Optimizing processes and products: The role of DOE

Amir Ahmad  Dar; Princy  Yadav; Aswani  N; Tashi Wangmo  A

doi:10.18282/i-s644

Amir Ahmad Dar Department of Statistics and Data Science, Lovely Professional University, Jalandhar 144411, India
Princy Yadav Department of Statistics and Data Science, Lovely Professional University, Jalandhar 144411, India
Aswani N Department of Statistics and Data Science, Lovely Professional University, Jalandhar 144411, India
Tashi Wangmo A Department of Statistics and Data Science, Lovely Professional University, Jalandhar 144411, India

Article ID: 644

99 Views, 62 PDF Downloads

DOI: https://doi.org/10.18282/i-s644

Keywords: DOE; factorial design; fractional design; Taguchi method; methodology

Abstract

The Design of Experiment (DOE) methodology is a fundamental tool for systematic inquiry and optimization in both scientific and industrial applications. The DOE’s statistical framework is designed to enhance the efficiency and reliability of experimental investigations by systematically planning, conducting, and evaluating controlled experiments. To support well-informed decision-making, process optimization, and quality improvement, the main goal of DOE is to discover and quantify the effects of various variables on a response variable. Various methods such as full factorial, fractional factorial, Taguchi, and response surface methodologies provide powerful tools for optimising processes and enhancing quality. The study covers the application of DOE in several industries, including engineering, manufacturing, agriculture, and medicine. Defect minimization, process optimization, and quality improvement are all aided by DOE in manufacturing. By determining the best dosages and formulations, it helps in drug development in the pharmaceutical industry. In the field of agriculture, DOE facilitates the identification of optimal growing conditions and techniques. It helps in the engineering and assessing new systems and products. To achieve consistency and accuracy in data collection, the experiment must be carried out with strict adherence to the experimental strategy. Analysing data involves using statistical tools to evaluate the findings and make conclusions, such as ANOVA, regression analysis, and graphical approaches. By pointing out important variables and their interactions, these studies aid in process optimization and product quality enhancement.

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