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<p>The textile industry faces growing challenges in improving operational efficiency while reducing the environmental impacts of resource-intensive processes such as dyeing and finishing. Digital Twin (DT) technology offers significant potential to support data-driven optimization and the transition toward circular economy practices. This study proposes a textile-specific Digital Twin framework that systematically adapts generic DT principles to the distinctive characteristics of textile manufacturing, including batch-based processes and high water and energy consumption. The framework follows a structured, five-phase implementation methodology integrating IoT-enabled data acquisition, real-time simulation, predictive analytics, and circular economy tools such as Life Cycle Assessment and Digital Product Passports. Its practical applicability is demonstrated through a realistic application scenario in a medium-sized linen textile plant in Southern Italy, highlighting potential reductions in energy and water consumption alongside improved traceability and environmental performance. The proposed approach contributes a process-oriented and sustainability-driven DT framework that supports both operational excellence and circular economy objectives in textile manufacturing.</p>

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A digital twin framework for circular economy and operational excellence in textile manufacturing

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Anaiz Gul Fareed,
  • Antonella Petrillo,
  • Fabio De Felice

摘要

The textile industry faces growing challenges in improving operational efficiency while reducing the environmental impacts of resource-intensive processes such as dyeing and finishing. Digital Twin (DT) technology offers significant potential to support data-driven optimization and the transition toward circular economy practices. This study proposes a textile-specific Digital Twin framework that systematically adapts generic DT principles to the distinctive characteristics of textile manufacturing, including batch-based processes and high water and energy consumption. The framework follows a structured, five-phase implementation methodology integrating IoT-enabled data acquisition, real-time simulation, predictive analytics, and circular economy tools such as Life Cycle Assessment and Digital Product Passports. Its practical applicability is demonstrated through a realistic application scenario in a medium-sized linen textile plant in Southern Italy, highlighting potential reductions in energy and water consumption alongside improved traceability and environmental performance. The proposed approach contributes a process-oriented and sustainability-driven DT framework that supports both operational excellence and circular economy objectives in textile manufacturing.