In the face of ever-increasing complexity and global uncertainty, today's industries are under immense pressure to develop adaptive and resilient production models. The ability to effectively manage and respond to disruptions has become not only a competitive advantage but an operational necessity. Digital Twins enable the creation of a virtual, real-time model of physical assets and processes. Through continuous data collection, a Digital Twins accurately mirrors the state and behaviour of machines, workflows, and supply chains, allowing companies to monitor, analyse, and predict operational conditions as they evolve. This research centres on a case study within the fashion industry, exploring the practical application of Digital Twins to manage and mitigate disruptions in mixed-model production environments. This work contributes to the growing body of knowledge on Digital Twin technology, emphasizing its potential as a tool for immediate operational resilience.

DIGITAL TWIN FOR MANAGING OPERATIONAL DISRUPTIONS: A CASE STUDY IN THE FASHION INDUSTRY

Fani V.;
2025-01-01

Abstract

In the face of ever-increasing complexity and global uncertainty, today's industries are under immense pressure to develop adaptive and resilient production models. The ability to effectively manage and respond to disruptions has become not only a competitive advantage but an operational necessity. Digital Twins enable the creation of a virtual, real-time model of physical assets and processes. Through continuous data collection, a Digital Twins accurately mirrors the state and behaviour of machines, workflows, and supply chains, allowing companies to monitor, analyse, and predict operational conditions as they evolve. This research centres on a case study within the fashion industry, exploring the practical application of Digital Twins to manage and mitigate disruptions in mixed-model production environments. This work contributes to the growing body of knowledge on Digital Twin technology, emphasizing its potential as a tool for immediate operational resilience.
2025
Data-Driven Scheduling
Digital Twin
Fashion
Luxury Fashion
Optimization
Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/35241
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