This training is aimed at professional development, and the certificate obtained does not replace the MYK certification required for training that mandates MYK certification.

Design of Experiments (DOE) Techniques Training Program

This training aims to equip participants with the skills to design and analyze effective experiments. It enhances the ability to collect data, analyze it, and make informed decisions through statistical methods and optimized experimental processes.

1. Introduction to Experimental Design

• Definition and importance of experimental design.

• The role of DOE techniques in various industries.

• Basic principles and methods of experimental design.

• Selection of experimental variables and factors.

2. Basic Statistical Concepts and Understanding DOE

• Statistical concepts: Mean, variance, confidence intervals.

• Basic statistical analyses and tests.

• Statistical significance and p-value.

• Factorial experiments and analysis of interactions.

3. Experimental Design Methods

• Full factorial and fractional factorial experiments.

• Plackett-Burman design and Box-Behnken design.

• Response Surface Methodology (RSM).

• Self-conducted and pre-designed experiments.

4. Data Collection and Analysis in Experimental Design

• Proper data collection and organization techniques.

• Graphical and numerical analysis of experimental data.

• Regression analysis and use of ANOVA (Analysis of Variance).

• Modeling and creation of response surfaces.

5. Interpreting and Improving Experimental Results

• Proper interpretation of experimental results.

• Decision-making based on statistical test results.

• Analysis of interactions and affected variables.

• Applications of results in business and engineering.

6. Applications of Experimental Design

• Use of DOE in manufacturing and production processes.

• Clinical trials and pharmaceutical research in the healthcare sector.

• Applications of experimental design in agriculture and environmental sciences.

• Importance of DOE techniques in product development and optimization processes.

7. Advanced Experimental Design Techniques

• Multivariate experimental design and optimization.

• Mixture designs and modeling.

• Monte Carlo simulations and randomized experiments.

• Advanced regression and modeling techniques.

8. Ethics and Advanced Topics in Experimental Design

• Ethical responsibilities in experimental design.

• Data security and confidentiality.

• Reporting and presenting findings accurately.

• Transparency of research results.

9. Software and Tools for Experimental Design

• Software used for experimental design: Minitab, Design-Expert, JMP, R.

• Advantages and disadvantages of these software tools.

• Modeling experimental processes using software.

• Visualizing and interpreting experimental data.

10. Decision Making and Innovation with Experimental Design

• The importance of data-driven decision making.

• Using experimental results for product and process innovation.

• Optimization and efficiency improvement methods.

• Applications of experimental design for industrial and commercial success.

The training is open to institutional collaborations (package for institution/company legal entities), and individual applications are not accepted. The training content can be reorganized according to your institutional participant profile and needs. As a result of mutual discussions, the scope and method of the training (face-to-face / online) are determined and the relevant processes are completed. In case of mutual agreement, the date, time, and location of the training are planned in line with the availability of your institution’s participants and our instructors.