Drug Inventory and Supply Chain Tracking System

The proposed idea of DISCTS demonstrates that with the help of innovation and technologies, it is possible to implement a revolution in the existing system and dynamics of drug inventories management. These findings of our research have led us to the conclusion that such technologies that include barcoding, cloud computing, blockchain, and maintaining big chains between hospitals and inventories will result in the domain of high inventory accuracy and measurement, eliminate the cases of stockouts in case of demand, and traceability and availability of pharmaceutical products. Along with the additions to the operational functionality of the management system, they are helpful in refining the health of the patients, too, as the errors in the accounts are reduced, and receiving the required medicine becomes simpler. The introduction of DISCTS (Drug Inventory Tracking and Supply Chain Tracking System) will contribute to the major increase in the accuracy of inventory management and reflected in the results through the increase in the percentage of accuracy and the reduction in the number of stockouts. These results can be observed in the change where the DISCTS is being applied in a large scale. These results prove that it is feasible to account and manage this supply chain with the assistance of the application and indicates that it results in the introduction of the clear and efficient supply chain of pharmaceuticals through the drug inventories which is facilitated by the application of the blockchain technology. Therefore, risks associated with counterfeit drugs are real, however, detection rate of the counterfeit drugs is high, to address this regulation will be achieved as the openness will be known between organizations. It proves healthy to the health cares, who are keen on introducing an improved drug management approach to maintain and run the drugs that minimize system inefficiency that may impinge on the health of the patient. This can also encourage the exploration of the DISCTS and such undertakings in the future which can be attributed to technologies and researches in the future. It will translate to a familiar beneficiary product that principally addresses the need, requirements of the user and whether the user is contented; that is how it will be facilitated by the future scope and technologies, be improving with the emerging technologies such as AI and ML too so that we can manage our pharmaceutical inventories more effectively. In summary therefore, whenever the management of such a drug inventory and the supply chain that ensures the whole distribution of drugs flow in place, is relief when such an application and technology is introduced when it is most needed. It is that we now possess the technologies, and, thus, we will devise ways to ensure that our applications become the source of quality, better, and healthier lives of the patients as the result. So here to draw a conclusion prospectively, the execution of such an application and technology when it comes to the management of these drug inventories and the supply chain, which manages the entire drug flow distribution, is a relief whenever it comes to encountering the issues which are encountered in the use and adherence of traditional practices. We now possess the technologies, and thus, we will discover ways of ensuring that our applications bring about quality, better and healthier lives of the patients as the fruit. 

[1]     D. Ivanov and A. Dolgui, “A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0,” Production Planning & Control, vol. 32, no. 9, pp. 775–788, 2021. [2]     A. K. Gupta and S. K. Maranas, “Managing uncertainty in pharmaceutical supply chains: A review,” Computers & Chemical Engineering, vol. 134, 2020. [3]     Sachdeva, V., Agarwal, G., Kumar, A., Varshney, S., & Rajput, D. Federated Machine Learning in Medical Science: A Prospective Investigation. In Federated Learning for Smart Communication using IoT Application (pp. 109-123). Chapman and Hall/CRC. [4]     World Health Organization, “Medicines and health products: Inventory management,” WHO, 2020. [5]     Y. Wang, J. Zhang, and L. Liu, “Healthcare supply chain management: Literature review and future research directions,” Sustainability, vol. 12, no. 17, 2020. [6]     YADAV, A., MISHRA, A., SINGH, H., & SACHDEVA, V. (2024, May). Identity Verification System using Deep Learning. In 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS) (pp. 01-07). IEEE. [7]     World Health Organization, Guidelines for Medicine Supply Chain and Inventory Management, WHO Press, 2021. [8]     Y. Bai and J. Zhang, “Barcoding in healthcare,” Journal of Patient Safety, vol. 16, no. 2, pp. e45–e52, 2020. [9]     S. Gupta and R. Kumar, “Healthcare supply chain resilience: Insights gained during the COVID-19 crisis,” Journal of Health Services, vol. 52, no. 1, pp. 45–60, 2022. [10]   Kashyap, B., Sachdeva, V., Shirahatti, A., Singh, G., Arya, A., Jagatheesan, S., & Kumar, C. (2024). A Novel Approach for Human Behaviour Prediction Using Deep Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 793-801. [11]   P. T. Helo and Y. Hao, “Cloud-based supply chain integration and performance in healthcare,” Computers in Industry, vol. 65, no. 6, pp. 911–921, 2014. [12]   M. [Author(s)], “Drug Inventory and Supply Chain Tracking,” ResearchGate, 2023. [13]   Sachdeva, V., & Arora, V. (2022, December). Comparative Analysis of Accuracy of Supervised Learning Classifier for breast cancer classification. In 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 395-399). IEEE. [14]   M. Chowdhury and M. Rahman, “Effects of inventory control practices on patient safety outcomes in hospital settings,” vol. 34, no. 3, pp. 234–245, 2021. [15]   H. Hwang, J. Kim, and S. Lee, “Automated dispensing technologies and their influence on medication administration errors: A systematic review,” International Journal of Medical Informatics, vol. 148, p. 104392, 2021. [16]   Sachdeva, V., Mishra, N., Kumar, A., & Singh, N. (2024, November). Advanced Techniques for Early Prediction from CT Scans. In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 951-955). IEEE.