The power of Six Sigma methodology in diagnostic development

23 May 2023 4min read

Six Sigma methodology is a problem solving framework that focuses on understanding and reducing variation, to improve the performance of new and existing products and processes. Widely adopted across a range of businesses spanning multiple industries, it features a general framework to assist with structuring improvement projects alongside a toolkit of more specific techniques that sit under the Six Sigma banner.

Used correctly, it can be a powerful methodology to fast track your diagnostic development, using statistical tools to evaluate and optimise the performance and reliability of your product. It is particularly useful for the optimisation of complex systems with multiple design and user variables, making it an ideal tool to use when developing modern diagnostics systems.

An effective framework for problem solving

Much of Six Sigma is about understanding system variation and how your device design decisions will impact the overall function of the product. It is a powerful problem solving tool, helping to promote a systematic and data-driven approach to quantify any performance gaps in your device, determine the root cause of any issues and develop robust solutions.

Coding and diagnostic devices

What is Six Sigma methodology?

The DMAIC Six Sigma framework that underpins the methodology divides a project into five key stages – Define, Measure, Analyse, Improve and Control.

table of five stages of six sigma methodology 2

Statistical analysis tools

There is no standard set of tools under the Six Sigma banner and, depending on who you ask, the contents of the toolkit can vary. Many of the tools are also not unique to this methodology. However, there is a subset of core tools that utilise statistics to understand and work with variation that are the key to why this methodology can be so valuable in medical device and diagnostic development. Three examples of such tools include design of experiments, Gauge R&R and Monte Carlo analyses.

1. Design of experiments

This is a tool that allows the relationship between process inputs and system performance to be established quickly and efficiently in complex systems. When implemented as a screening test, it can be used to pinpoint the key variables that are responsible for affecting the system outputs, allowing the project team to quickly narrow their focus. A more in-depth set of tests can then be conducted to allow the construction of a mathematical model of the system, enabling the key sensitivities to be quantified and the system optimised.

The flexibility of this approach makes it equally useful when applied to data gathering using physical and analysis-based methods such as Computational Fluid Dynamics (CFD). Applied appropriately, this tool can greatly reduce the amount of time it takes to understand and optimise complex systems. Broadly speaking, it has the potential to take months out of your development program.

Photo of man using computer for computational fluid dynamics 2

2. Gauge R&R study

This tool is all about determining the suitability of the measurement systems used to measure the performance of your device. Without having confidence in the gauges and measurement protocols used to measure your device performance, it is impossible to have confidence in the conclusions of any test results. A Gauge R&R study will help to quantify the gauge derived variability (repeatability) and operator derived variability (reproducibility) of any measurement system. By using this tool early on in a diagnostic development project, it can ensure the right measurement equipment is in place to enhance the value of all future test work and prevent the possibility of missing out on key results that could unlock the potential of your medical device.

3. Monte Carlo analyses

Monte Carlo analyses is a form of tolerance analysis for complex systems, used to predict the resultant system variation based on the anticipated manufacturing variation. The idea is to simulate the production of your system in a virtual environment where it is possible to instantly build thousands or even millions of assembles to see the magnitude and characteristics of the final system variation. The statistical probability of a falling out with your product specification limits can then be predicted, and the data used to enable optimisation of the system design.

Applying Six Sigma methodology to your diagnostic development project

Given the broad scope of the Six Sigma methodology, to achieve maximum value, it is important to select the right tools and techniques for each individual situation. This includes not only knowing what tools are available, but also having a strong understanding of the mathematics that underpins them. Using inappropriate tools and blindly accepting the results from a calculation that you don’t fully understand is not a sensible foundation to base key development decisions on. In the right hands however, Six Sigma is a powerful tool to progress your diagnostic development.

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