Efficiency of Full and Fractional Factorial Designs in Quality Management: A Case Study on Identifying Key Adhesion Factors

The Design of Experiments (DoE) method is a fundamental tool in quality management, providing a structured approach to process optimisation, reducing variability, and identifying key factors that influence outcomes. By systematically planning, executing, and analysing experiments, DoE enables quality management professionals to enhance product performance and consistency. The aim of this study is to compare the effectiveness of full factorial and fractional factorial experiment designs in quality management, specifically in identifying statistically significant factors that impact the adhesion properties of thermoplastic polyurethane (TPU) film with an acrylic-silicone adhesive, applied to laboratory glass with standardised parameters. The methodology comprises laboratory experiments designed to determine significant variables influencing adhesion, followed by statistical analysis using Minitab software. Both experiment types revealed the same primary factor as statistically significant. However, the fractional factorial design omitted additional significant factors due to data reduction, which led to some information loss. Despite this limitation, the fractional factorial design demonstrated high efficiency in terms of resource utilisation, including time and cost savings, while maintaining essential insights with minimal loss of valuable data. The findings contribute theoretically to the field of quality management, underscoring the comparative advantages and limitations of fractional factorial designs in scenarios where resources are constrained. Practically, this study provides guidance for quality management practitioners, indicating that while fractional factorial designs are cost-effective and time-efficient, they require meticulous data analysis to ensure the completeness and accuracy of findings, especially in complex experimental settings where missing critical information may impact decision-making.