EFFECTIVE DEMAND FORECASTING IN INTERNATIONAL FLOWS DATA PHARMACEUTICAL GOODS USING AI: A CASE STUDY

Noureddine Mohtaram (), Jérôme Verny (), Elsa Corbin, Ghoul, D., Eric Lambourdière () and Jérémy Patrix ()
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Noureddine Mohtaram: NEOMA - Neoma Business School
Jérôme Verny: NEOMA - Neoma Business School
Elsa Corbin: Université des Antilles (Pôle Martinique) - UA - Université des Antilles
Ghoul, D.: NEOMA - Neoma Business School
Eric Lambourdière: Université des Antilles (Pôle Martinique) - UA - Université des Antilles
Jérémy Patrix: NEOMA - Neoma Business School

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Abstract: Pharmaceutical products are subject to a high degree of demand variation in the industrial market. We have seen that the global spread of the COVID-19 pandemic has had a devastating impact on such supply chains. In this paper, we describe the integrated procedure for market demand forecasting and purchase order generation in the pharmaceutical demands on a national level. Thereafter, we process and analyze data on international flows of labeled pharmaceutical goods to extract knowledge and forecast. We undertake cross-national analysis to forecast demand for over 240 countries around the world. In this study, we rely on an OEC data source "The Observatory of Economic Complexity" to visualize, understand and interact with the historic international trade data. The objective of this paper is to predict future demands from the quantity of previous features by considering the effects of external factors by employing several existing machine learning approaches applied on pharmaceutical import/export data. Forecasting scenarios for demand calculations such as LSTM, CNN, multiple linear regressions and other models are tested. The given application illustrates the effectiveness of these approaches. Their performance measures in both cross validation and overfitting learning modes are studied experimentally, and their practical implications are discussed.

Keywords: demand forecasting cross-country pharmaceutical industry deep learning; demand forecasting; cross-country; pharmaceutical industry; deep learning (search for similar items in EconPapers)
Date: 2022-05-18
Note: View the original document on HAL open archive server: https://hal.univ-antilles.fr/hal-04734349v1
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Published in Rencontres Internationales de la Recherche en Logistique, RIRL, May 2022, Clermont-Ferrand, France

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