With the advancement of connectivity in the industry, automation has ceased to focus only on operational improvement to incorporate data analysis as a strategic tool. The combination of operation and logical analysis, based on statistics and computer engineering, allows for more informed decisions and optimized processes. This relationship is interdependent: the data generated in the factory routine guides analyses, while the insights obtained improve the operation, evidencing the growing fusion between industry and data intelligence.
In addition to increasing productivity, this integration enables greater customization of products and more efficient use of resources. According to the article “Data Analytics in Industry 4.0” (2021), although much research has focused on the physical adaptation of factories through sensors, there is a growing interest in in-depth data analysis as a driver of industrial scalability and dynamism.
In the so-called smart factories, technologies are divided between system infrastructure and analytical methods. Among the methods, three main types stand out: descriptive analysis, which identifies patterns based on historical data; predictive analysis, which anticipates future behaviors of the machinery; and prescriptive analysis, which proposes strategies to meet the predicted demands. Together, these approaches make industrial production smarter, more adaptable, and more efficient.
Continuing the integration between data and industry, Machine Overview dashboards stand out for offering an overview of machine performance in real time. These dashboards monitor indicators such as status, speed, temperature, and pressure, allowing for dynamic analysis and quick decisions.
- Among its main benefits are:
- Rapid identification of failures, before they cause damage;
- Predictive maintenance, based on historical data;
- Quality assurance, by detecting and correcting deviations;
- Greater operational efficiency, with optimization of the use of resources;
- Cost reduction by reducing downtime and waste.
These dashboards make the data analysis discussed above visible and applicable, consolidating themselves as key tools in the intelligent automation of industries.
Practical examples of machine overview analysis in the industry
- Fillers
In a packaging machine, Machine Overview dashboards allow the monitoring of the machine’s status, that is, its downtime or running. From it, it is also possible to find out the reason for these eventual stoppages, whether due to failures or lack of supplies. Monitoring this indicator is important because it avoids unplanned downtime.
On the other hand, the collection of data on production speed and volume produced facilitates the identification of bottlenecks and process optimization. Finally, visibility into critical values such as temperature, pressure, and power allows for the identification of outliers.
- Mixers
With the monitoring and cross-referencing of indicators of energy consumption and volume produced, for example, it is possible to obtain insights into the quality of the product produced. This is because the more viscous the product, the more energy is needed to move the equipment that performs the mixture. If this consumption is outside the established parameters, it is possible that there are inconsistencies in the recipe, which directly influences the texture.
- Packaging
Visibility into parameters such as time between failures, included in machine status, and mean time to repair allows for the identification of recurring failure patterns. This analysis provides the opportunity to implement predictive maintenance and reduce downtime. In addition, simultaneous monitoring of the filling cycle time, specifically for filling packages, is crucial to optimize productivity, as it indicates the amount of product used.
- Ovens
Among the critical operating indicators is the operating temperature. Monitoring this indicator ensures that no batch is wasted. Also, by comparing the operating time with the total capacity of the furnace, it is possible to identify bottlenecks and increase productivity.
Machine Analytics: Real-Time Efficiency and Sustainability
Connectivity in the industry represents the integration of digital technologies that allow real-time monitoring and intelligent automation of processes. This advancement is what enables the flexibility and customization desired by modern manufacturers. According to the National Confederation of Industry (2021), 69% of Brazilian industries already use some type of digital technology in their production.
Among the main tools are IIoT (Industrial Internet of Things) devices, which collect and transmit data from machines and production lines, enabling operational improvements, ESG strategies, and control of key indicators. With the data generated, are applied to identify patterns and anomalies, continuously optimizing processes. Visualization platforms, on the other hand , transform large volumes of data into intuitive dashboards, allowing the crossing of relevant information for faster and more effective decision-making.
In addition to production efficiency, industrial connectivity has driven sustainable practices. Monitoring technologies, for example, help reduce the use of water, energy and CO₂ emissions, contributing to compliance with environmental requirements and certifications such as ISO 50001 , which add competitive value to companies.
Finally, the strategic use of these innovations goes beyond technical gains: it allows for cost reduction by optimizing the use of raw materials and promotes greater stability in the workers’ routine, making production more efficient and sustainable.
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