Granting analysis of the credit financing of the platform-type highway transportation supply chain
Ontology highlight
ABSTRACT:
SUBMITTER: Xu L
PROVIDER: S-EPMC11236115 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:IntroductionThe rise of live-stream selling has made the e-commerce platform attractive to many small and medium-sized retailers that are often faced with capital constraints. The choice between the e-commerce platform financing (EPF) and trade credit financing (TCF) for the capital-constrained e-retailers engaging in live-stream selling is particularly important problem.MethodsThis paper considers a supply chain made up of a manufacturer, an e-commerce platform that offers live-stream selling service to consumers and an online retailer with capital constraint. We, respectively, investigate the optimal decisions of the supply chain enterprises under EPF and TCF modes based on Stackelberg game models and optimization theories.ResultsWe compare the profits of supply chain firms under different cases and obtain some important conclusions through theoretical and numerical analysis.DiscussionFirst, when the e-commerce platform's commission rate is low enough, the retailer's ordering quantity is, under EPF mode, greater than that evidenced without capital constraint. In addition, when the retailer's marginal profit is high and the e-commerce platform's commission rate is low, the online retailer should choose EPF mode; in other instances, TCF is its optimal choice. Second, the e-commerce platform can obtain the highest profit under EPF mode, while TCF mode will bring the highest profit to the manufacturer. Third, when the platform's commission rate is below a certain threshold, the profit of the entire supply chain under EPF mode is larger than that of well-funded supply chain, but TCF mode cannot. Finally, we also find there exists the access threshold about the live-stream selling. Only when the commission rate is relatively high, the e-commerce platform should offers live-stream service to consumers and the live-stream investment is the highest under EPF mode.
| S-EPMC9871903 | biostudies-literature
Project description:Assessment of associated credit risk in the supply chain is a challenge in current credit risk management practices. This paper proposes a new approach for assessing associated credit risk in the supply chain based on graph theory and fuzzy preference theory. First, we classified the credit risk of firms in the supply chain into two types, namely firms’ “own credit risk” and “credit risk contagion”; second, we designed a system of indicators for assessing the credit risks of firms in the supply chain and used fuzzy preference relations to obtain the fuzzy comparison judgment matrix of credit risk assessment indicators, on which basis we constructed the basic model for assessing the own credit risk of firms in the supply chain; third, we established a derivative model for assessing credit risk contagion. On this basis, we carried out a comprehensive assessment of the credit risk of firms in the supply chain by combining the two assessment results, revealing the contagion effect of associated credit risk in the supply chain based on trade credit risk contagion (TCRC). The case study shows that the credit risk assessment method proposed in this paper enables banks to accurately identify the credit risk status of firms in the supply chain, which helps curb the accumulation and outbreak of systemic financial risks.
| S-EPMC9934404 | biostudies-literature
Project description:To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China's commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.
| S-EPMC8782329 | biostudies-literature
Project description:Impacts of COVID-19 in maritime transportation and its related policy measures have been investigated by more and more organizations and researchers across the world. This paper aims to examine the impacts of COVID-19 on seaport transportation and the maritime supply chain field and its related issues in India. Secondary data are used to analyze the performance indicators of major seaports in India before and during the COVID-19 crisis. We further explore and discuss the expert's views about the impact, preparedness, response, and recovery aspects for the maritime-related sector in India. The results on the quantitative performance of Indian major seaports during the COVID-19 indicate a negative growth in the cargo traffic and a decrease in the number of vessel traffic compared to pre-COVID-19. The expert survey results suggest a lack of preparedness for COVID-19 and the need for developing future strategies by maritime organizations. The overall findings of the study shall assist in formulating maritime strategies by enhancing supply chain resilience and sustainable business recovery process while preparing for a post-COVID-19 crisis. The study also notes that the Covid-19 crisis is still an ongoing concern, as the government, maritime organizations, and stakeholders face towards providing vaccine and remedial treatment to infected people. Further, this study can be expanded to the global maritime supply chain business context and to conduct interdisciplinary research in marine technical fields and maritime environment to measure the impact of COVID-19.
| S-EPMC9761557 | biostudies-literature
Project description:In order to evaluate the impact of emergencies on the resilience of highway transportation, a resilience network hierarchical model of the highway transportation system was constructed by analyzing the formation and emergence process of safety resilience in the highway transportation system. Four layers of networks were divided, including highway network, transport network, traffic network, and emergency network. Combined with the network hierarchical model, a resilience evaluation index system was designed, and an assessment method for highway transportation systems based on the fuzzy analytic hierarchy process(FAHP) was proposed. Finally, a case study of a public health emergency in a region of Hunan was carried out. The results show that the proposed method for evaluating the safety resilience of highway transportation systems can better reflect the overall resilience under public health emergencies, which is consistent with the quantitative analysis results through the system resilience curve. It helps to accurately evaluate the safety resilience of the system. At the same time, this method has the advantages of flexibility and simplicity in solving unstructured decision-making problems of the system, which helps to improve the safety production management and safety resilience level of highway transportation systems. In the future, the scope of research scenarios and regions can be expanded, and further analysis of the evolution of safety resilience and the ability of resilience development in different stages under external disturbances can be conducted in order to further explore and optimize the resilience of the system.
| S-EPMC11259266 | biostudies-literature
Project description:Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.
| S-EPMC9137412 | biostudies-literature
Project description:The term "hereditary spastic paraplegia" (HSP) refers to a genetically and clinically diverse group of disorders whose primary feature is progressive spasticity of the lower extremities. The condition arises because of degeneration of the longest motor and sensory axons on the spinal cord, which appear to be most sensitive to the underlying mutations. The marked genetic heterogeneity in HSP, with 20 loci chromosomally mapped and eight genes now identified, suggests that a number of defective cellular processes may be shown to result in the disease. Although previous studies have suggested a mitochondrial basis for at least one form of the disease, a mechanism common to a number of the other genes mutated in HSP has remained elusive until now. The identification of the most recent genes for the condition suggests that aberrant cellular-trafficking dynamics may be a common process responsible for the specific pattern of neurodegeneration seen in HSP.
| S-EPMC385081 | biostudies-literature
Project description:This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.
| S-EPMC10919668 | biostudies-literature
Project description: Not available
| S-EPMC10881317 | biostudies-literature
Project description:With the rapid expansion of global e-commerce, effectively managing supply chains and optimizing transportation costs has become a key challenge for businesses. This research proposed a new framework named Intelligent Supply Chain Cost Optimization (ISCCO). ISCCO integrates deep learning with advanced optimization algorithms. It focuses on minimizing transportation costs by accurately predicting customer behavior and dynamically allocating goods. ISCCO significantly enhanced supply chain efficiency by implementing an innovative customer segmentation system. This system combines autoencoders with random forests to categorize customers based on their sensitivity to discounts and likelihood of cancellations. Additionally, ISCCO optimized goods allocation using a genetic algorithm enhanced integer linear programming model. By integrating real-time demand data, ISCCO dynamically adjusts the allocation of resources to minimize transportation inefficiencies. Experimental results show that this framework increased the accuracy of user classification from 50% to 95.73%, and reduced the model loss value from 0.75 to 0.2. Furthermore, the framework significantly reduced order cancellation rates in practical applications by adjusting pre-shipment policies, thereby optimizing profits and customer satisfaction. Specifically, when the pre-shipment ratio was 25%, the optimized profit was approximately 7.5% higher than the actual profit, and the order cancellation rate was reduced from a baseline of 50.79% to 41.39%. These data confirm that the ISCCO framework enhances logistics distribution efficiency. It also improves transparency and responsiveness across the supply chain through precise data-driven decisions. This achieves maximum cost-effectiveness.
| S-EPMC11784774 | biostudies-literature