Journal of Quality Engineering and Management

Abstract Purpose: The vaccine cold supply chain, as a quality-sensitive service–operational system, plays a critical role in ensuring timely delivery, maintaining vaccine efficacy, and minimizing wastage. The occurrence of operational and disruption risks intensifies process variability, undermines system reliability, and degrades service quality. The objective of this study is to develop a quality-oriented framework for the modeling and optimization of the COVID-19 vaccine cold supply chain, with a particular emphasis on quality-related performance indicators under conditions of uncertainty.

Methodology: In this study, a novel multi-period and multi-product simulation–optimization framework is developed to support decision-making in vaccine inventory management, allocation, and distribution under operational and disruption risks. The proposed approach integrates agent-based simulation with optimization techniques. The simulations are configured based on scenarios involving transportation disruptions and vaccine supply disruptions and are benchmarked against a disruption-free case.

Findings: The results are evaluated using several key performance indicators, including the expected vaccine delivery time, service level, vaccine wastage due to vehicle failures, and financial metrics. The simulation results indicate that the disruption-free scenario achieves the highest service level (0.82) and greatest degree of performance robustness, whereas transportation disruptions result in the spoilage of 8.9 million vaccine doses, and vaccine supply disruptions lead to the lowest service level (0.76). Statistical validation using the paired sign test further confirms the significance of these differences at the 95% confidence level.

Originality/Value: The present study adopts a quality engineering perspective to analyze the vaccine supply chain as a service system sensitive to process variability and proposes a quality-driven risk management framework. The findings provide practical insights for policymakers to enhance system reliability, reduce quality-related costs, and improve the resilience of vaccine distribution systems under crisis conditions.

Abstract Dispersion is the enemy of quality and an inherent and integral part of manufactured products. Therefore, identifying critical components and their contribution to total variation is an important engineering task, and measuring it in general without the most restrictive assumptions is very complex. The present article presents a systematic approach that can identify the contribution of each component to the total variability in a complex system, in proportion to the type of mechanism of action of the components. The index introduced in this study determines the contribution of the components and can be used as a measure of the criticality of the components of a system. The application of the proposed method in this article does not require any assumptions regarding the linearity of the functional function of the components or the normality of the statistical distribution of the quality characteristics, and includes the analysis of all systems with uncorrelated components. The proposed solution can be used as a powerful tool in the analysis phase of Six Sigma and Lean Six Sigma, and with its help, it is possible to prioritize and policy the use of resources in order to reduce process dispersion. To understand the proposed method in more detail, two well-known examples in industrial engineering are described.