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Neural Computing and Applications

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06.10.2022 | Original Article

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

verfasst von: R. Rathipriya, Abdul Aziz Abdul Rahman, S. Dhamodharavadhani, Abdelrhman Meero, G. Yoganandan

Erschienen in: Neural Computing and Applications | Ausgabe 2/2023

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Abstract

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.

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Titel: Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model
verfasst von: R. Rathipriya
Abdul Aziz Abdul Rahman
S. Dhamodharavadhani
Abdelrhman Meero
G. Yoganandan
Publikationsdatum: 06.10.2022
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07889-9

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