A scenario-based framework for strategic inspection decision-making in high-volume production environments

Purpose - This study bridges the gap in quality control strategies for high-volume production by balancing the cost and effectiveness of inspection strategies. Using the cost of quality (CoQ) to manage cost and external failures (EF) to gauge effectiveness, this research introduces an innovative inspection strategy chart that serves as a decision-making tool for optimizing inspection processes. Design/methodology/approach - This paper presents a scenario-based framework designed to support strategic decision-making in inspection processes by integrating empirical data analysis with inspection strategy charts. This approach allows for a dynamic assessment and visualization of the relationship between CoQ and EF, facilitating more informed decision-making in quality management. Notably, it contrasts the traditional models with a novel approach that more accurately captures the uncertainty and correlation among key quality indicators, showcasing its potential for more refined decision-making in quality management. Findings - Application of the framework illustrates its effectiveness in offering a nuanced understanding of the cost implications and effectiveness of various quality control strategies. This facilitates enhanced strategic decision-making, optimizing inspection processes and reducing external failures in high-volume production settings. Research limitations/implications - The study focuses on a single industry case study, limiting the generalizability of findings across different high-volume production contexts. Future research could explore the framework’s applicability in other sectors and refine the model based on additional empirical data. Originality/value - The research introduces a versatile framework that navigates the unique challenges of high-volume manufacturing environments. Diverging from models optimized for low-volume settings, this approach provides a valuable tool for adapting inspection strategies to complex production demands, marking a significant contribution to quality management and control literature.