Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers’ reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the “US Stores Sales” dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party’s profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses’ marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer’s profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies.
The challenge of providing students with practical, hands-on experience in realistic industrial environments is increasingly prevalent in modern technical education. The concept of a Learning Factory addresses this issue by facilitating skill acquisition through immersive, practice-oriented training that integrates advanced digital technologies. An innovative educational platform has been developed, incorporating Internet of Things (IoT) devices, Cyber-Physical Systems (CPS), and Digital Twin (DT) technology to enhance manufacturing education. This platform combines modular hardware and software, enabling immersive visualisation and real-time monitoring through DT-supported systems. These features offer a comprehensive, interactive learning experience that closely simulates real-world manufacturing processes. The system's smart reconfigurability further enhances its educational potential by enabling customisable training scenarios tailored to specific learning outcomes. The proposed approach aligns with the principles of Industry 4.0 and serves as a catalyst for the improvement of both educational and professional training environments. By leveraging digitalisation, this platform not only supports adaptive learning but also enhances the efficiency of educational models. Through the simulation of dynamic manufacturing systems, students are exposed to a variety of industrial scenarios, fostering deeper understanding and skill development. The integration of IoT, CPS, and DT technologies is expected to provide a scalable framework for future educational environments, ultimately improving the adaptability and effectiveness of manufacturing training.
The shear connection behaviour of steel-concrete composite beams is primarily governed by the strength of the connectors and concrete. Modern seismic evaluations and vibrational analyses of composite beams, particularly concerning their load-slip characteristics and shear strength, predominantly rely on push-out test data. In this study, the Finite Element Method (FEM) has been employed to simulate and analyse the shear, bending, and deflection responses of composite beams subjected to various load conditions, in accordance with Eurocode 4 standards. Failure modes, ultimate loads, and sectional capacities were examined in detail. The results indicate that increased strength of both steel and concrete significantly enhances the beam’s capacity in bending. Specifically, flexural and compressive resistance showed marginal improvements of 3.2%, 3.1%, and 3.0%, respectively, as concrete strength increased from 25 N/mm² to 30, 35, and 40 N/mm², while steel strength increased by 27% and 21%, with yield strengths of 275 N/mm², 355 N/mm², and 460 N/mm², respectively. Under seismic loading, however, the ultimate flexural load capacity exhibited a reduction with a fixed beam span, irrespective of steel strength. The shear capacity remained constant across varying beam lengths but demonstrated significant improvements with increased steel yield strength, with enhancements of 29% and 67% as steel yield strength increased from 275 N/mm² to 355 N/mm² and 460 N/mm², respectively. A detailed vibration analysis was also conducted to investigate the dynamic behaviour of these composite beams under seismic conditions. These findings underscore the critical influence of material strengths and loading conditions on the performance of steel-concrete composite beams, particularly in seismic scenarios, providing valuable insights for the design and assessment of such structures in seismic-prone regions.
The strategic location of emergency supply depots is critical for enhancing pre-disaster preparedness and post-disaster relief efforts. Given the inherent uncertainties and risks associated with natural and man-made disasters, ensuring the swift and effective delivery of relief materials to affected areas is pivotal for minimizing disaster impacts and safeguarding lives and property. This review synthesizes the current body of research on the siting of emergency stockpiles, providing a comprehensive analysis of the factors influencing site selection. Key factors such as the geographic scope of disaster response, hydrographic conditions, transportation infrastructure, and accessibility to affected populations are examined. Various siting models are evaluated to optimize resource allocation, minimize logistics costs, and improve supply chain responsiveness during emergencies. This review also identifies key challenges within the existing literature, including limitations in model algorithms, disaster stage considerations, optimization criteria, and the degree of stakeholder involvement in decision-making. Notably, while previous research has often focused on isolated factors, this study emphasizes the need for an integrated approach that accounts for dynamic, diversified, intelligent, and human-centered considerations. Dynamic models are essential to adapt to the unpredictable nature of disasters, while diversified approaches are necessary to address the varying needs of different disaster types and affected populations. Intelligent decision-making tools, incorporating data analytics and real-time information, can enhance the efficiency and accuracy of site selection processes. Human-centric models, focusing on the actual needs of disaster-affected communities, are critical for ensuring the effectiveness of relief operations. The review concludes by outlining future research directions, emphasizing the importance of developing adaptable, sustainable, and context-specific siting models. Future investigations should focus on the practical application of emerging technologies, such as big data analytics, artificial intelligence, and remote sensing, to refine siting models and improve their responsiveness in a rapidly changing global landscape. These advancements are expected to contribute to more efficient and cost-effective emergency supply systems, better equipped to address the evolving challenges of global disaster risks.
Risk management in the transportation of dangerous goods is critical for safeguarding human health, the environment, and infrastructure. This study explores systematic methodologies for risk assessment in the context of hazardous materials transit, with a particular focus on the transport of bitumen in Hungary. Key techniques, including Failure Mode and Effect Analysis (FMEA), Hazard and Operability Analysis (HAZOP), and Bow-Tie Analysis, are employed to identify, evaluate, and prioritize risks associated with the transportation process. These approaches enable the systematic breakdown of potential failure points, the evaluation of their effects, and the identification of mitigation strategies. The case study on bitumen transport highlights several significant risk factors, including operational failures, human errors, and vehicle-related incidents. The analysis reveals the importance of robust safety measures, such as enhanced driver training, real-time monitoring systems, and comprehensive documentation protocols, in reducing the likelihood and impact of such incidents. Furthermore, the study advocates for the continuous improvement of risk assessment procedures, emphasizing the need for adaptation to evolving regulatory standards and emerging challenges in hazardous materials transport. The findings underscore the importance of a proactive safety culture that integrates both technical solutions and organizational practices, ensuring a comprehensive approach to risk management in the transport of dangerous goods (TDG).
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