Authors :
Vibin Ravi Kumar; Pallavi Waghmare; Sampath Bukya; Bharani Kumar Depuru; Dr. Ilankumaran Kaliamoorthy
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/yc7sz6xa
DOI :
https://doi.org/10.5281/zenodo.8351668
Abstract :
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
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
