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Validating Process Changes with Design of Experiments (DOE)

We’ve got a plethora of case studies showing how businesses from different industries solve problems and implement solutions with data Analysis. Take a look for ideas about how you can use data analysis to ensure excellence at your business!

Boston Scientific, one of the world’s leading developers of medical devices, is just one organization who has shared their story. A team at their Heredia, Costa Rica facility was able to assess and validate a packaging process, which resulted in a streamlined process and a cost-saving redesign of the packaging.

Below is a brief look at how they did it, but you can also take a look at the full case study at

Their Challenge

Boston Scientific Heredia evaluates its operations regularly, to maintain process efficiency and contribute to affordable healthcare by reducing costs. At this facility, one packaging engineer led an effort to streamline packaging for guidewires—which are used during procedures such as catheter placement or endoscopic diagnoses—with the introduction of a new, smaller plastic pouch.

Using smaller and different packaging materials for their guidewires would substantially reduce material costs, but the company needed to prove that the new pouches would work with their sealing process, which creates a barrier that keeps the guidewires sterile.

How Data Analysis Helped

To ensure that the Seal Strength for the smaller pouches met or exceeded standards, they evaluated the process and identified several important factors, such as the temperature of the sealing system. They then used a statistical method called Design of Experiments (DOE) to determine how each of the variables affected the quality of the pouch seal.

The DOE revealed which factors were most critical. Below is a Minitab Pareto Chart that identified the factors that significantly affect seal strength: front temperature, rear temperature, and their respective two-way interaction.

Armed with this knowledge, the team devised optimal process settings to ensure the new pouches had strong seals. To verify the effectiveness of the improved process, they used a statistical tool called capability analysis, which demonstrates whether or not a process meets specifications and can produce good results:


The analysis showed that guidewires packaged using the new, optimal process settings met, and even exceeded, the minimum seal strength requirements.

With the new pouches, Boston Scientific has saved more than $330,000. “At the end of the day,” a key team member noted, “the more money we save, the more additional savings we can pass on to the people we serve.”

For another example of how Boston Scientific uses data analysis to ensure the safety and reliability of its products, read Pulling Its Weight: Tensile Testing Challenge Speeds Regulatory Approval for Boston Scientific, a story about how the company used Minitab Statistical Software to confirm the equivalency of its catheter’s pull-wire strength to previous testing results, and eliminate the need to perform test method validation by leveraging its existing tension testing standard.

This post first appeared on Nickle Loves To, please read the originial post: here

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Validating Process Changes with Design of Experiments (DOE)


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