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

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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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Data availability

The datasets analysed during the current study are available on the kaggle website. These datasets were derived from the following public domain resources: https://www.kaggle.com/milanzdravkovic/pharma-sales-data-analysis-and-forecasting/data

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Funding

The authors received no specific funding for this study.

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Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, India

    R. Rathipriya & S. Dhamodharavadhani

  2. Kingdom University, Riffa, Bahrain

    Abdul Aziz Abdul Rahman & Abdelrhman Meero

  3. Department of Management Studies, Periyar University, Salem, India

    G. Yoganandan

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  1. R. Rathipriya
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  2. Abdul Aziz Abdul Rahman
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Correspondence to Abdul Aziz Abdul Rahman.

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Rathipriya, R., Abdul Rahman, A.A., Dhamodharavadhani, S. et al. Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model. Neural Comput & Applic 35, 1945–1957 (2023). https://doi.org/10.1007/s00521-022-07889-9

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