Merchandise replenishment and pricing decisions based on time series model forecasting and log-linear analysis with split logarithms

Abstract

In this paper, in the process of researching the replenishment and pricing decisions of vegetable products, we use the Spearman model and log-linear analysis with logarithms to determine the correlation between individual vegetable products and categories, and analyze the relationship between the total amount of sales of each vegetable category and the cost-plus pricing, and then build a time-series model to fit the prediction to get the daily replenishment and pricing strategy for the coming week. Then a time series model is fitted to predict the daily replenishment and pricing strategy for the coming week. In this paper, based on the model of Spearman's correlation coefficient calculation, we set up a log-linear analysis model with logarithmic scores, and use MATLAB to calculate the correlation coefficients of each type of vegetables, and find that the total sales volume of each type of vegetables has a strong correlation with the cost-plus pricing and a high degree of fit. Then we establish ARIMA model in time series model to fit the time with the sales volume, cost and pricing of vegetables respectively, and predict the sales volume, cost- and cost-plus pricing in the coming week after passing the white noise test to get the total daily replenishment and pricing strategy.