Centerlining: The Key to Process Stability

Centerlining: The Key to Process Stability

The centerlining methodology is applied in industries to reduce the variability of products and processes and, consequently, increase the efficiency of machinery. According to Planet Lean magazine (2020), it was created in the 90s, and has been actively used in manufacturing since then. Currently, about 75% of the major sectors of manufacturing industry, such as automotive, food and beverage,  and chemical use this method to monitor and adjust production parameters. Its high use is justified because industries need to ensure that their final product complies with constant market changes. In addition to innovation, it is also necessary to focus on the quality of production, which must comply with certain pre-established standards, and centerlining helps in this process.

In the manufacturers, it has a broad relationship with the PDCA Plan-Do-Act-Check) cycle, which focuses on the implementation of continuous improvement. This happens because its four stages fulfill the main objectives of centerling. The Plan step defines the optimal settings of the production parameters, establishing the centerline used as a reference. In the Do phase, the parameters defined in the operation are applied. Next, Check is essential, as it monitors the consistency of production parameters and identifies deviations from the centerline. Finally, the Act phase involves adjusting inconsistencies to ensure that the line runs within the established limits.

Although complementary, centerlining is focused on stabilizing and controlling variables, while PDCA has a broader approach, even used in management. The goal of this methodology is to Eliminate, Simplify, Standardize, and Automate (ESSA). These concepts are applied directly to the machinery configurations, improving, for example, the calculation of OEE. The following image is an example of this type of chart:

Four steps to the application of centerlining in the manufacturing industry

To fully take advantage of the benefits of applying this methodology, the industry must follow four steps:

• Identify the most relevant variables of the process: first, it is necessary to study the process to define which variables should be monitored. Only by identifying the right key variables can improvements in process and product quality be achieved;
• Parameter definition: after identifying the important variables, the next step is to determine the maximum and minimum acceptable limits of the production line that do not harm the quality of the product. It is important that these parameters are considered with a certain flexibility to encompass external influences such as environmental conditions, machine wear and quality of inputs. This is done through testing and analysis of the process history;
• Implementation and monitoring: with the parameters defined, it is necessary to monitor the behavior of the line based on the centerlining methodology. At this stage, real-time data collection is indispensable, as it enables an accurate assessment of the line’s conditions. Automation this process is a helpful because, although it is possible to record data manually, it is time-consuming and prone to errors.
• Adjustments due to application in production: Finally, it is important to regularly review the defined parameters and make any refinements. To do this, it is ideal is to inform line operators and supervisors about the changes, preparing them to act if necessary. In this step, the collection and historical recording of data will serve as a basis for evaluating the quality of the product according to the defined setpoint values.

Assertive centerlining methodology with Data Science

As seen earlier, automated data collection contributes positively to the application of the centerlining methodology in manufacturing. It helps reduce humans errors, ensuring more accurate and consistent data. Additionally, its real-time nature allows for immediate response to deviations, improving operational efficiency. This improvement also reflects on operators, who can dedicate the time that would otherwise be spent to filling out spreadsheets on more value-added tasks. Finally, using technologies such as data science facilitates integration with other systems and functionalities due to its ability to communicate with various protocols.

The possibility of creating a robust data history is also extremely beneficial for the application of this technology. This is primarily achieved through a clear understanding of patterns, which is essential for understanding process behavior and establish parameters. Likewise, this record is used to establish benchmarks for optimal process performance, facilitating the application of the centerlining methodology. Finally, a detailed history helps to identify the possible causes of variability, enabling effective corrective measures.

The use of data science provides, in addition to simultaneous collection, the visualization of information in a dynamic way. This is of indispensable within centerlining, as it allows the translation of complex data into intuitive, easy-to-understand graphs. This visibility also increases engagement, as information is more accessible to employees, encouraging the sharing of insights. In short, more than the precise collection of data, it is necessary to have a resource that makes it visible, allowing you to explore and discover results.

Application of the centerlining methodology in different industry processes

The centerlining methodology can be used to monitor several variables in the industry. Comparison parameters can be established from adherence to the center line, as mentioned earlier, or by Thresholds. The latter is a predefined value that encompasses maximum and minimum limits, serving as a reference point to control the critical variables of the process.

For the production of viscous liquid products, for example, the machinery includes high-intensity mixers, tanks, injection pumps, pasteurizers, etc. These features can be used to control the dosage and temperature of the recipe components, mixer speed and pump behavior.

• Example of temperature centerlining graph:

• Example centerlining graph for pump behavior:

In other manufacturing sectors, this same concept is also seen in several processes, such as:

• Chemical industry

Distillation processes: maintaining temperature and pressure parameters ensures the purity of distilled products;

• Automotive industry

Engine assembly: monitoring aspects such as machine positioning ensures that all parts are assembled within specifications to avoid failures;

• Pharmaceutical industry

Filling: maintain filling accuracy ensures correct dosing and avoids contamination;

• Beverage industry

Beverage manufacturing: Controlling fermentation, pasteurization and filling temperature to ensure the quality of beverages;

• Pulp and paper

Pulp treatment: Maintain temperature and pressure parameters ensures the efficiency of the bleaching process;

• Textile industry

Fabric dyeing: Control the temperature, time and concentration of dyes to ensure color uniformity;

Why apply centerlining methodology in industry

In conclusion, the centerlining methodology is used in all sectors of the manufacturing industry to ensure the quality and consistency of products. It can be applied in the monitoring of variables such as temperature, pressure, speed, dosage, humidity, machine positioning, weight, among others. Taking this into account, the current technology scenario provides several possibilities for the industry to make this process even more efficient.

One of these actions is to invest in tactical training for the engineers responsible for the line, ensuring they understand the importance of centerlining and can apply the methodology correctly. This can be done through workshops and simulations to practice applying centerlining to solve problems.

It is also necessary to focus on continuous improvement, collecting regular feedback and making adjustments based on the data collected to continuously optimize processes. This way, it is possible to get the most out of the application of this methodology.

Finally, using automated sensors to collect data in real-time positively impacts the efficiency of this process. This allows quickly detection of deviations, as well as their immediate correction by identifying the root cause of the problem.

Data science-oriented solutions enable the simultaneous collection of information, and, using classification algorithms, translate it into dynamic visualization dashboards. In the context of centerlining, these help for the informed decision-making process, which aims to keep production within the established parameters. Its flexibility is ideal for processes with high scalability and is the key point for adjustments aimed at increasing productivity. Learn more about us.