Torben Bygvraa Rasmussen has an educational background in applied physics and holds a Ph.D. and an M.Sc in experimental physics. In addition, he has a Six Sigma green belt degree from Motorola University.
He has more than 10 years of experience working in the medical device industry applying statistical tools such as DoE, SPC and tolerance analysis to obtain more robustness, higher yield and lower costs in production.
To accommodate the rising demand for risk and science-based approach to process validation and batch release, Torben Bygvraa Rasmussen also advises customers in statistics for e.g. the 2011 FDA guideline on process validation.
- Applied manufacturing science
- Six Sigma
- Tolerance analysis (DFSS)
- Quality by Design (QbD)
- Statistical process control (SPC)
- Design of Experiments (DoE)
- Sampling for process validation and continued process verification
What is your view on the industry?
“In the device industry Quality by Design (QbD) has been a buzzword for a long time. Still, few companies go all-in when it comes to ensuring product reliability and quality by linking the customer’s demands with the component specifications – or perhaps even with the process window for production.
Design for Six Sigma DSFF provides a strong tool to connect our customers’ demands with dimensions on single components, and dimensions on components with control parameters on the production equipment making the components.
The rising demand for science and risk-based approaches from health authorities calls for more sound rationales for e.g. product batch release. Especially in the medical device industry this has not been practiced in general. The design for the Six Sigma methodology closes this gap.”
Why do you work in this business?
“DSFF is an exciting area because I get to collaborate with customers on very different levels of maturity of both their products and level of expertise, which allows both me and the customer to evolve.
In its full extent DFSS spans across a great part of a product's lifecycle, right from concept to process monitoring. This calls for a versatile toolbox of statistical methodologies as well as competencies within e.g. GMP, product development, industrialisation or supply chain management. As NNE houses all of these competencies in-house, we most often collaborate in cross-disciplinary teams to provide customer-tailored solutions. That is very inspiring to me”