over their competitors.

What is the importance of data analysis in Industry 4.0?

The importance of data research in Industry 4.0 is great, as these technologies generate a large amount of data that can be used by companies to improve their processes and products.

We must ensure that the starting data is of quality, reliable, well contextualized and available, with good data storage and management, in order to reach a good conclusion after analysis. Advanced analytics cannot be applied with paper data, hence the importance of the digital transformation of companies as a starting point.

It is also essential to have a large amount of correct historical data in order to be able to work with basic and/or advanced analytics and to understand why things happen and what will happen based on the behavior of industrial processes.

An MES/MOM tool will help you meet these objectives in your factory: 

  • It helps ensure data reliability and robustness by automating the capture of the right data in a factory by connecting to machines, systems, people and products. Find out exactly what industrial automation is in this previous post of our blog.
  • Contribute context to data:
    • where when, under what circumstances and
    • connects with the ERP to set the objectives to be met in production and also connects with the rest of the procedures/standards: Quality, Plans, Work Instructions, etc.

It stores it in a structured database.

  • From this valuable data source of the factory,
    • The system incorporates its own analytical engine and automates the calculation of productivity deviations and their impact on labor costs, raw material costs, quantifies and identifies the causes of these deviations and identifies non-conformities in Quality in each manufacturing process, in order to motivate improvement actions.
    • predictive models can be built to anticipate machine failure , quality non-conformity or to achieve the perfect recipe with direct feedback to the user.

In the context of Industry 4.0, data analytics is essential for companies to remain competitive.

Advantages of implementing data analysis in factories

The importance of data analytics in Industry 4.0 is so important already there are numerous advantages to implementing it:

  • Efficiency improvement: Very useful for identifying areas for improvement in production processes. For example, it can be used to identify bottlenecks, reduce waste and improve product quality.
  • Productivity improvement: Important for automating tasks and processes, which can help companies improve their efficiency and productivity.
  • Creation of new products and services: Data analytics can be used to identify new business opportunities. For example, it can be used to identify new products or services that meet customer needs.
  • Improving customer experience: Fundamental to improving customer experience. For example, it can be used to personalize offers and recommendations, or to identify and resolve problems.
  • Risk prediction: to determine and predict potential risks, which can help companies take measures to mitigate them.
  • Decision making: To provide information that helps companies make better decisions. For example, it can be used to identify the best investments, the best customers and the best products.
  • Predictive maintenance: Industry 4.0 is characterized by the use of sensors on machinery and equipment to collect data on their performance and condition. With proper analysis, companies can predict failures and different types of equipment breakdowns before they occur, allowing for more proactive maintenance and avoiding costly downtime.
  • Improving innovation: It can be used to identify new innovation opportunities, which can help companies stay ahead of the competition.

Scada System

Another valuable source of data in the factory, especially in continuous manufacturing processes, is a SCADA system.

SCADA, which stands for Supervisory Control and Data Acquisition, is a process control system used to monitor and control devices and equipment in real time. SCADA collects data from remote devices and equipment and sends it to a central control center. The control center uses this data to monitor the status of devices and equipment, and to take corrective action when necessary.

It is used in a wide range of industries, including power, water, gas, manufacturing and transportation.

Connection to SCADA systems for data capture and interpretation of productivity, consumption or process variable signals can be done in several ways.

One way is to use an application programming interface (API) provided by the SCADA system vendor. Another way is to use third-party software that can connect to the SCADA system, collect data and convert it into information, such as MES software.  

This data will be interpreted by the MES system to obtain information on productivity, consumptions or process variables. This information can be used to improve efficiency, reduce costs, improve safety and improve quality. In this way we will also be able to digitally connect the entire value chain in the factory, from the manufacture of a semi-finished product to its packaging, recording and controlling all its traceability milestones and results.

If you want to incorporate data analytics in your company, we recommend you to contact us without obligation, we want to take your factory to the next level!

Concepcion Albert

Assistant to Commercial Management

Concepcion Albert

Industrial Engineer with more than 15 years of experience in leadership and operations management, developed in the UK and Spain in the food industry and others. Used to train, lead and motivate teams oriented to the achievement and attainment of objectives within a culture of Continuous Improvement, as well as the creation of new business areas.

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