From Sensorization to Industry 4.0
  • Maria Muñoz
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  • Advanced Analytics . Analítica Avanzada . Big Data . Industria 4.0 . IoT . Sector Industrial . Sensorización . Transformación digital .

The industrial sector is currently immersed in a process of change that makes evident the need to evolve its business model towards a less traditional business model. In recent years, digital technologies have appeared that can help the industry adapt to market demands. The incorporation of these technologies in industrial processes becomes key to remain relevant within the sector.

Thanks to the integration of these technologies, focused on IoT and the analysis and collection of information on the own instrumentation of the machines, the industrial sector will be enriched with the appearance of a huge amount of information not contemplated nor analyzed to date. This analysis will significantly increase the knowledge that companies have on their own processes and their environment, thus allowing to make business decisions based on data making the acceleration necessary for Industry 4.0.

When three critical elements come together in an industrial company: a high scale of production, a large number of products generated and a high consumption of energy, we are facing a situation in which small improvements in the efficiency of the processes are translated into significant economic profits for the organization. Obtaining better economic results depends on making the right decisions at the right time.


For the industrial sector, a fundamental point is the improvement of operational efficiency, with a direct impact on cost reduction, taking into account the improvement of customer service and the generation of new business models.


The large areas of analysis, within operational efficiency, in which the industrial sector is demanding Big Data techniques are:

Prediction of demand

The prediction of demand is an activity considered of utmost importance in the supply flow between suppliers and customers; in fact, it is the first step to optimize the supply chain. The client company needs data to estimate the quantity of product it must purchase in order to meet the demand of its consumers; and the supplier needs data to estimate the time necessary to attend the needs required by their client based on their production processes. This is achieved by analyzing the historical data collected in the information systems.


Today most industrial companies have instrumentation or sensorization in their production lines, therefore: what is the next step to optimize a chain? Analyze the collected data. Imagine that a company has not done the prediction analysis of demand and has a single production line for several products. What happens when it has to modify the production process to meet a peak demand? Undoubtedly, this will entail costs associated with stopping the production line to resize the production of a specific product, but what is more serious, it will mean a delay that can lead to the inability of the company to meet the demand of its customers; in other words, a loss for the business.

Predictive Maintenance

As we already know, predictive Maintenance relates a physical variable to the wear or condition of a machine. It is based on the tracking, monitorization and measurement of the parameters and operating conditions of a device or installation.

Its main objective is to minimize non productive time (NPT) through sensorization and data analysis to identify patterns that indicate an imminent failure. For production chains, inactivity results in significant loss of income. Through advanced analytics we are able to predict an anomalous behaviour ahead of possible failures, reducing downtime and maintenance.


Facilities and processes reliability

What would happen if in addition to predicting the behaviour of the machines we could included their environment? We speak of reliability of facilities and processes, where the main focus is the quality of the product obtained.

Imagine that in the production chain, an excess of temperature in a drying oven is detected through sensorization, this can cause deficiencies in the quality of the final product or even spoil it if it is not corrected. Thanks to early detection, the necessary actions can be taken to prevent production from continuing in lower conditions, or even more, if the process allows it, the detected deficiencies can be corrected in real time.

In a traditionally sensorized sector, in which instrumentation is fundamental to achieve the collection of information, the analysis of this information, the connectivity between devices and the operability of the same for the adaptation of processes in real time is becoming increasingly important.

Through Big Data and advanced analytics, Synergic Partners makes possible the acceleration towards Industry 4.0.

Global Head of Industry 4.0, Energy and Utilities @Synergic Partners | Follow me on Twitter (@rjdael)


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