How do you apply a design of experiments (DOE) technique to optimize a process or product?

(a) Define objectives - Clearly identify what you aim to achieve with the DOE, focusing on process or product parameters. (b) Select factors & levels - Choose relevant factors (variables) & their levels that will influence the process/product. (c) Choose experimental design - Opt for a suitable design (e.g., full factorial, fractional, response surface) based on objectives & resources. (d) Conduct experiments - Execute the plan systematically, ensuring consistency & accuracy in data collection. (e) Analyse data - Use statistical tools to interpret results, identifying significant factors & interactions. (f) Optimise & validate - Apply findings to establish optimal conditions & perform validation runs to confirm reproducibility & robustness.

Defining an objective is crucial in the design of experiments to ensure the experiment is focused and effective. For instance, in the fintech domain, if a company aims to optimize a loan approval algorithm, a clear objective might be to reduce the approval time while maintaining accuracy. Without this objective, experiments may lack direction, wasting resources and time. A well-defined objective guides the selection of factors and responses, ensuring relevant data is collected. For example, testing different data processing techniques and machine learning models specifically for speed and accuracy directly addresses the objective, leading to meaningful and actionable results.

Selecting the relevant and critical factors which affects the output is next step. Here based on these factors our repetition of experiments will be defined. If 2 factors are there then 2!, if 3 then 3! etc. More number of factors equals more experiments. However it is also important to know that choosing the critical and important factors is very essential for the step.

Design of experiments (also called experiment design, experimental design, or DOE) is describes and explains the variability of information under the conditions that are supposed to represent the variability. The analysis of a DOE involves the interpretation of the data collected in the experiment to determine the effects of factors on the outcomes. ANOVA is a statistical analysis of variance that evaluates the significance of factors and their interaction on the outcomes. An ANOVA divides the total variability into components attributed to various factors and error conditions. Significant factors are those that have a significant impact on the outcome variables.