From Uncertainty to Precision: Advancing Industrial Rework Rate Analysis with Fuzzy Logic

Abstract

The industrial sector plays a crucial role in the global economy by providing products to meet the ever-evolving societal needs. However, the relentless pursuit of quality and efficiency faces challenges, with one of the most significant obstacles being rework. Accordingly, this paper presents the development of a rework rate index in the industrial sector, using Mamdani-type fuzzy logic as the methodology, aiming to overcome the limitations of traditional approaches and capture the complexity and uncertainty of rework data. Seventeen indicators grouped into different categories were analyzed by assessing correlations to obtain the proposed index. The results demonstrated the effectiveness of the Mamdani fuzzy logic approach in evaluating rework rates, offering comprehensive insights and clear categorizations. The analysis of correlations among indicators revealed intricate interdependencies influencing rework rates. The creation of the Rework Reduction Index signifies a significant advancement in quality and efficiency management within the industrial sector. The fuzzy approach provides a comprehensive means to address data uncertainty and subjectivity, enabling a precise and contextual evaluation of rework rates. The results have direct implications for informed decision-making, allowing companies to identify problematic areas, allocate resources efficiently, and monitor progress over time. Furthermore, the proposed approach has the potential to inspire similar practices in other companies, contributing to enhancing efficiency and quality in industrial processes Future studies could extend the application of the Rework Reduction Index to various industrial sectors and explore the relationship between index classifications and traditional performance metrics.