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随着医药企业间的市场竞争加剧,企业销售管理的重要性逐渐凸显。医药销售预测是个复杂的非线性系统,为提高企业销售预测的准确性,本文选取医药上市企业处方药七叶皂苷钠历史销售数据,分别建立ARIMA线性模型和BP神经网络非线性模型并加以验证。证明了在销售预测上采用ARIMA-BP组合模型可以有效降低误差,为医药企业的销售管理和企业决策带来新的思路。
With the intensified market competition among pharmaceutical companies, the importance of enterprise sales management gradually highlights. Pharmaceutical sales forecast is a complex nonlinear system. To improve the accuracy of the sales forecast, this paper selects the historical sales data of the prescription drug sodium aescinate listed in the pharmaceutical listed companies to establish and validate ARIMA linear model and BP neural network nonlinear model respectively. Proved that using ARIMA-BP combination model in sales forecasting can effectively reduce the error and bring new ideas for sales management and enterprise decision-making of pharmaceutical enterprises.