Amos-Based Risk Forecast of Manufacturing Supply Chain

Wan, Yao

doi:10.2507/ijsimm20-1-co3

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…These mapping relationships can be linear or nonlinear. In addition, for the MTR problem, a class of samples contains multiple different output variables, and there are often different semantic relationships between them [22]. By mining the correlation of each output object, the comprehensive prediction effect of multiple indicators can be effectively improved.…”

Section: A Analysis Of Correlation Methods Based On Multi-objective R...mentioning

confidence: 99%

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Huang,

Lian

2023

IJACSA

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With the continuous development of Internet technology, the scale of Internet data is increasing day by day, and business forecasting has become more and more important in corporate business decision-making. Therefore, to improve the accuracy of Multi Target Regression in the actual e-commerce supply chain forecasting, research through the method of constructing the labeling feature for each target is optimized, the Multi-Target Regression via Sparse Integration and Label-Specific Features algorithm is obtained, and the experimental analysis is carried out on the performance of the algorithm and the application effect in the actual e-commerce supply chain. The experimental results show that the average of Relative Root Mean Square Error value of the research algorithm and is the lowest in most datasets, with a minimum of 0.058 in the effect experiments of prediction and label-specific features; in the effect and flexibility experiments of sparse sets, the lowest average of Relative Root Mean Square Error value of the research algorithm was 0.058, and the average rank value was the smallest. In addition, the average of Relative Root Mean Square Error value of the research algorithm is the smallest under the target variable of Y2 in the Enb data, and its value is 0.075. In the actual e-commerce supply chain forecast, the research algorithm has the highest score of 0.097 points. Overall, research algorithm has a better forecasting effect and higher performance, and has better practicality in practice, and can play a better effect in actual e-commerce supply chain forecasting.

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Section: A Analysis Of Correlation Methods Based On Multi-objective R...mentioning

confidence: 99%

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Huang,

Lian

2023

IJACSA

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“…Given the above analysis, combined with the current agricultural products supply chain finance situation in Jiangsu Province, China, this study applies the structural equation model (Wang et al, 2010;Hasman, 2015;Asparouhov et al, 2018;Lefcheck, 2016) (SEM) to construct the financial risk evaluation model (this quantitative research method has been widely used in psychology, education, society, and other fields). It uses the Amos method to calculate the model (Ali et al, 2019;Wan, 2021) and defines the level of factors affecting the agricultural products' supply chain financial risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on financial risk management and control of agricultural products supply chain—A case study of Jiangsu Province of China

Yang

Tang

Kong

et al. 2022

Front. Environ. Sci.

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Agriculture has always been the focus of all countries, the rapid development of agriculture is inseparable from the strong support of finance. As a new financial model, agricultural product supply chain finance has attracted much attention. Through the analysis of the agricultural product supply chain finance model, we find that the core problem of agricultural supply chain finance is the management and control of supply chain financial risks. Based on the identification of financial risk categories of agricultural products supply chain, this paper constructs tan evaluation index system. Taking Jiangsu Province of China as the research object, this research collects relevant data of financial institutions, logistics enterprises, agricultural product production, and processing enterprises and farmers in southern and northern Jiangsu Province in 2021. The structural equation is used to establish the model. This statistical method can handle multiple potential variables at the same time, and allows observation variables and potential variables to contain measurement errors. The application of statistical methods that can handle multiple potential variables at the same time and allow observation variables and potential variables to contain measurement errors to build a model. By using the Amos calculation model, this research found that the factors affecting the financial risks of the agricultural product supply chain in Jiangsu Province from high to low are: the agricultural product pledge risks, the credit risks, the supply chain operation risks, the technical risks, the legal and policy risk. We hope this research can play a theoretical guiding role for the effective development of agricultural product supply chain finance business.

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“…Green development is an important direction of the logistics industry. However, the logistics supply chain faces extremely severe problems like pollution and energy consumption, owing to the high energy consumption, high pollution, and high emissions in the traditional logistics industry [1][2][3][4][5][6][7][8][9]. That is why the green supply chain came into being.…”

Section: Introductionmentioning

confidence: 99%

Influence Mechanism of Smart City Innovation on Green Supply Chain Network Efficiency

2023

Teh. vjesn.

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The traditional logistics industry faces increasingly prominent problems like high energy consumption, high pollution, and high emissions. The improvement of green supply chain network efficiency (GSCNE) has become the development direction of this industry. Focusing on the panel data of 225 prefectures in China during 2012-2021, this paper uses the difference in differences (DID) method to explore the influence mechanism of smart city construction on GSCNE. The results show that smart city construction can enhance GSCNE via three mediators: information and communication technology (ICT), sustainable development, and technological innovation. Finally, some managerial implications were summarized according to the research conclusions.

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Amos-Based Risk Forecast of Manufacturing Supply Chain

Cited by 8 publications

References 13 publications

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Research on financial risk management and control of agricultural products supply chain—A case study of Jiangsu Province of China

Influence Mechanism of Smart City Innovation on Green Supply Chain Network Efficiency

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