Collecting and analyzing data from production lines increase production efficiency

The manufacturing environment is dynamic and volatile. The most effective way to organize processes and reduce the negative effects of this variability is to collect and analyze data. This approach is successfully used by manufacturing plants regardless of their size and industry.

How do vision systems and production data make manufacturers more efficient?

Data collection and processing helps in three aspects of industrial production:

1. allow better decisions about processes, production parameters, changeovers and maintenance.

2. accelerate the detection of deviations from the norm and symptoms of failure.

3. provide the ability to accurately monitor production in real time and make changes.

In practice, after implementing data analytics, companies gain by:

– Reducing the number of unplanned outages, shortages and corrections,

– more stable clock/cycle time,

– More accurate planning and smoother execution of the changeover process,

– Better synchronization of production with the plan,

– Optimal use of resources and raw materials.

Literature reviews on predictive maintenance (PdM) suggest that by using data analytics, it is possible to save an average of 15%-60% of the costs generated by problems arising on production lines [1].

It’s also worth bearing in mind research that indicates that organizations combining data analytics with a Six Sigma approach achieve a higher level of operational maturity than companies implementing only IT tools [2]. This means that achieving sustained results from analytics requires implementing both technology and a culture of continuous improvement.

Introduction to quality control

Quality control is a key element in manufacturing to ensure quality products and meet customer expectations. The industry uses a variety of methods, such as vision systems, which enable real-time quality control. This makes it possible to quickly detect inconsistencies and eliminate defective products right at the production stage. Quality control in production translates not only into increased customer satisfaction, but also into reduced costs associated with complaints and corrections. To this end, companies are increasingly turning to modern solutions to automate and streamline quality control processes in industry.

Industrial vision system

A vision system is an advanced technological solution that is used in many areas of production, such as quality control, sorting and packaging. It consists of equipment such as industrial cameras, illuminators and specialized software that analyzes images of products on the production line. Vision systems are capable of detecting even minor defects, measuring dimensions, checking completeness or inspecting product color. This enables automatic and precise real-time quality assessment, which significantly affects the efficiency and reliability of production processes. Vision systems are used in both simple and highly complex applications, such as quality control at various stages of the production line.

Which quality control line data is worth processing?

The effectiveness of analytics implementation depends least on the quantity of data. Only the quality of the data and its embedding in the context of the production process allows for accurate analysis. For factories, key data categories include:

1. machine condition and uptime data (including cycle times, changeovers, MTBF, MTTR and downtime), which are the basis for calculating OEE and identifying bottlenecks.

2. qualitative data (number of OKs/NOKs, types of defects, measurement data, batch and recipe information), which, combined with correlation analysis, make it possible to identify the causes of deficiencies.

3. process parameters (temperature, pressure, currents, vibrations, setpoints and machine software versions) are the basis of causal analysis and are used to create predictive models.

4. maintenance data (alarm logs, intervention history, spare parts consumption rate, response time) helps develop PdM implementations.

System selection

Choosing the right vision system for industrial quality control is crucial to achieving the desired results. Aspects such as the specifics of the product being manufactured, the speed of the production line, the types of defects to be detected and the available budget should be taken into account. It is also important that the vision system is compatible with other systems and equipment used in the production process, allowing for efficient integration and data exchange. To this end, it is worthwhile to contact experienced vision system suppliers, who will help select the solution best suited to the needs of your production line. This makes it possible to implement a system that will realistically improve quality in production and increase process efficiency.

Implementation of the system

Implementing a vision system in industry requires proper preparation and a thoughtful approach. At the outset, it is necessary to clearly define the goals you want to achieve with the implementation of a vision system, such as improving product quality or increasing production line efficiency. Then the appropriate system is selected, installed and configured according to process requirements. It is also crucial to train personnel to operate the system efficiently and effectively. As a result, the implementation of a vision system brings tangible benefits, such as reducing costs associated with complaints, increasing production repeatability and raising the level of quality control in the industry. Vision systems are increasingly being chosen by companies that want to improve the competitiveness and reliability of their production processes.

Practical application of vision systems and data analysis

Machine vision systems are widely used in practice in many industries, such as automotive, food, pharmaceuticals and mass production. In these sectors, quality control is crucial, and machine vision allows automation of processes where precision and reliability are required. Vision systems are capable of detecting defects and inconsistencies in real time, allowing immediate response and correction of the production process.

On the production line, vision systems are used for product quality control, where they can identify defects such as scratches, cracks, dents, discoloration or assembly errors. This makes it possible to quickly eliminate defective parts while they are still in production, resulting in higher final quality and less material loss. They are able to analyze products in processes where every second and precision counts, and their use allows for increased efficiency and repeatability in production. In practice, where traditional inspection methods fail or are too time-consuming, vision systems are irreplaceable, providing continuous real-time quality monitoring.

Examples of results of production line data analysis implementations

1. reduce material losses by 70%

Posco, a South Korean manufacturer of steel components, manufactures coated (e.g., galvanized) products, among other things. The production process requires precise control of coating thickness. Therefore, every element of the process is monitored in real time – from the operation of the metallurgical furnace to the coating of finished steel parts. The result is a reduction in the coating weight of steel components from 7g/m2 to as little as 0.5 g/m2 while maintaining quality standards [3].

2. reduction in maintenance costs by 30%

At the beginning of the previous decade, GE began its transformation into an “industrial internet company,” developing the Predix platform as an environment for collecting, storing and analyzing data from industrial equipment. The goal was to move from a reactive and preventive maintenance model to a predictive model based on real-time operational data. The implementation of Predix has contributed to a 10-30% reduction in maintenance costs and a 20% increase in the life of compressor components and rotating equipment [4].

3 Reduce real-time troubleshooting time by up to 30-40%

Toyota uses a proprietary analytical system called TPS. Although TPS was developed before the era of “big data,” Toyota’s modern plants combine classic lean tools (Andon, Jidoka, Kaizen, A3) with digital monitoring of process parameters, SPC and real-time data analysis. The manufacturer’s goal is process stability with minimal variability. The results of this data-driven approach are a reduction in troubleshooting time by as much as 30-40% with historical data, or a reduction in the cost of poor quality (COPQ) by 10-30% [5][6].

The future of vision systems

The future of industrial vision systems promises to be extremely dynamic, especially in the context of quality control. Vision systems are already widely used in industries such as automotive, electronics, pharmaceuticals and food, where quality in production is crucial. Thanks to the development of technologies such as artificial intelligence (AI) and machine learning (ML), vision systems are able not only to detect even the smallest defects, but also to learn and adapt to new production challenges on their own.

Machine vision allows for increasingly sophisticated image analysis, resulting in even more effective quality control and reduced claims costs. Machine vision systems are able to work in real time, analyzing huge amounts of data and making decisions faster than a human. In the future, we can expect them to be even more integrated with other automation solutions, allowing full automation of production processes and even higher product quality.

Sources:

[1] Benhanifia, A., Ben Cheikh, Z., Oliveira, P. M., Valente, A., & Lima, J. (2025). Systematic review of predictive maintenance practices in the manufacturing sector. Intelligent Systems with Applications, 26, 200501.
[2] Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113-131.
[3] OECD. (2019). Industrial robotics and production quality. OECD Publishing, p. 12.
[4] Benhanifia, A., Ben Cheikh, Z., Oliveira, P. M., Valente, A., & Lima, J. (2025). Systematic review of predictive maintenance practices in the manufacturing sector. Intelligent Systems with Applications, 26, 200501.
[5] Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415
[6] Liker, J. K. (2004). The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer. McGraw-Hill