A digital twin of a manufacturing system is a digital copy of the physical manufacturing system that consists of various digital models at multiple scales and levels. Digital twins that communicate with their physical counterparts throughout their lifecycle are the basis for data-driven factories. The problem with developing digital models that form the digital twin is that they operate with large amounts of heterogeneous data. Since the models represent simplifications of the physical world, managing the heterogeneous data and linking the data with the digital twin represent a challenge. The paper proposes a five-step approach to planning data-driven digital twins of manufacturing systems and their processes. The approach guides the user from breaking down the system and the underlying building blocks of the processes into four groups. The development of a digital model includes predefined necessary parameters that allow a digital model connecting with a real manufacturing system. The connection enables the control of the real manufacturing system and allows the creation of the digital twin. Presentation and visualization of a system functioning based on the digital twin for different participants is presented in the last step. The suitability of the approach for the industrial environment is illustrated using the case study of planning the digital twin for material logistics of the manufacturing system.

Digital twin technology has proven to be a transformative tool in the development of smart factories by providing real-time data-driven simulations and virtual representations of physical assets and systems. This paper explores the integration of digital twins in the context of Industry 4.0 and sustainability, highlighting their potential to increase operational efficiency, optimize material usage and minimize waste. To demonstrate these benefits, case studies are presented in which a digital twin of a real manufacturing system was implemented. The digital twin enabled us to run different “what-if” scenarios to evaluate the improvements in the manufacturing system, efficiency and reduction of raw material consumption and waste by incorporating quality control operations at all assembly stations. Based on the analyzed results of both case studies, we demonstrated that additional quality control operations had a significant impact on the efficiency of the manufacturing system and its sustainability, as waste was significantly reduced. The proposed approach has proven to be highly effective for different types and sizes of manufacturing systems, especially those with high waste generation.

Digital twin technology has proven to be a transformative enabler for sustainable manufacturing by providing real-time virtual representations of physical assets and supply chain processes. This paper explores the integration of digital twins with agile supply chain strategies to improve the sustainability of manufacturing systems. By leveraging real-time data and advanced simulations, digital twins facilitate dynamic decision making, optimize resource utilization and reduce environmental impact. A case study is presented in which a digital twin is implemented with the aim of improving the responsiveness of agile supply chains and suggesting appropriate times for the delivery of components and the shipment of the final product, with the goal of minimizing the time components spend in warehouses. The analysis shows how digital twins improve clarity, adaptability and predictive capabilities, leading to greater efficiency and sustainability. The results show that the combination of digital twin technology and agile supply chain frameworks contributes significantly to resource optimization, emissions reduction and overall operational resilience. The proposed approach proves to be highly effective for various manufacturing environments, especially those that strive to balance efficiency and sustainability goals.