Quality by Design and Design Space - 3 days course - Switzerland
Quality by Design and Design Space
Scientific investigations involve changing a number of controlled variables to direct the response in question towards a desired level. Design of Experiments (DOE) is a rational and cost-effective approach to practical experimentation that allows the effect of variables to be assessed using only the minimum of resources. DOE is the backbone for efficient QbD implementation strategies. The final specifications for a region where all specifications are fulfilled to a defined risk level is called Design Space.The course is composed of lectures, demonstrations and computer exercises in software MODDE® based on real life investigations.
On completion, participants will know how to:
- Create efficient experimental designs to match the objectives.
- Analyze experimental data using sound statistical principles.
- Improve and optimize products and processes.
- Interpret the results to increase understanding.
- Make a risk estimate of the decided settings.
- Understand robust optimization objectives
- Set specifications for normal operations (Design Space)
- Report results in a comprehensive graphical format.
WHO SHOULD PARTICIPATE?
Intended for researchers, scientists and engineers from all sectors of industry and academia. Typical applications include product development, process improvement, optimization, validation and quality control. No prior knowledge of statistics is assumed.
SELECTED COURSE CONTENT
- Understanding the DOE concept
- Data modeling and diagnostics
- Create a solid base for decisions
- Finding the optimal setting
- Quality estimates and robustness evaluation
- Design Space specifications
Focus on how and when should Design of Experiments be used?
- Problem formulation
- Selection of goals, factors, responses, type of model and design
- Properties and analysis of Full factorial designs
- Evaluation of raw data
- Regression analysis and model interpretation
Exercises followed by discussion
Focus on screening and optimization
- Screening designs, which factors dominate and what are their optimal ranges
- What to do after screening, optimization or modification of the design
- Optimization designs, how do we find an optimum?
Exercises followed by discussion
Focus on robustness testing and Quality specifications.
- Verification that the method or process is robust within given specifications
- Use Monte Carlo simulation to identify operating regions that assure good quality product.
- Set specifications for a Design Space
- Robustness testing, discuss four different scenarios.
Exercises using participants' or Umetrics' examples.
Discussion of participants' own data including creation of new designs.
COST AND CONDITIONS
Please note that at some course locations you are required to bring your own computer for the exercises. Instructions on how to install the program will be included in the final course information.
Included in the course fee (+ VAT) is coffee, lunch and course documentation.
Cancellations received later than two weeks before the course starts will not be refunded. For courses cancelled more than two weeks before the course starts, Sartorius Stedim Data Analytics AB will retain 10% of the course fee to cover administrative costs and the rest of the amount will be refunded.
Course participant(s) can be substituted by the registering company as long as Sartorius Stedim Data Analytics AB is notified.
Sartorius Stedim Data Analytics AB are providing courses based on a sufficient number of registrants. Therefore, Sartorius Stedim Data Analytics AB reserves the right to cancel the course 14 days prior to the course start date, if the number of registrants is too low. Full refund will be made to these registrants. A 10% discount will be made to any registrant(s) enrolling in the next available course.