Abstract
In order to satisfy the consumers’ pursuit of diversification of goods, new retail enterprises begin to gradually produce small quantities and various kinds of products, which makes the sales data become more complex and various, and then makes the inventory management more difficult. Therefore, it is very necessary to establish an accurate demand prediction model for the sub-category stratum. In this paper, we firstly consider the effect of external macrofactors on sales, and establish a multiple linear regression model to forecast the sales of the target products. Then we consider the regularity and trendency of previous sales, comparing the fitting degree of different parameter ARIMA models, and finally establish the ARIMA (2, 2, 1) model with the best prediction effect. Finally, in the light of the fitting degree, the two models are given different weights, and a predictive model that combines multiple linear regression and ARIMA (2, 2, 1) is established. It can be shown from the results that the prediction effect of combined model is better and it can accurately predict needs for new retail goods, thereby reducing the difficulty of inventory management and improving corporate competitiveness.
Highlights
The results show that the combined model has higher prediction accuracy. (Zhang & Qiu, 2019) used the decision tree model that can effectively deal with the problem of nonlinear regression to predict the sales of gas stations, and obtained a good prediction effect. (Wang, 2019) established a forecasting model through factor analysis of the sales data of heavy trucks. (Zhang, 2020) proposed a combined model based on ARIMA time series and BP neural network to study both the linear and non-linear characteristics of dish sales data. (Yang, 2017) established multiple linear regression and BP neural network models to predict the passenger car market by analyzing relevant factors of automobile sales
On the one hand, this article builds a multiple linear regression model to research the forecast of actual price, inventory, and holiday on sales
We utilize the characteristics of the historical data tendency of the target goods, through parameter estimation and fitting degree comparison, to establish the optimal ARIMA (2, 2, 1) model
Summary
In the context of the quick increase of the Chinese commodity economy and the comprehensive popularization of Internet technology, new retail enterprises which combine the Internet technology, big data technology and logistics tech-. New retail enterprises use big data mining technology, combined with consumers’ hobbies, behaviors, habits and other aspects of user characteristics, continuously to improve the production model, further subdivide the product hierarchy, and produce more diverse, beautiful and fashionable target products to satisfy the diverse, fashionable, and personalized demand of consumers. Combing with the above-mentioned literature, we consider the external factors influencing the sales volume of the target product, and the influence of historical data and holiday factors on the product, and establish a combined forecasting model based on multiple linear regression and ARIMA (2, 2, 1) model, to accurately predict the future sales of retail products
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