Forecasting Daily Replenishment Volume and Pricing Strategies for Vegetables Based on Time Series Models

Ziliang Yin; Yuxin Sang; Hongsu Li

doi:10.54097/nt89qj57

Authors

Ziliang Yin
Yuxin Sang
Hongsu Li

DOI:

https://doi.org/10.54097/nt89qj57

Keywords:

Descriptive Statistical Analysis, Quantitative Relationship, ARIMA Model, Autocorrelation Analysis, Time Series Analysis.

Abstract

Against the backdrop of today's rapid socio-economic development, retail supermarkets are faced with increasingly complex sales management challenges, especially in the area of vegetable sales. As a seasonal and perishable commodity, the accurate forecasting of vegetable sales volume and the effective formulation of pricing strategies are crucial to the operational efficiency of retail supermarkets. The relationship between total daily sales and cost-plus pricing is analyzed by quantifying the total daily sales, average daily sales price per unit, the degree of daily sales discounts, and the daily sales shipping loss metrics for each category. The total sales and total pricing data of each vegetable category from July 1 to 7 in each of the past years were screened to forecast the total sales and pricing from July 1 to 7 in the year 2023 by using the ARIMA model in the time series analysis method. Based on the forecast results, the replenishment volume and pricing strategy for the day are adjusted to achieve the goal of maximizing revenue.

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