Background: In today’s complex and rapidly changing global markets, supply chain resilience (SCR) has become critical for businesses aiming to maintain continuity and competitive advantage. Disruptions and challenges in the supply chain will always exist; therefore, preparing in advance and improving resilience for the upcoming consequences should be the utmost important goal. Methods: Digital twins (DTs) provide a comprehensive view of product performance, enabling companies to adopt an end-to-end approach to product management. To maximize product and service value, supply chains must also be managed holistically. Results: Therefore, companies will need smarter strategies to balance inventory costs, availability and lead times. The optimal setup of suppliers, manufacturing, logistics and stock locations will ensure high service levels and meet customer expectations. Additionally, supply chains must be resilient, and capable of maintaining performance during disruptions and adapting to demand changes. Conclusions: This paper aims to provide a comprehensive review of the state of the art in digital twin applications within supply chains, focusing on their role in improving visibility, agility and decision-making. This paper explores how digital twins might integrate with emerging technologies such as IoT, AI and blockchain, fostering a more adaptive and robust supply chain ecosystem.

Transport studies are conducted for a better understanding of the actual mobility and for developing transport forecasting models to predict the future transport demand and the changes in travel patterns. Transport planning involves the decision-making process for potential improvements to a community’ s roadway infrastructure. The first transport models used to analyze globally the transport system requirements while nowadays models were rethought as a demand – supply interaction reflecting the correlation between transport and socio-economic development. The transport forecasting methodology use a four stage structure consisting of: trip generation, trip distribution, modal split, traffic assignment. In the second stage of the model, the generated trips for each zone are distributed to all other zones based on the choice of destination. The trip pattern is represented by means of an origin-destination (O-D) matrix. The Growth Factor Model and the Gravity Model are two methods to distribute trips among destinations. The two methods for developing the O-D Matrix are presented and criticized in this paper, showing the similarities and differences between them and highlighting the implications for rigorous determination of future transport demand. A case study is done to emphasize the differences between these models and their implications in carrying out transport studies.

Technological progress and the economic and social environment dynamics lead to changing mobility needs. Modifying travel practices, increasing the pressures for fast and predictable services, and expanding the demands of users for individualized offers determine requirements for diversifying the mobility supply in an integrated framework that must meet sustainable development objectives. In this framework, intermodal public transport plays a significant role. The chapter introduces the definitions of multimodality and intermodality in urban mobility systems. Intermodal transportation accounts for the different capabilities of diverse modes, including their availability, speed, density, costs, limitations, and, therefore, their most appropriate operating. The functions of intermodal public transportation are discussed. It is emphasized that solutions for sustainable mobility can be developed through a better understanding of the opportunities and challenges of the new mobility services.

The transport system is a powerful system with varying loads in operation coming from changes in freight and passenger traffic in different time periods. The variations are due to the specific conditions of organization and development of socio-economic activities. The causes of varying loads can be included in three groups: economic, technical and organizational. The assessing of transport demand variability leads to proper forecast and development of the transport system, knowing that the market price is determined on equilibrium between supply and demand. The reduction of transport demand variability through different technical solutions, organizational, administrative, legislative leads to an increase in the efficiency and effectiveness of transport. The paper presents a new way of assessing the future needs of transport through dynamic series. Both researchers and practitioners in transport planning can benefit from the research results. This paper aims to analyze in an original approach how a good transport forecast can lead to a better management in transport, with significant effects on transport demand full meeting in quality terms. The case study shows how dynamic series of statistics can be used to identify the size of future demand addressed to the transport system.

The significant growth of volumes and distances on road transportation led to the necessity of finding solutions to increase water transportation market share together with the handling and transfer technologies within its terminals. It is widely known that the biggest times are consumed within the transport terminals (loading/unloading/transfer) and so the necessity of constantly developing handling techniques and technologies in concordance with the goods flows size so that the total waiting time of ships within ports is reduced. Port development should be achieved by harmonizing the contradictory interests of port administration and users. Port administrators aim profit increase opposite to users that want savings by increasing consumers' surplus. The difficulty consists in the fact that the transport demand – supply equilibrium must be realised at costs and goods quantities transiting the port in order to satisfy the interests of both parties involved. This paper presents a port equipment sizing model by using queueing theory so that the sum of costs for ships waiting operations and equipment usage would be minimum. Ship operation within the port is assimilated to a mass service waiting system in which parameters are later used to determine the main costs for ships and port equipment.