Supply Chain for Safe & Timely Distribution of Medicines using Blockchain & Machine Learning

• Main Authors: Shah, Yashvi, Verma, Yashashvi, Sharma, Umang, Sampat, Aum, Kulkarni, Vikram
• Format: Conference Proceeding
• Language: English
• Subjects: Blockchain; Counterfeit Drugs; Decision making; Drugs; Machine learning; Machine learning algorithms; Medical services; Pharmaceutical Supply Chain Management; Real-time systems; Supply chains
• ISSN: 2832-3017
• DOI: 10.1109/ICSSIT55814.2023.10061049

Because fake drugs are so prevalent, researchers, managers, and policymakers are paying close attention to the issue. When fake medications are used, they can harm or even kill the person who is taking them. The majority of the issues with the safety of counterfeit drugs in the supply chain are related to how the drugs are manufactured in the first place. Because it is difficult to track down the right active pharmaceutical ingredients during manufacturing, finding medicines that do not have the right active ingredients can harm or kill patients. Because Blockchain is so advanced, it can be used to track medicines from the manufacturer to the person who takes them. It can also be used to detect counterfeit medications. Blockchain technology can be promising to transform and improve the healthcare industry because it is secure, dependable, and open, and it can store data in real time. Blockchain technology and Machine Learning(ML) will be used in this study to investigate drug safety and ensure timely distribution of medicines. The use of Blockchain technology, proposes a supply chain management system based on a permissioned Blockchain network and role-based authorization. This would prevent people from tampering with the network's data. By ensuring that all item units are transparent, traceable, and consistent, the Blockchain-based decentralized solution will make it easier for people to collaborate and trust one another. By using data from the Blockchain network based machine learning algorithms will be used to predict delays in deliveries and hence help the Supply Chain Managers to take preventive action and save further supply chain expenses.