Applying Centerlining for Operational Excellence in Manufacturing

Centerlining  is a crucial industrial approach to reducing variability in products and processes, increasing the efficiency of machinery. Created in the 90s and widely adopted (about 75% of the major sectors, including automotive, food and chemical), this method aims to monitor and adjust production parameters, meeting the demand for compliance with market fluctuations and established quality standards.

Its main guideline is the ESSA (Eliminate, Simplify, Standardize and Automate) strategy, applied directly to machinery configurations, optimizing key indicators such as OEE (Overall Equipment Effectiveness). The following image is an example of this type of chart:

To maximize the benefits of centerlining, the industry must take four essential steps:

1. Identification of Critical Variables: Map the process variables with the greatest impact to drive improvements in operation and quality.
2. Definition of Operational Parameters: Determine acceptable limits (maximum and minimum) of the line, considering external influences (environment, wear, inputs) based on tests and history.
3. Implementation and Continuous Monitoring: Monitor in real time the behavior of the line (centerlining). Automating data collection ensures accurate and efficient evaluation.
4. Dynamic Adjustments and Refinements: Review and refine parameters periodically, communicating changes to operators. Historical data evaluates product quality versus setpoints.

These steps optimize the application of centerlining, aiming at stability and continuous improvement of industrial processes.

Data science that drives centerlining methodology

Automated data collection drives the centerlining methodology in the industry, reducing human errors and ensuring accurate data for analysis. Immediate response to deviations increases efficiency, and operators focus on higher-value tasks.

Data science facilitates the integration of systems and the creation of a robust history, essential for identifying patterns, defining performance benchmarks, and causes of variability. In addition to collection, data science offers dynamic visualization, transforming complex data into intuitive graphs, which increases engagement and facilitates the discovery of insights to optimize processes.

The centerlining methodology allows the monitoring of several industrial variables, using as a reference both the adherence to the centerline and thresholds (predefined maximum and minimum limits). In viscous liquid production processes, equipment such as mixers, tanks, and pumps can be controlled to adjust critical variables such as dosage, temperature, and speed, ensuring product quality.

The concept of monitoring variables via centerlining methodology is applicable in several industrial sectors:

1. Chemical industry: ensures the purity of distilled products by controlling temperature and pressure.
2. Automotive industry: ensures accurate engine assembly.
3. Pharmaceutical industry, maintains accuracy in filling to avoid contamination and ensure correct dosage.
4. Beverage industry: controls fermentation, pasteurization and packaging for quality.
5. Pulp and paper industry: optimizes pulp bleaching with temperature and pressure control.
6. Textile industry: standardizes colors in dyeing through the control of temperature, time and concentration of dyes.

Centerlining and Continuous Improvement in manufacturing

The centerlining methodology, intrinsically linked to the search for operational excellence, establishes a remarkable synergy with the PDCA (Plan-Do-Act-Check) cycle, the engine of continuous improvement in manufacturing. Its four phases mirror the primary objectives of centerlining:

1. Planning defines the optimal configurations of the production parameters, erecting the reference centerline.
2. The Execution (Do) stage materializes these parameters in the manufacturing operation.
3. Check plays a crucial role in monitoring the consistency of parameters, identifying deviations from the centerline in real time.
4. The Action in centerlining translates into precise adjustments to mitigate inconsistencies, ensuring process stability within established limits.

Although complementary, centerlining directs its focus to the stabilization and strict control of critical variables of the production process, while PDCA boasts a broader scope, including permeating organizational management.

The effectiveness of centerlining is exponentially amplified by the implementation of robust technological solutions, capable of collecting historical and real-time data with high accuracy. The application of intelligent algorithms on this vast volume of data ensures the integrity and quality of the information, allowing predictive analysis and proactive optimization of production parameters, consolidating centerlining as a fundamental pillar in the continuous journey of improvement of manufacturing industries.

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