COURSE OVERVIEW:

The meaning of this course lies in the transition from intuitive "trial and error" to the rigorous application of the scientific method in laboratory research. Experimental Design (DOE) is the essential discipline that allows scientists to extract the maximum amount of information from the minimum number of experiments. This program provides the mathematical and logical tools needed to structure investigations that are statistically sound, reproducible, and economically efficient.

The scope of the training focuses on the systematic planning of experiments to identify the variables that truly drive process performance. It addresses the limitations of the "One-Factor-at-a-Time" (OFAT) approach and introduces the power of Factorial Designs to reveal complex interactions between parameters. Participants will explore the entire experimental lifecycle, from the formulation of a testable hypothesis to the execution of the run-plan and the final interpretation of the response surfaces.

The coverage includes detailed instruction on screening designs, optimization techniques, and the management of experimental bias through randomization and blocking. The course emphasizes the importance of statistical power, sample size calculation, and the use of modern software for data visualization. By mastering these scientific methodologies, participants will be able to accelerate product development, optimize analytical methods, and provide robust evidence for technical decision-making.

[Image showing a comparison between OFAT and Factorial Design]

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Apply the principles of the Scientific Method to industrial problems.
• Formulate clear, testable, and falsifiable research hypotheses.
• Distinguish between independent, dependent, and nuisance variables.
• Execute Full and Fractional Factorial Designs for multi-variable testing.
• Identify and quantify interactions between experimental factors.
• Calculate the required sample size and statistical power of a test.
• Implement "Randomization" and "Blocking" to eliminate systemic bias.
• Utilize Response Surface Methodology (RSM) for process optimization.
• Analyze experimental data using ANOVA and regression techniques.
• Interpret "Main Effects" and "Interaction Plots" accurately.
• Design "Screening Experiments" to identify critical process drivers.
• Document experimental protocols to satisfy Peer Review and IP standards.

TARGET AUDIENCE:

This course is intended for Research Scientists, Process Engineers, Method Development Chemists, and Quality Improvement Specialists.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.