Author(s)

Boyu Liu ¹, Weiran Zhang ¹, Yi Qin ¹

Affiliation(s)

¹ School of Economics and Management, Yanbian University, Hunchun, 133300, China

Corresponding Author

Boyu Liu

ABSTRACT

In the fluctuating market demand for fresh supermarket logistics, this study introduces an innovative methodology combining Spearman correlation analysis, K-means clustering, and PSO particle swarm optimization to enhance the accuracy of BP neural network predictions for sales profits. Aimed at improving the procurement process for vegetables and fresh products, our analysis reveals effective strategies for supermarkets to increase profit margins, reduce product losses, and enhance customer service quality. These strategies offer supermarkets guidance on making wiser replenishment and pricing decisions in complex market environments, maintaining a competitive edge. The application of the PSO-optimized BP neural network not only improves profit predictions but also provides flexibility in pricing and stocking decisions, allowing for rapid market adaptation and efficient inventory management. This research provides a powerful tool and theoretical support for formulating scientific strategies, helping supermarkets stay competitive in the retail industry. Future studies could explore the model's application to other product types or market conditions, extending its retail industry applicability.

KEYWORDS

Dynamic Pricing Mechanism, Automatic Replenishment System, PSO-BP Model

CITE THIS PAPER

Boyu Liu, Weiran Zhang, Yi Qin, Research on Automatic Pricing and Replenishment Decision for Vegetable Products Based on PSO-BP Model. Information Systems and Economics (2024) Vol. 5: 122-130. DOI: http://dx.doi.org/10.23977/infse.2024.050317.

REFERENCES

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