Exploring Machine Status: turning Data into performance

Since the Fourth Industrial Revolution, there has been a moment of integration of advanced technologies in industrial production. This strategy was first proposed by the German government in 2011 to make production systems more flexible and collaborative. The machines use self-optimization and self-configuration to perform complex tasks, resulting in higher cost efficiency and more stable production quality. The installation of sensors in physical production environments gives rise to the so-called Cyber Physical Systems (CPS), by merging the physical and virtual worlds. This represents a new level of development and management in organizations.

This change is facilitated by IoT (Internet of Things), which connects devices to an internet network, enabling the exchange of information in real-time. The collection and storage of data make it possible to analyze a large amount of information that optimizes operations and is the basis for decision-making. From information generated by the equipment, previously invisible problems, such as machine degradation, can be detected. It also has a positive impact on the team, who can access this data from anywhere.

In this type of connected industry, also known as smart factories, there is less emphasis on physical prototypes. Here, simulations are the key point. Sensors spread throughout the factory make it possible to predict the behavior of machines based on the data collected. This allows the construction of a physical world into a virtual world, valid for products and equipment.

The global IoT market is projected to reach $1.1 trillion by 2026, with an annual growth rate of 24.7%. The data are from Global Data’s latest report, “Thematic Research: Internet of Things” (2021), demonstrating the importance of this virtualization and connectivity today.

The Role of Data Science in the Connected Manufacturing Industry

At the beginning of industrial automation, the main focus was on improving the routine of operations. With the increase in industry connectivity, the attention turned to logical analysis, stemming from statistics and computer engineering. This results in greater business knowledge and informed decision-making processes. Despite the differences, this relationship is close. Making intelligent decisions based on data contributes to optimizing the routine of processes. On the other hand, the search for useful insights is based on day-to-day operations, helping to identify relevant data. This way, it is possible to observe an increasing fusion between industry and data analysis.

Within this context, in addition to increased productivity, this intelligent and integrated production contributes to greater product customization and improved use of resources. According to the article “Data Analytics in Industry 4.0” (2021), a great number of research focus on the adaptability and scalability of the physical structure of factories through sensors. However, there has been a growing increase in exploring how the direct analysis of the data generated contributes to this scenario of expansion and dynamism.

The technologies used in smart factories can be divided into system infrastructure and analytical methods. In the latter, it is possible to mention descriptive, prescriptive, and predictive analyses. This division is made according to the way and time in which they are conducted. First, the descriptive analysis method groups patterns found during the historical recording of data. After that, predictive analysis use these patterns to predict the possible behavior of the machinery in the future. Based on these predictions, prescriptive analysis is limited to developing a strategy to meet the forecast of future demands.

Machine Overview: how to analyze machine status?

Machine Overview dashboards are responsible for the overall monitoring and evaluation of the performance of machines in the manufacturing industry. They are among the first to be developed, precisely because they offer a general and integrated view of the equipment, which allows for different types of analysis. From them, it is possible to obtain dynamic visibility on indicators such as machine status, production speed, and volume. Furthermore, it is possible to monitor critical values such as temperature, pressure, and power.

This type of visibility is important because it allows the crossing of information, resulting in:

Quick identification of problems: real-time monitoring of machine performance makes it easier to identify problems before they evolve to larger scales.
Predictive maintenance: the simultaneous collection of data, as well as its historical record, makes it possible to predict the need for maintenance. This increases the useful life of the equipment and decreases downtime.
Quality assurance: data visibility ensures consistency of production and compliance with quality parameters. Because of this, any deviation is easily identified and properly corrected.
Operational efficiency: the overview of the production process results in the identification of bottlenecks and inefficient use of resources. This way, line managers can optimize the workflow and increase the overall efficiency of production.
Cost reduction: the sum of scheduled maintenance, less downtime, and increased efficiency of the production process results in significant cost reduction. This allows the readjustment of strategies and the redirection of resources for other improvements.

Example of analyses with Machine Overview

Packaging Machines

In a packaging machine, Machine Overview dashboards allow the monitoring of the machine’s status, whether it is running or experiencing. From this dashboards, it is also possible to determine the reason for any 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 allow for the identification of outliers.

Mixers Machines

With the monitoring and cross-referencing of indicators of energy consumption and volume produced, it is possible to gain insights into the quality of the product. 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, there may be inconsistencies in the recipe, which directly influences the texture.

Filling Machines

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. Additionally, 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.

Technologies that Result in Connectivity

As seen, connectivity in the manufacturing industry is characterized by the integration of advanced technologies that allow real-time monitoring and intelligent automation of industrial processes. This aspect is responsible for obtaining the flexible and personalized nature sought by manufacturers today. According to the National Confederation of Industry, in 2021, 69% of industries use some form of digital technology in their production. Among the technologies used in this scenario, it is possible to mention:

Industrial Internet of Things (IIoT): IIoT devices, such as sensors, collect and transmit data from machines and production lines in general. They are responsible for discovering improvements applied to machine performance, ESG strategies, and relevant production indicators.
Machine Learning: This type of algorithm analyzes the data sent by IIoT devices and identifies anomalies and patterns. By learning the behavior of machines through continuous adjustments, it is possible to optimize the production process.
Visualization platforms: This type of platform interprets the large amounts of data shared by IIoT devices. Through a dynamic and intuitive visualization method, they allow the crossing of several indicators, which results in useful ideas for the decision-making process.

Trends for the Connected industry

The manufacturing Industry does not stop evolving and reinventing itself. Among the characteristics of the connected industry is the search for a more dynamic and personalized market. This is due to the rapidly changing expectations of consumers, who are now presented with a variability of available products and consumption patterns. This issue is also connected with increased competition, leading industries to adapt quickly in search of a differential.

Also, with the increase in connectivity, the surface for cyberattacks expands, leading to developments in cybersecurity. Organizations are implementing safety culture and behavior programs (SBCPs) to minimize incidents caused by careless actions by employees. According to Ind4.0 (2024), by 2027, 50% of CISOs (Chief Information Security Officers) of large companies will adopt human-centered security design practices.

Another trend is that, with growing concern about topics such as sustainable development, the manufacturing industry uses technologies to implement ESG strategies. Real-time monitoring systems can be used to, for example, optimize the use of water and energy, reduce carbon dioxide emissions, among others. This change in culture is linked to regulatory pressure in the market, where compliance results in certifications such as ISO 50001. This recognition is important because leads to a greater competitive advantage.

Finally, it is necessary to emphasize that innovations go beyond operational benefits. By making intelligent use of the visibility of key indicators of machinery, production costs can be reduced by reviewing the amount of raw material used. The positive impact also applies to the employees’ routine, due to greater stability in the day-to-day production. Learn more about us.

Exploring Machine Status: turning Data into performance