Building operational resilience: A digital twin approach in mixed-model assembly line

Disruptions in mixed-model assembly lines pose significant challenges for industries operating in high-variability environments. These challenges are particularly acute in sectors such as fashion, where SKU alternation, high customization, and dynamic production requirements complicate operational resilience. To address these complexities, this paper proposes a digital twin (DT) application framework aimed at identifying and mitigating operational disruptions. The framework was validated through a case study in the fashion industry, demonstrating its effectiveness in managing disruptions such as machine failures and quality issues. From a theoretical perspective, the study classifies disruptions within Supply Chain Operations Management (SCOM) dimensions, distinguishing between operational and supply chain levels. Furthermore, the framework contributes to the ongoing discourse on DTs by offering validated insights into their role in managing operational disruptions in mixed-model assembly lines. More broadly, it situates itself within the ongoing conversation on how Industry 4.0 technologies, such as DTs, act as enablers for the transition toward Industry 5.0. Specifically, this study underscores the role of DTs as key facilitators of resilience, a foundational pillar of Industry 5.0. From a managerial perspective, company's performance is improved through the proposed structured approach to managing disruptions. By integrating information across systems and transforming raw data into actionable insights, it enhances resilience through informed decision-making and recovery action evaluation via simulation-based optimization. This focus on resilience and optimization positions the framework as a comprehensive tool for navigating the complexities of modern production environments. Future research could expand its applicability to supply chain-level disruptions, broadening its impact across key supply chain nodes.