A Model for Improving Supply Chain Monitoring by Integrating Fuzzy Artificial Intelligence Algorithms into a Blockchain Platform: A Case Study of the Healthcare Industry
Keywords:
Supply Chain Monitoring, Blockchain, Fuzzy Artificial Intelligence, Healthcare Industry, Fuzzy Cognitive Map, Fuzzy Inference SystemAbstract
This study aimed to design and validate an integrated model based on blockchain technology and fuzzy artificial intelligence algorithms to improve supply chain monitoring in the healthcare industry. This applied-developmental study employed a mixed-methods design with a sequential exploratory approach. The statistical population included 117 managers, specialists, and experts active in the healthcare supply chain in Qazvin Province, all of whom were included in the quantitative phase through census sampling. In the qualitative phase, semi-structured interviews were conducted with 15 experts, and the data were analyzed through open, axial, and selective coding. The quantitative instrument was a researcher-made questionnaire, whose overall reliability was confirmed using Cronbach’s alpha coefficient of 0.916. The proposed model was developed in three layers: a blockchain layer based on Hyperledger Fabric, a fuzzy artificial intelligence layer consisting of a fuzzy inference system and fuzzy cognitive maps, and an integration layer. Data were analyzed using descriptive and inferential statistics, confirmatory factor analysis, fuzzy analytic hierarchy process, dynamic system simulation, and t-tests. The FAHP results showed that security and credibility had the highest priority weight among the monitoring dimensions (0.324), followed by adaptability (0.287), tracking and tracing (0.221), and operational efficiency (0.168). In the fuzzy cognitive map, data immutability was identified as the most influential concept, with a centrality degree of 15.28. The one-sample t-test indicated that all main dimensions were significantly higher than the midpoint of the scale. The paired-samples t-test confirmed significant differences between the proposed model and the traditional system in disruption detection accuracy, user satisfaction, perceived security, and information transparency. The proposed model obtained a total weighted score of 8.92 out of 10, compared with 7.74 for the traditional system, indicating a 15.2% improvement. The integration of blockchain and fuzzy artificial intelligence can provide an effective framework for intelligent monitoring of the healthcare supply chain. Although the proposed model has limitations in speed and scalability compared with centralized systems, its advantages in security, transparency, reliability, and decision-making accuracy make it a valuable option for improving healthcare supply chain management.
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