ADVANCED INVENTORY FOR MEDICAL DISTRIBUTOR (AIMD)
Keywords:
Medicinal Inventory System, AI Image Process- ing, Automated Data Entry, Machine Learning, Stockout Predic- tion, Inventory Optimization, Pharmaceutical Distribution, Bill Image Recognition, Predictive Analytics, Inventory ReplenishmentAbstract
This paper presents the design and implementation of a medicinal inventory system tailored for distributors, integrat- ing advanced AI technologies to streamline inventory manage- ment. The system automates database entry through AI-powered image processing and text analysis, enabling the extraction of relevant data from invoices and bills captured via photos. This eliminates manual data entry, reducing errors and increasing efficiency. Additionally, the system incorporates machine learning (ML) capabilities to predict stockouts by analyzing historical sales data, demand patterns, and inventory trends. It also provides intelligent stock order suggestions to maintain optimal stock levels, ensuring uninterrupted supply. This integrated approach aims to enhance operational efficiency, reduce costs, and improve decision-making for medicinal distributors.
References
I. A. Mostofi and V. Jain, “Inventory management and control of deteri- orating pharmaceutical products sing industry 4.0,” in 2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA), pp. 394–400, IEEE, 2021.
II. Y.-M. Su, H.-W. Peng, K.-W. Huang, and C.-S. Yang, “Image processing technology for text recognition,” in 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 1–5, IEEE, 2019.
III. A. Deepa, S. Chinta, N. K. Ashili, B. S. Babu, R. R. Vydugula, and R. S. VSL, “An intelligent invoice processing system using tesseract ocr,” in 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), pp. 1–6, IEEE, 2024.
IV. C. Kaundilya, D. Chawla, and Y. Chopra, “Automated text extraction from images using ocr system,” in 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 145– 150, IEEE, 2019.
V. K. V. Sakhare and I. Kulkarni, “Predictive analysis of end to end inventory management system for perishable goods,” in 2022 3rd International Conference for Emerging Technology (INCET), pp. 1–5, IEEE, 2022.
VI. K. Praveen, P. Kumar, J. Prateek, G. Pragathi, and J. Madhuri, “Inventory management using machine learning,” International Journal of Engineer- ing Research & Technology (IJERT), vol. 9, no. 06, pp. 866–869, 2020.
VII. N. Dhaliwal, P. K. Tomar, A. Joshi, G. S. Reddy, A. Hussein, and M. B. Alazzam, “A detailed analysis of use of ai in inventory management for technically better management,” in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 197–201, IEEE, 2023.
VIII. W. Saena and V. Suttichaya, “Predicting drug sale quantity using machine learning,” in 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), pp. 1–6, IEEE, 2019.
IX. S. A. Abhishek, S. Raj, and L. Malphedwar, “Evaluation of handwritten mathematical equations,” International Journal of Advanced scientific Research and engineering trends, 2004.
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