چكيده لاتين
In recent years, the emergence of new diseases has led to an increase in the production of various pharmaceutical products. Simultaneously, the expansion of pharmaceutical exchange points along the supply chain has resulted in drugs being transported over long distances and through multiple stages before reaching the end consumer. While this quantitative and qualitative development in the production and distribution of medicines has positively impacted public health, it has also introduced significant complexities into the pharmaceutical supply chain. One of the major consequences of this complexity is the increased risk of distributing substandard (counterfeit or non-compliant) drugs. In response to these challenges, various approaches have been proposed to improve the pharmaceutical supply chain. Some of these approaches focus on enhancing traditional supply chain management infrastructures to increase operational efficiency. However, traditional methods are heavily reliant on centralized authorities or third parties, making them vulnerable to manipulation when information is not in favor of a specific organization. Such data manipulation can result in inconsistent information across different exchange points in the supply chain, ultimately disrupting the traceability of medicines. To address this issue, decentralized methods have gained attention as a means to ensure data security, transparency, and immutability. Nevertheless, decentralized systems alone are insufficient for identifying and reducing the circulation of substandard drugs, as they primarily focus on data storage and record-keeping, lacking inherent capabilities for analysis, decision-making, and deriving operational insights. In other words, most blockchain-based approaches concentrate on general tracking of drug production and distribution processes, without exploring patterns of counterfeit drug distribution or identifying hidden contributing factors.
This dissertation introduces an enhanced pharmaceutical supply chain management system, named DSCM (Distributed Supply Chain Management), which focuses on identifying and mitigating substandard drugs to ensure control over the distribution process. DSCM utilizes smart contracts, designed in this research, along with private blockchain technology, to propose an integrated approach for detecting indicators of substandard drugs and effectively reducing their distribution. In the first phase, DSCM designs and implements a blockchain-based smart contract to authenticate supply chain participants. By recording and maintaining identity information on the blockchain, this contract enables traceability of product origin and distributor credibility, and through automatic interaction with other smart contracts, facilitates the identification of unauthorized drug distributors. Next, a unique identifier is generated for each drug item using smart tags and recorded in the blockchain ledger. This enhances the traceability and tracking of pharmaceutical products throughout the supply chain. DSCM then introduces a smart contract for environmental anomaly detection and tracking, enabling real-time monitoring of drug conditions during distribution. This contract collects data such as temperature and location through sensors and user interfaces. If unfavorable conditions arise that could compromise drug quality, alerts are automatically sent to regulatory authorities. Subsequently, DSCM incorporates a location-time adaptive algorithm and a drug movement-tracking module into a new smart contract to increase the detection rate of substandard drugs. This contract flags drugs that remain in the supply chain outside of expected time windows or exceed allowable limits, notifying relevant observers. This mechanism helps identify drugs with potentially incorrect labeling. Additionally, DSCM includes a smart contract designed to monitor distribution sequences.
