Study on the Pricing and Replenishment Strategies for Vegetable Commodities

Qinfa Zhuang, Kangbo Pei, Kaihua Wen and Fang He ()
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Qinfa Zhuang: Shenzhen Technology University, Business School
Kangbo Pei: College of Health Science and Environmental Engineering, Shenzhen Technology University
Kaihua Wen: Shenzhen Technology University, Future Technology School
Fang He: Shenzhen Technology University, College of Big Data and Internet

A chapter in Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025), 2025, pp 560-571 from Springer

Abstract: Abstract With the development of the economy and increased health awareness, the demand for vegetables continues to rise. Addressing the issue of short shelf life and price fluctuations in fresh supermarkets, this paper proposes a comprehensive optimization model aimed at optimizing inventory management, reducing food waste, and maximizing supermarket revenue through scientific pricing and replenishment strategies. Initially, correlation and time series analyses are conducted to establish a multiple linear regression model and a Seasonal Autoregressive Integrated Moving Average forecasting model. Subsequently, profitability is assessed using profit contribution rates, and profitable vegetable products are selected. Finally, optimal replenishment and pricing strategies are proposed using linear programming. Rational pricing and replenishment strategies not only enhance supermarket operational efficiency but also contribute to the sustainable development of the vegetable supply chain.

Keywords: Seasonal Autoregressive Integrated Moving Average; Multiple Linear Regression; Correlation Analysis; Time Series Analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-770-0_63

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