Evaluation and Prediction of Sustained Competitive Advantage of Baijiu Enterprises Based on Entropy Weight-TOPSIS and INGO-BP Neural Network

Yu Zhou; Guijuan Wang; Yadong Wu; Weihan Zhang

doi:10.54097/41aeh818

Authors

Yu Zhou
Guijuan Wang
Yadong Wu
Weihan Zhang

DOI:

https://doi.org/10.54097/41aeh818

Keywords:

Neural Network, Baijiu, Entropy Weight-TOPSIS, Competitive Advantage, INGO

Abstract

In-depth scrutiny of the sustained competitive advantage within the liquor industry is paramount for facilitating a comprehensive understanding of market positions, benefiting both enterprises and investors. This study addresses the limitations of the current model, characterized by fewer evaluation indices and incomplete data, leading to lower assessment efficiency. To enhance the objective and effective evaluation of sustained competitive advantage, financial indicators (development ability, profitability, solvency, operating ability, and cash flow) are integrated, alongside non-financial indicators (brand value, environment, and social responsibility), forming a systematic approach for assessing listed liquor companies. Authentic data is sourced from official information disclosure websites. Utilizing the sliding average of each index enhances the reflection of the sustained development advantage of enterprises. The study employs the entropy value method to calculate weighted indicators, followed by the TOPSIS method for a comprehensive evaluation of sustained competitive advantage. The obtained evaluation values are used as a priori samples for the prediction model. An INGO-BP neural network prediction model is proposed in this study. This model, optimized with a sinusoidal algorithm for the exploratory phase of the Northern Goshawk Algorithm (NGO), incorporates a nonlinear reduction strategy to expedite the convergence of the Northern Goshawk. A spiral perturbation stage is added to prevent the NGO algorithm from entering local minima. The improved NGO algorithm (INGO) is employed for BP neural network parameter optimization. Simulation experiments reveal a significant enhancement in the performance of the INGO algorithm. In empirical analysis, a comparative assessment with other models demonstrates that the evaluation and prediction model in this study more accurately reflects the sustained competitive advantage of liquor enterprises, yielding predictions of higher accuracy and enhanced stability in performance.

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