Postpandemic Quality Control Challenges in Manufacturing: A Hybrid Triple Vague Fuzzy Decision-Making Approach

The COVID-19 pandemic has brought unprecedented challenges to the manufacturing sector, particularly affecting the roles and responsibilities of quality control (QC) workers. This study delves into the specific hurdles faced by QC workers during the pandemic and investigates potential strategies to navigate these obstacles in the post–COVID-19 era. Employing a triple vague structure which combines interval fuzzy, negative interval fuzzy, and hesitant fuzzy numbers in the decision matrix, further it has been calculated using the MCDM method called superiority and inferiority ranking (SIR) approach, along with the best–worst method (BWM) for criteria weighting; the research provides a comprehensive analysis of the issues faced by QC workers. The triple vague fuzzy SIR approach allows for detailed evaluation under uncertainty, offering a robust framework for prioritizing QC improvement actions. The study suggests that implementing an advanced training program can effectively mitigate many postpandemic challenges, enhancing the resilience and efficiency of QC practices. Moreover, this research advances decision-making methodologies under uncertainty and provides actionable insights for practitioners aiming to improve QC in the manufacturing industry during the postpandemic period.