Analyzing Vaccine Manufacturing Supply Chain Disruptions for Pandemic Preparedness using Discrete-Event Simulation

TL;DR

This study develops a discrete-event simulation that integrates production processes, QA/QC activities, and raw-material procurement to analyze vaccine manufacturing disruptions under pandemic conditions. Applied to an mRNA platform with CEPI collaboration, the model identifies QA/QC capacity as the primary bottleneck and raw-material lead times as a major driver of stockouts, revealing distinct resilience patterns for acute versus chronic disruptions. A doubled QA/QC workforce yields substantial, though non-linear, gains in output (+$79.1\%$ to $279.6$ million doses), while chronic supply stress can markedly reduce throughput. The framework provides a proactive decision-support tool for policymakers and manufacturers to evaluate resilience investments and pandemic preparedness strategies before disruptions occur, with guidelines for future extension to multi-facility networks and platform-specific analyses.

Abstract

The COVID-19 pandemic exposed critical vulnerabilities in vaccine supply chains, highlighting the need for robust manufacturing for rapid pandemic response to support CEPI's 100 Days Mission. We develop a discrete-event simulation model to analyze supply chain disruptions and enables policymakers and vaccine manufacturers to quantify disruptions and assess mitigation strategies. Unlike prior studies examining components in isolation, our approach integrates production processes, quality assurance and control (QA/QC) activities, and raw material procurement to capture system-wide dynamics. A detailed mRNA case study analyzes disruption scenarios for a facility targeting 50 million doses: facility shutdowns, workforce reductions, raw material shortages, infrastructure failures, extended procurement lead times, and increased QA/QC capacity. Three main insights emerge. First, QA/QC personnel are the primary bottleneck, with utilization reaching 84.5% under normal conditions while machine utilization remains below 33%. Doubling QA/QC capacity increases annual output by 79.1%, offering greater returns than equipment investments. Second, raw material disruptions are highly detrimental, with extended lead times reducing three-year output by 19.6% and causing stockouts during 51.8% of production time. Third, the model shows differential resilience: acute disruptions (workforce shortages, shutdowns, power outages) allow recovery within 6 to 9 weeks, whereas chronic disruptions (supply delays) cause prolonged performance degradation.

Figures (16)

• Figure 1: The relationship between the different modules in the simulation model. The production processes module is the main flow of goods, while the QA/QC and raw materials modules are inputs to and outputs from this main flow.
• Figure 2: Overview of the QA and QC activity flows with personnel pools.
• Figure 3: Overview of the raw materials replenishment, QA/QC and consumption.
• Figure 4: Product flow of the mRNA case study, including the different processes, intermediate inventories and final inventory.
• Figure 5: Base case pandemic responsive metrics: (a) time to first dose, (b) time to 50 million doses, and (c) number of doses after 365 days.