Development of a QR Code-Based Inventory System and Consumable Material Management using Django Python

Inventory management and consumable materials in educational institutions, particularly at the Faculty of Health at Nurul Jadid University, face significant challenges. Current manual recording and reporting methods are prone to errors, leading to operational inefficiencies and financial risks, especially given the high cost of medical equipment. In addition, unrecorded inventory data can be lost, and consumable materials can either run out prematurely or accumulate unused. Lack of transparency and accuracy in tracking further complicated planning and decision-making. QR Code technology and the Django Python framework are widely used in other industries; however, their application in education, particularly healthcare, remains underexplored. Unlike previous studies that primarily focused on commercial applications, this study delves into the underexamined area of healthcare inventory management in educational settings, offering a scalable and efficient solution using modern technology. This study addresses these empirical and research gaps by developing a QR Code-based inventory system using Django Python to manage consumable materials at the Faculty of Health. The proposed system enhances efficiency, accuracy, and transparency by providing real-time data for better decision-making. The evaluation will focus on reliability, user-friendliness, ease of access, response time, and user satisfaction. This research not only contributes to the literature on technology applications in education but also provides a practical model for other institutions.

Authors

Publication Details

Journal
Journal of Intelligent Computing & Health Informatics
Published
2024-09-30
DOI
https://doi.org/10.26714/jichi.v5i2.15578
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
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article

Development of a QR Code-Based Inventory System and Consumable Material Management using Django Python

Moh. Ainol Yaqin, Satria Agus Ariyanto, Laila Silvia Amtini, Fina Nuralita Zhalianti
Journal of Intelligent Computing & Health Informatics
Advanced Manufacturing and Logistics Optimization
article

Development of a QR Code-Based Inventory System and Consumable Material Management using Django Python

Moh. Ainol Yaqin, Satria Agus Ariyanto, Laila Silvia Amtini, Fina Nuralita Zhalianti
article en

Abstract

Inventory management and consumable materials in educational institutions, particularly at the Faculty of Health at Nurul Jadid University, face significant challenges. Current manual recording and reporting methods are prone to errors, leading to operational inefficiencies and financial risks, especially given the high cost of medical equipment. In addition, unrecorded inventory data can be lost, and consumable materials can either run out prematurely or accumulate unused. Lack of transparency and accuracy in tracking further complicated planning and decision-making. QR Code technology and the Django Python framework are widely used in other industries; however, their application in education, particularly healthcare, remains underexplored. Unlike previous studies that primarily focused on commercial applications, this study delves into the underexamined area of healthcare inventory management in educational settings, offering a scalable and efficient solution using modern technology. This study addresses these empirical and research gaps by developing a QR Code-based inventory system using Django Python to manage consumable materials at the Faculty of Health. The proposed system enhances efficiency, accuracy, and transparency by providing real-time data for better decision-making. The evaluation will focus on reliability, user-friendliness, ease of access, response time, and user satisfaction. This research not only contributes to the literature on technology applications in education but also provides a practical model for other institutions.

Journal of Intelligent Computing & Health InformaticsVol. 5(2)
Openalex Percentile: Top 74%
Advanced Manufacturing and Logistics Optimization
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