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 parameterARIMA 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.
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