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Improving Product Quality Using Six Sigma Methodologies | ALPHA Technologies

How to Improve Product Quality Using Six Sigma Practices

By Richard Hanzlik, Product Manager

Rubber processing has come a long way since its beginnings in the 1820s. Modern manufacturing methods offer greater control over quality and consistency than was possible before. However, variations in the process can still have a significant impact on the quality of rubber products when left unmanaged.

W. Edwards Deming, an American business theorist and industrial engineer, famously said, “Quality comes not from inspection, but from improvement of the production process.” One of the most powerful process improvement tools in history is the Six Sigma method.

What is Six Sigma?

The Six Sigma model is a quality management methodology. Six Sigma is not simply a set of rules to follow. The Six Sigma method encourages close knowledge of every step of your processes, no matter how small, and constantly evaluating them for improvement opportunities.

According to Six Sigma philosophy, catching faults at the end of the line is not enough. True quality requires building a process so robust and well-tuned that defects become a rarity and deploying systems that persistently reduce variation in the production process. This is a proactive practice, a shift from reactive quality checks to an integrated, holistic approach to quality.

What is DMAIC?

DMAIC stands for Define, Measure, Analyze, Improve, and Control. The DMAIC process is a core Six Sigma tool. Like Six Sigma, DMAIC is not a mere checklist. Every step of the DMAIC process plays a vital role in improving quality control in rubber processing and builds on its predecessor.


The first step is creating a clear and precise definition of the system to be improved and the key measures to be addressed. This means pinpointing specific areas in the production line that need efficiency, quality, or sustainability improvements.


Appropriate measurement tools are deployed to capture comprehensive data from the system being examined. Precision and sensitivity are key at this stage, since minute differences may become extremely important at later stages of the DMAIC process. In the context of rubber processing, this may be a rubber process analyzer (RPA).


Experts use statistical methods to study the measurement data. Root cause analysis and design of experiments (DoE) may be conducted if the findings call for it.


Action steps at this stage may range from fine-tuning—such as modifying machining settings or altering material compositions—to redesigning certain aspects of the production process entirely to enhance overall quality and efficiency.


New process control methods are deployed. Statistical control charts of key process outputs are used to monitor production and ensure that newly set standards for excellence and quality are being met.

Sources of Variation when Employing Six Sigma

The Six Sigma method asserts that variation in system output is caused by variation in system inputs. These variations can be traced back to a number of sources, some of which may be easy to overlook. Variations in input can stem from:

  • Human operators: Skill level, attention to detail, fatigue
  • Machines: Condition, precision, calibration
  • Measurement: Instrument sensitivity, accuracy
  • Environment: Ambient temperature, ambient humidity
  • Materials: Quality, consistency, interactions

None of these variables exist in a vacuum. A variation in one category can lead to variation in another, compounding the effect on final product quality. A good illustration of the complex relationship between these factors is an Ishikawa diagram, or “fishbone diagram,” created by Japanese organizational theorist Kaoru Ishikawa.

The Ishikawa diagram is a valuable tool for troubleshooting output issues and identifying potential sources of unwanted variation. 

How Improved Measuring Systems Support Rubber Product Quality

A common challenge in the rubber industry is a lack of sufficient tools for detecting small, yet impactful, variations. Traditional testing instruments, such as a Mooney viscometer or moving die rheometer (MDR), may not be sensitive enough to detect and diagnose the cause of bubbles, shark skin, non-fill, poor dimensional control, and other fine variations.

Case Study: Detecting Minute Variations in Rubber Lots

Alpha Technologies was approached by a client who needed help identifying the cause of inconsistent processing. The client reported very different processing outcomes from two seemingly identical batches of nitrile rubber. Specifically, they were observing flow issues that resulted in improper mold fill and, ultimately, scrapped parts.

Samples from both batches were subjected to high torque (MH) and low torque (ML) testing on an MDR, which failed to reveal any differences between the two batches. Tensile strength and modulus testing did not detect any variations, either. The issue called for a more sensitive instrument with sufficient signal to noise ratio that could detect subtle inconsistencies: A rubber process analyzer (RPA).

Alpha Technologies collaborated with the Rubber Manufacturers Association (RMA) to develop a test method to find the difference between the two lots of nitrile rubber. Set at 100% strain, the rubber process analyzer (RPA) revealed that one of the two lots had higher molecular weight, which was causing the flow issues. This distinction, which other instruments could not detect, empowered the client to understand why flow issues were occurring and make confident decisions about suppliers going forward.

How the Rubber Process Analyzer (RPA) supports Six Sigma Methodology

The DMAIC process of Six Sigma methodology is designed to foster continuous improvement and enhanced quality in rubber processing. As demonstrated by the above case study, the RPA is an exceptionally valuable tool for detecting minute process variations. The RPA can also be used for predictive quality control. With an RPA, rubber processors can detect and resolve potential issues well before they manifest as defects in the final product and become large-scale problems, potentially saving time, money, and hassle. As a result, rubber processors can maintain a competitive edge in an increasingly demanding and quality-conscious market.

Advanced analytics will undoubtedly play a critical role in the future of quality control in rubber processing. Sophisticated instruments like the RPA and methodologies like Six Sigma are enabling an unprecedented degree of precision, efficiency, and reliability in the industry.

To learn more about the Premier RPA from Alpha Technologies, talk to a member of our team.

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