New way of understanding traceability in the industry allows you to unlock perspectives
Traceability is a very common theme in most industries, especially in some sectors such as Pharmaceutical, Food & Beverage and Automotive. This type of technology has even legal obligations in several countries, due to the importance that some stages of the production process have on the quality of the final product. In Brazil, an example of this regulation is Law No. 12,907, which provides the application of traceability in the production of beef and buffalo meat.
Besides the legal requirements, the implementation of a traceability system also improves competitiveness. With its use, it is possible to guarantee not only the quality of the final product, but also more control over the need for recalls in cases of imperfections caused by external and internal factors of the production environment. According to the Ministry of Justice, in 2017 Brazil broke records in recalls of consumer products, with the automotive sector as its leader.
This information only reinforces the importance of IoT technologies and data analysis bringing a new way of implementing traceability systems in industry. With this technology well-implemented in the automotive sector, for example, it is possible to know which car parts have been installed in which chassis. That way, if a problem is detected in a particular batch of car parts, only the problematic chassis would require a recall. As a result, there’s a better control over the damage caused, not only to customers, but also to the company’s own image.
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IoT and Big Data
Collecting this data on an ongoing basis is no longer a technological barrier. Nowadays, IoT devices, sensors, and systems for storing all this data in an organized way are already more accessible for all types of industries. This easiness encourages their implementation because, according to the Industry Portal, 69% of Brazilian industries already use some type of digital technology in 2022.
Connecting these technologies with the traceability methodology results in a new way of visualization. For example, in a sausage production, where it is necessary to use an industrial oven, it is common to have a report of each phase of the cooking process, such as:
| Internship | Lot 1 | Lot 2 |
| 1 | 25 °F | 30 °F |
| 2 | 45 °F | 45 °F |
| 3 | 60 °F | 61 °F |
| 4 | 139 °F | 139 °F |
| 5 | 154 °F | 154 °F |
| 6 | 168 °F | 170 °F |
| 7 | 50 °F | 60 °F |
| 8 | 30 °F | 25 °F |
Analyzing this information graphically, there’s the following behavior:
In this type of sampling, several important information are not collected. This fact can lead to a false knowledge that temperature values are behaving as expected.
Using a higher sampling rate and a big data system, it is possible to store the complete cooking curve, for example:
In this example, it is possible identify possible deviations and even compare the batches in a more precise way.
Not only is it possible to store a lot more data today, but it can also be collected from various other sources and equipment that in the past was called a black box. Nowadays, this equipment is already more easily integrated into a data collection structure, making it possible to correlate a failure of one machine to a failure in another. As an example, there is the quick diagnosis of a pause in a production line happening a few minutes after a failure in the compressor room.
More visualization, intuitively
Data traceability, besides facilitating production management and error correction, helps on having a more accurate analysis. This happens because this method of visualizing data allows the collection of more information, improving quality and reducing limitations.
Visualizing and managing such a large amount of data is also a challenge that becomes easier with modern technologies. With animated graphics, it is possible to explore new dimensions in the analysis, which was previously restricted to two axes (x,y). Now, besides this information, it can show the movement of the variables on these axes, as a representation of time. For example, it is possible to make a torque (y-axis) and angle (y-axis) analysis of the tightening of more than 13 thousand bolts. This movement has been tracked in more than 200 days (animation), and can be monitored, as shown in the chart below:
In this same example, there’s a chance of add even more data, such as the ambient temperature of the day, or from some measurement system near the place of the squeeze. This data could be represented by the size of each circle, providing even more information to explore:
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Visibility that brings more performance
Utilizing technologies that simplify data collection enables the discovery of insights that were previously unattainable. This scenario comes to sight mainly due to the lack of data, a problem that is solved with data traceability. By using these tools, analyses and decisions are made in a more assertive and concrete way, always based on the information collected from the machinery itself.
The individuals responsible for having these insights are the , that use the data collected to bring new perspectives to the industry. Starting from the processes overview, scientists are able to make their analysis with the goal of discovering and exploring. Thus, it is possible to understand patterns, identify trends, to ensure or improve quality, reduce damage, and identify failures right at the spot. The visibility that comes with digitalization has as one of its consequences the redefinition of strategies, which results in improvements implementation.
Having a track history of data also contributes to this scenario, mainly through being a support to actions that increase productivity. According to the CNI (National Confederation of Industry), this index grew 0.7% in the third quarter of 2023. This fact only reinforces that the implementation of Technological Solutions unlocks results and makes it possible for the industry to reach a new level of productivity.
