Authors: Meysam Jahani, Zahra Zojaji, AhmadReza Montazerolghaem, Maziar Palhang, Reza Ramezani, Ahmadreza Golkarnoor, Alireza Akhavan Safaei, Hossein Bahak, Pegah Saboori, Behnam Soufi Halaj, Ahmad R Naghsh-Nilchi, Fatemeh Mohamadpoor, Saeid Jafarizadeh
Date published: 2025/1/1
Publisher: Medknow
Vol. 15, page.10.4103, Issue.1

Abstract

Background: The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time. Methods: Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions‑2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results. Results: A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics. Conclusions: In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models. Keywords: Drug demand forecasting, Isfahan artificial intelligence competitions, Supply chain managemen