Analyzing Factors Affecting Manufacturing Defects in the Industry 4.0 Era Using the Fuzzy Analytic Hierarchy Process: A Case Study of Chang Shin Vietnam
DOI:
https://doi.org/10.24425/mper.2026.1320Abstract
Manufacturing defects pose significant challenges to product quality, operational efficiency, and
customer satisfaction across various industries. Effectively identifying and mitigating these defects
requires a robust understanding of their underlying causes and the complex interplay among contributing
factors. This study addresses this critical need by thoroughly investigating the factors influencing
manufacturing defects through the application of the Fuzzy Analytic Hierarchy Process (AHP). This study
presents a focused case study on Chang Shin Vietnam, a prominent manufacturing entity, to provide a realworld
context for our analysis. The primary objective of this research is to systematically identify and
prioritize the root causes of manufacturing defects within Chang Shin Vietnam's operational framework.
By leveraging the unique capabilities of the Fuzzy AHP method, which excels in handling vagueness and
subjectivity inherent in expert judgments, this study illuminates the intricate relationships and hierarchical
structure among various factors contributing to defects in the manufacturing process. This approach allows
for a more nuanced and accurate assessment compared to traditional methods. The insights garnered from
this comprehensive analysis go beyond mere identification, providing a clear prioritization of the most
impactful factors. Ultimately, the robust findings of this research offer invaluable guidance for developing
highly targeted and effective strategies to mitigate manufacturing defects, thereby enhancing overall
product quality, reducing waste, and improving operational performance not only for Chang Shin Vietnam
but also for similar industrial settings facing comparable quality challenges. This research contributes to
the body of knowledge by demonstrating the practical utility of Fuzzy AHP in addressing complex
industrial quality problems.
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