ATTRACTIVEJOURNALATTRACTIVEJOURNAL

Asian Journal Science and EngineeringAsian Journal Science and Engineering

This study aims to evaluate the quality control of lightweight brick production at PT XYZ using the Fault Tree Analysis (FTA) method. In the manufacturing industry, product defects are a major challenge that can affect production efficiency, operating costs, and company competitiveness. Based on production data from January to December 2024, the total number of defects identified reached 125,334 units, consisting of three main types, namely cracks (63%), peeling (21%), and imprecision (16%). Through the application of FTA, this study revealed that the two dominant factors that are the root causes of product defects are human error and tools or equipment. Human error is mainly triggered by operator carelessness, overly rapid mold dismantling processes, and errors in installing cutting tools. Meanwhile, machine factors include worn components, excessive vibration, deteriorating cutting wire quality, and lack of regular maintenance. The results of the study emphasize the need for a comprehensive improvement strategy through increasing operator competence, enforcing work discipline, scheduled machine maintenance, and standardizing operational procedures. The implementation of these improvements is expected to reduce the defect rate and improve product quality in a sustainable manner.

The study concludes that the defect rate of lightweight brick products at PT XYZ from January to December 2024 was dominated by cracks (63%), followed by peeling (21%), and imprecision (16%), totaling 125,334 defective units.The main source of defects stemmed from a combination of human error and machine/equipment factors, leading to inconsistencies in product quality.Improving product quality requires a comprehensive strategy encompassing operator training and discipline, enhanced supervision, and a planned machine maintenance system to reduce defects and ensure consistent production quality.

Based on the findings, future research could explore the integration of real-time monitoring systems with FTA to predict and prevent defects before they occur, potentially utilizing machine learning algorithms to identify patterns indicative of impending failures. Furthermore, a comparative study analyzing the effectiveness of different maintenance strategies – such as predictive versus preventative maintenance – on reducing equipment-related defects would be valuable. Finally, investigating the psychological factors influencing operator error, such as workload and stress levels, and developing targeted interventions to improve operator focus and adherence to procedures, could significantly contribute to enhancing product quality and reducing human-induced defects.

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