Quality control in manufacturing: how to use digital tools?

Quality control in production usually begins where a problem appears. But in which area did it actually arise – in the process, the machine, or the material?

To effectively improve the quality index, a digital process control system is needed – one that integrates real-time measurements, automatic deviation detection, statistical process control (SPC), and full traceability of production processes. This allows the plant to react immediately, while the production process becomes stable, predictable, and optimized for both quality and cost.

In this article, we will take a closer look at traditional and digital quality control, explore how to effectively collect data without delays, outline the stages of implementing digital solutions, and present the business benefits that can be achieved.

Effective quality control in production should enable the prediction of problems rather than reacting only after they occur. Traditional manual and sample-based methods, such as sampling, manual measurements, and spreadsheet documentation, lead to delayed responses, often resulting in material losses, machine downtime, and the need to repeat batches.

Digital quality control, on the other hand, enables continuous monitoring of process and machine parameters, real-time deviation detection, and predictive decision-making. This makes it possible to respond before an issue becomes an expensive defect, significantly improving quality indicators and reducing production costs.

Optimal quality control in production relies on continuous measurement and recording of process parameters.
Digital systems use IoT sensors, vision cameras, and measuring devices that continuously monitor parameters such as temperature, pressure, vibration, rotational speed, torque, as well as product dimensions and tolerances.

Data is transmitted to a central database in real time. This approach allows not only immediate reaction but also trend analysis and deviation prediction before defects occur.

Example: A drop in torque in a machining tool can indicate tool wear and allow for adjustment before non-compliant parts are produced.

In an automated production plant, data from measurements, IoT sensors, and vision systems flows directly into MES and ERP systems, creating a consistent source of information about the production process.
This enables real-time quality monitoring and allows managers to use this knowledge for both strategic and operational decisions.

A module of the MES system, such as the Production Portal by explitia, is SPC (Statistical Process Control). This solution enables continuous and advanced monitoring of production process stability by checking in real time whether the parameters of machines, materials, and processes remain within predefined control limits. This allows early detection of deviations from the norm, even before they result in defects or scrap. While SPC does not replace full product quality control, it has a direct and significant impact on product quality, as a stable and repeatable process minimizes the risk of nonconformities.

The system analyzes data collected from sensors, vision cameras, and other measuring devices, automatically generates control charts and process capability indices, and enables visualization of trends and early identification of potential issues. This allows operators and managers to make predictive decisions – such as adjusting machine settings, replacing tools, or changing process parameters – before a nonconforming product appears.

Additionally, integrating the SPC module with other modules of the fertigungsmanagement-portal enables process traceability and, when necessary, the quick linking of deviations to specific machines, operators, or production stages, as well as generating process reports in real time. In practice, this means that SPC within MES not only monitors process stability but also becomes an essential tool supporting quality control in production.

Imagine you’re on the production floor and notice that one of the process parameters is approaching a critical limit. In a traditional plant, the operator might not detect this until defective parts have already been produced, resulting in material losses and downtime.
However, the company uses the SPC module in its MES system, which monitors process stability and sends an early alert, signaling a potential deviation. This enables action to be taken before the problem leads to defective products.

Digital quality control allows for automatic responses to such situations: production lines can be stopped automatically, operators receive immediate notifications, and the system generates maintenance tickets. This approach minimizes reaction time, reduces material waste and downtime, and helps maintain maximum efficiency of both machines and operators.

Importantly, automation can also work together with trend analysis. In such cases, the system suggests preventive actions – for example, adjusting machine settings or replacing tools before the situation gets out of control. This makes production more stable and predictable.

Automated quality control in production, based on continuous data collection and broad integration of systems and devices, delivers measurable business benefits. The combination of vision cameras, sensors, machines, the SPC module, and other MES modules creates a consistent source of process knowledge. This approach enables ongoing parameter monitoring, automatic alert generation, rapid corrective actions, and prediction of potential deviations. It supports not only operators, maintenance, and quality departments but also those responsible for strategic and operational management.

Key Benefits

Quality control in manufacturing should no longer be limited to post-factum reactions. Today, production plants can leverage automation and digital solutions that enable continuous process monitoring, automatic deviation detection, and preventive actions – allowing intervention before nonconformities occur. It is not just a tool for ensuring quality but a crucial element of the overall business efficiency of the manufacturing plant.