Forecasting Drug Demand for Optimal Medical Inventory Management: A Data-Driven Approach with Advanced Machine Learning Techniques

Authors : Vibin Ravi Kumar; Pallavi Waghmare; Sampath Bukya; Bharani Kumar Depuru; Dr. Ilankumaran Kaliamoorthy

Volume/Issue : Volume 8 - 2023, Issue 9 - September

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A hospital's capacity to allocate resources efficiently and guarantee drug supply depends on effective medical inventory management. This study paper offers a thorough data-driven strategy for drug demand forecasting that makes use of cutting-edge machine learning methods, intending to improve medical inventory management procedures. A range of machine learning algorithms were used to precisely model and anticipate drug demand trends using historical data, including Deep Learning-based models, time series forecasting techniques, and ensemble learning methods. To determine the best strategy for predicting drug demand, the study compares the performance of various algorithms. Healthcare facilities can improve patient care, reduce waste, and achieve optimal supply chain performance by minimising stockouts, lowering surplus inventory, and optimising the supply chain. The findings of this study increase medical inventory management procedures by offering insightful information on the use of cutting-edge machine learning methods for precise drug demand forecasting. In turn, this promotes the use of evidence-based decision-making and medical resources. Machine learning for forecasting has enormous potential for revealing previously unknown patterns in disease, treatment, and care as the healthcare sector experiences a data revolution with the growing use of Artificial Intelligence (AI), Predictive Analytics, and Business Intelligence. The research intends to enhance people's health outcomes, socioeconomic status, and day-to-day activities by resolving the difficulties caused by the complexity of pharmaceuticals and ensuring the supply of vital medications. The supply of essential drugs and life-saving supplies can be less uncertain with accurate demand estimates, which helps to create a well-organised and effective health supply chain. The study highlights the significance of using suitable prediction models, such as collaborative predictions based on end-user consumption data, economic order quantity, or the Min/Max formula, to ascertain the necessary dosages of critical medications while taking into account available resources, supply chain information, and inventory levels. Healthcare organisations can considerably reduce prediction errors and improve the efficiency of medical inventory management by utilising the results of this extensive research.

Keywords : Drug Demand Forecasting, Machine Learning in Medicine, Medical Inventory Management, Healthcare Supply Chain, Predictive Analysis, Hospital Management


Paper Submission Last Date
31 - December - 2023

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In 1-2 Days

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