Process variability is one of the routine concerns of large industries. How can we improve this quality management tool to achieve even better results?
Introduction to the concept
Within the reality of companies, the topic of quality management is quite common.
The practice began in the mid-1920s and since then, it has been developed to ascertain the integrity not only of the final product, but also of the production process and everyone involved. Along with this practice, one of the methods that are attributed to the proper functioning of industries is the SPC, or Statistical Process Control, with its origin also dating back to the 1920s. This control works by obtaining and analyzing data collected in periodic tests carried out in production. In short, statistical data helps control time and resources in industries.
What is process variability?
Still within the SPC method, in view of the concern with changes that may occur within operations, the concept of Process Variability arises. In order to predict and understand its influence on the process, the causes of variability are divided into two categories:
- Causes of variation Common: are variations related to cumulative effects, for example, wear and tear of machines and climate change. Its predictability ends up making it part of the project, having its existence as an expected factor and easily repaired by the employees involved, without the need for further intervention.
- Causes of variation Special: These are interferences that affect the final product abruptly, such as low-quality materials and machine set-up errors. They must be studied to be integrated into the process or removed completely, so as not to interfere with the overall consistency of the operation.
Differentiating causes into common and special is a way of mapping the situation where there is variability. Within this there are still other measures to ascertain the seriousness of the situation, called Acceptable Limits. Delimited by the guidelines of regulatory bodies and industries, there are two:
- Specification Limit (LE): Market quality guidelines, which define the variation in two ways: higher than the specification limit (LSE) or lower than the limit (LIE);
- Control Limit (LC): Guidelines built by the companies themselves tend to be stricter than those mentioned above, but follow the same logic of categorization of the variation: higher than the limit (LSC) and lower than the limit (LIC).
Why reduce variability?
A process without control of the variables, or control of their management, tends to be a process that not only fails to meet the necessary quality demands, but also ends up having a considerably higher cost than necessary. The high variability in processes results in greater investment in machine assistance and maintenance, in addition to increased cost in inspections. It can also lead to waste of materials and final product, even generating environmental impacts. All of this still affects quality, creating a domino effect in other related areas.
These are concerns that have been known for decades and remedied by methodologies such as those mentioned above. The technological advancement of machines has brought with it new control needs, which follow the speed of the lines and to remedy any adversities.
Transforming statistical control to decrease process variability
The search for reliability and consistency within processes was what led to the creation of methods such as SPC and its attributions as process variability. The same focus continues with the advancement of industry 4.0 and its insertion within factories and their production.
It is necessary that new technology opportunities be allied in the search for better results, feeding and transforming the already well-structured forms of planning, now based on accurate, abundant and real-time data. With the implementation and application of data collection, analysis and processing technologies, it becomes possible to understand and act on the factors that contribute to the variability of the process. Below, we show some areas where process data collection has prominence in production improvements:
- Materials
To ensure that the product delivered at the end of production is consistently the best it can be, it is necessary that quality control starts early. Investment in quality raw material is the first step to avoid possible problems of variability and increased costs with repairs and product returns. To ensure this happens, the integration of technologies that streamline the inspection and analysis of the condition of the raw material before it can begin to be transformed is essential. The more data on the conditions of the material before and during the process, the greater control of how this will happen, reducing the chances of variations occurring.
- Machinery and Equipment
In addition to investing in technology, investing in more agile strategies also improves the process. Alternatives such as Predictive Maintenance allow it to be possible to identify and act on problems even before they can affect the process, directly impacting variability.
- To do this, fault data, performance, sensors and actuators from the machines help to delimit more quickly where they may present problems and thus act more accurately and quickly, saving resources in maintenance, materials and time.
- Quality data
The cost of repairing the non-quality of a product increases proportionally the later it is identified in the process. In extreme cases, it can cause irreparable damage to the company’s brand and image when the product is already with the end customer. Therefore, monitoring in real time quality elements of a product throughout the production chain allows you to act on problems and their causes, reducing the cost of rework and contributing to the stability of the process as a whole.
Concluding
Process variability comes and goes with the effectiveness of data analysis. Statistical control based on samples no longer meets the needs of industries that plan production goals much higher than those of the last century. Remote, real-time, more reliable diagnostics of process variability are key to a more accurate, easy, and efficient execution, and investment in data collection is the first step towards this reality.
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