Smart pharmacy management system

ABSTRACTPharmacy operations in many small andmedium healthcare setups continue to depend onmanual stock registers, handwritten billing records,and experience-based procurement strategies. Thesetraditional approaches often result in stockshortages, excessive inventory holding, expiredmedicines, financial loss, and inefficient workforceutilization. With the growing demand for accuracy,automation, and data-driven decision-making inhealthcare retail, there is a pressing need forintelligent pharmacy management solutions. Thispaper presents the design and implementation of aSmart Pharmacy Management System, developed asa containerized full-stack web applicationintegrating FastAPI, React, MongoDB, and aMachine Learning–based demand forecastingmodule. The system provides real-time inventorymonitoring, automated sales and billing operations,expiry date tracking with alert mechanisms, andshort-term demand prediction using a LinearRegression model. Docker-based containerizationensures platform independence, rapid deployment,and scalability. Experimental evaluation shows thatthe system significantly reduces manual workload,improves inventory accuracy, minimizes medicinewastage due to expiry, and enhances operationaldecision making through predictive analytics. Theproposed solution is modular, extensible, andsuitable for future expansion into enterprise-levelpharmacy chains and cloud-native healthcaresystems.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-12
DOI
https://doi.org/10.5281/zenodo.18221186
Primary Topic
Forecasting Techniques and Applications
Type
article
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article

Smart pharmacy management system

Mrs X.Ignatius Selva Rani, Mr. Muthu Raja M, Mr. Antony Johnson R, Mr. Aakash K
Zenodo (CERN European Organization for Nuclear Research)
Forecasting Techniques and Applications
article

Smart pharmacy management system

Mrs X.Ignatius Selva Rani, Mr. Muthu Raja M, Mr. Antony Johnson R, Mr. Aakash K
article en

Abstract

ABSTRACTPharmacy operations in many small andmedium healthcare setups continue to depend onmanual stock registers, handwritten billing records,and experience-based procurement strategies. Thesetraditional approaches often result in stockshortages, excessive inventory holding, expiredmedicines, financial loss, and inefficient workforceutilization. With the growing demand for accuracy,automation, and data-driven decision-making inhealthcare retail, there is a pressing need forintelligent pharmacy management solutions. Thispaper presents the design and implementation of aSmart Pharmacy Management System, developed asa containerized full-stack web applicationintegrating FastAPI, React, MongoDB, and aMachine Learning–based demand forecastingmodule. The system provides real-time inventorymonitoring, automated sales and billing operations,expiry date tracking with alert mechanisms, andshort-term demand prediction using a LinearRegression model. Docker-based containerizationensures platform independence, rapid deployment,and scalability. Experimental evaluation shows thatthe system significantly reduces manual workload,improves inventory accuracy, minimizes medicinewastage due to expiry, and enhances operationaldecision making through predictive analytics. Theproposed solution is modular, extensible, andsuitable for future expansion into enterprise-levelpharmacy chains and cloud-native healthcaresystems.

Zenodo (CERN European Organization for Nuclear Research)
Madurai Kamaraj University (IN)
Openalex Percentile: Top 92%
Forecasting Techniques and Applications
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Smart pharmacy management system — Mrs X.Ignatius Selva Rani, Mr. Muthu Raja M, Mr. Antony Johnson R, Mr. Aakash K · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS