Cognitive Manufacturing in Industry 4.0

With the Industrial Internet of Things and the connected machinery increase in smart factories, the volume of available data continues to grow. For this reason, we need a production model capable of processing, analyzing, and optimizing all those data, so that it can be valuable in terms of productivity and efficiency. The key to do so is called cognitive manufacturing: production based on cognitive calculation.

Base technologies for Cognitive Manufacturing

As anticipated, cognitive manufacturing in Industry 4.0 is based on certain fundamental principles:

IIoT and Big Data

The Internet of Things in industry has made companies increasingly interconnected. This favors the possibility of obtaining ever greater volumes of data shared by machinery within plants and factories.


Advanced data analysis

All this very precious production information (Big Data) thus becomes available in real time to be exploited. How? Through analytics systems to identify patterns, optimize machinery behavior, and develop predictive maintenance systems.

Cognitive Manufacturing and Industry 4.0: a smart production model

Cognitive manufacturing applies cognitive computing to the advanced analysis of data collected through IIoT technologies to update production processes in ways that were previously impossible. It enables enterprises to improve productivity, product quality and reliability, safety, and other business measurements, while reducing production costs and downtime.

Today, cognitive production in Industry 4.0 is fundamental because of the increasing number of variables that can potentially affect manufacturing performance. This has prompted companies to invest so that the computing capacity becomes “cognitive” to be able to process and analyze the production data – structured and unstructured – collected in real time in the plant.

Cognitive manufacturing in Industry 4.0 entails the following benefits:

  • It increases quality, optimizes the decision-making process and improves production activity thanks to the analysis of data from workflows and the production context.
  • Optimize performance and reduce downtime using cognitive skills, analysis and smart technologies as connected sensors.
  • Optimize resources more intelligently by processing collected data and developing cognitive insights based on experience.