Risk Assessment of Supplier R&D Investment Based on Improved BP Neural Network

Yinghua Song, Xiaoyan Sang (), Zhe Wang () and Hongqian Xu
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Yinghua Song: China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Xiaoyan Sang: China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Zhe Wang: China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Hongqian Xu: China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2025, vol. 13, issue 13, 1-20

Abstract: As market competition intensifies, the survival and development of suppliers increasingly rely on research and development (R&D) investment and innovation. Due to the uncertainty of factors affecting supplier R&D investment, the risks faced by supplier R&D investment are also uncertain. Therefore, identifying and assessing risks in advance and controlling risks can provide effective support for suppliers to carry out risk management of R&D investment. This paper selects key factors through literature review and factor analysis, and establishes a risk index evaluation system for R&D investment of medical material suppliers. Seventeen indicators that affect and constrain project investment factors were identified as input variables of the back propagation (BP) neural network, the comprehensive score of the R&D investment risk assessment was used as the output variable of medical supplies suppliers, and a risk assessment model for the R&D investment of medical material suppliers was established. By leveraging the ability of particle swarm optimization (PSO), whale optimization algorithm (WOA), and genetic algorithm (GA) to search for global optimal solutions, the BP neural network is improved to avoid becoming trapped in local optimal solutions and enhance the model’s generalization ability. The improvement in accuracy and convergence speed of these three methods is compared and analyzed. The results show that the BP neural network improved by the genetic algorithm has better accuracy and faster convergence speed in predicting and assessing risks. This indicates that the BP neural network model improved by genetic algorithm is effective and feasible for predicting the risk assessment of the R&D investment of medical supplies suppliers.

Keywords: cooperative R&D; risk assessment; BP neural network; particle swarm optimization; whale optimization algorithm; genetic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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