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Using Support Vector Machine Method to Improve Company Performance Management

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초록

Purpose: To explore the application prospect of support vector machine (SVM) in supply chain management and its practical application in supply chain performance evaluation practice. Research design, data and methodology: This paper establishes the performance evaluation index system of supply chain management according to the balanced scorecard (BSC) theory, and establishes the SVM model of supply chain management performance evaluation based on the SVM principle. Results: The performance evaluation results of the supply chain of an electric power equipment Co., Ltd. in Harbin established by using the model are consistent with the actual situation, which indicates the nature and accuracy of the possible reflection of the established supply chain performance evaluation model. Conclusions: The results show that SVM model can be used to evaluate enterprise supply chain management performance indicators, and can improve enterprise supply chain management performance, thus demonstrating the effectiveness of the model.

Abstract

Purpose: To explore the application prospect of support vector machine (SVM) in supply chain management and its practical application in supply chain performance evaluation practice. Research design, data and methodology: This paper establishes the performance evaluation index system of supply chain management according to the balanced scorecard (BSC) theory, and establishes the SVM model of supply chain management performance evaluation based on the SVM principle. Results: The performance evaluation results of the supply chain of an electric power equipment Co., Ltd. in Harbin established by using the model are consistent with the actual situation, which indicates the nature and accuracy of the possible reflection of the established supply chain performance evaluation model. Conclusions: The results show that SVM model can be used to evaluate enterprise supply chain management performance indicators, and can improve enterprise supply chain management performance, thus demonstrating the effectiveness of the model.

참고문헌 (8)

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  2. AlBadani, B., "A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM", Applied System Innovation, 5(1), pp.13~ (2022)[DOI]
  3. Chauhan, V. K., "Problem formulations and solvers in linear SVM : a review", Artificial Intelligence Review, 52(2), pp.803~855 (2018)[DOI]
  4. Hong, L., "Blockchain performance in supply chain management : application in blockchain integration companies", Industrial Management & Data Systems (2021)
  5. Mustafa Abdullah, D., "Machine Learning Applications based on SVM Classification A Review", Qubahan Academic Journal, 1(2), pp.81~90 (2021)[DOI]
  6. Anurag Rana, "A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm", Materials Today: Proceedings, 56, pp.2025~2030 (2022), Elsevier BV[DOI]
  7. Yao, G., "A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain", Expert Systems with Applications, 200(117002), pp.117002~ (2022)
  8. Zhao, J., "Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network", Neural Computing and Applications, 34(15), pp.12467~12478 (2022)[DOI]

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