Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

被引：21

作者：

Rathipriya, R. ^{[1
]}

Abdul Rahman, Abdul Aziz ^{[2
]}

Dhamodharavadhani, S. ^{[1
]}

Meero, Abdelrhman ^{[2
]}

Yoganandan, G. ^{[3
]}

机构：

[1] Periyar Univ, Dept Comp Sci, Salem, India

[2] Kingdom Univ, Riffa, Bahrain

[3] Periyar Univ, Dept Management Studies, Salem, India

来源：

NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 02期

关键词：

Deep learning models; Demand forecasting; Pharmaceuticalindustry; Shallow neural network models; SUPPLY CHAINS; PREDICTION; ANN;

D O I：

10.1007/s00521-022-07889-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.

引用

页码：1945 / 1957

页数：13

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