Sustainable logistics has become a cornerstone of modern manufacturing, with companies seeking to minimise energy use, CO₂ emissions, and resource inefficiencies throughout the supply chain. This manuscript examines component delivery as a critical aspect of inbound logistics and introduces a digital twin framework to improve its organisation, transparency, and sustainability. The proposed model enables simulation of various delivery scenarios to assess vehicle capacity, fuel consumption, delivery frequency, and warehouse utilisation, providing a comprehensive decision-support tool for balancing economic and environmental objectives.

The digital twin integrates real production data, customer orders, and logistical parameters across various transport vehicles (van, lorry, and lorry with trailer) to generate real-time, data-driven insights (the data are refreshed after each completed shift). The methodology for developing the digital twin uses a previously developed five-stage procedure with integrated algorithms for automatic recognition of the transport vehicle type, automatic detection of the production part type, automatic control of delivered parts, etc. This enables users to perform detailed “what-if” analyses without disrupting actual operations, identifying the most efficient and sustainable delivery configurations. Results from simulated test cases show that larger transport vehicles reduce total fuel consumption by more than 50% and consequently also reduce emissions (Tank-to-Wheel), but increase temporary storage requirements and external warehouse costs. Conversely, smaller vehicles offer greater delivery flexibility but result in higher energy intensity and transport frequency. Validation and verification of the digital twin were conducted using a diverse set of test data designed to reflect typical operational variations and quantity fluctuations, enabling a reliable assessment of the digital twin’s accuracy and robustness for the manuscript.

The findings show that digital twins can be practical enablers of the green and digital transition in manufacturing, linking operational excellence with ecological responsibility.