Medical Devices

Blog posts and articles about using data analysis and statistics in quality improvement initiatives in the medical devices industry.

Recently, a customer called our Technical Support team about a Design of Experiment he was performing in Minitab Statistical Software. After they helped to answer his question, the researcher pointed our team to an interesting DOE he and his colleagues conducted that involved using nasal casts to predict the drug delivery of nasal spray. The study has already been published, and you can read... Continue Reading
All processes are affected by various sources of variations over time. Products which are designed based on optimal settings, will, in reality, tend to drift away from their ideal settings during the manufacturing process. Environmental fluctuations and process variability often cause major quality problems. Focusing only on costs and performances is not enough. Sensitivity to deterioration and... Continue Reading
When I talk to quality professionals about how they use statistics, one tool they mention again and again is design of experiments, or DOE. I'd never even heard the term before I started getting involved in quality improvement efforts, but now that I've learned how it works, I wonder why I didn't learn about it sooner. If you need to find out how several factors are affecting a process outcome,... Continue Reading
Measurement systems analysis (MSA) is essential to the success of any data analysis. If you cannot rely on the tool you’re using to take measurements, then why bother collecting data to begin with? It would be like trying to lose weight while relying on a scale that doesn’t work. What’s the point in weighing yourself? Minitab Statistical Software offers many types of tools that you can use to... Continue Reading
The U.S. Food and Drug Administration implemented new regulations in the pharmaceutical industry over the past several years. One focus of the FDA has been on quality in batch-to-batch production. Batch quality not only ensures safe product for consumers, but has the double benefit of saving pharma companies money.  It's a waste of time and money to go through the long process of manufacturing a... Continue Reading
by Manikandan Jayakumar, guest blogger We use Design of Experiments (DOE) to optimize the value of a response (Y) by simultaneously changing the values of several factors (X’s). The response will often be a continuous variable, but in some scenarios you need to optimize an attribute or categorical response (Pass/Fail, Accept/Reject, etc.).  Collecting the Data for an Attribute Response DOE Let’s see... Continue Reading