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Considering the growth of blockchain technology, the research aims to transform the efficiency of recommending optimal coffee suppliers within the complex supply chain network. This transformation relies on the extraction of vital transactional data and insights from stakeholders, facilitated by the dynamic interaction between the application interface (e.g., Rest API) and the blockchain network. These extracted data are then subjected to advanced data processing techniques and harnessed through machine learning methodologies to establish a robust recommendation system. This innovative approach seeks to empower users with informed decision-making abilities, thereby enhancing operational efficiency in identifying the most suitable coffee supplier for each customer. Furthermore, the research employs data visualization techniques to illustrate intricate clustering patterns generated by the K-Means algorithm, providing a visual dimension to the study's evaluation.
This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (201803932), and supported by the MSIT (Ministry of Science and ICT), Korea, under the Special R&D Zone Development Project (R&D) - Development of R&D Innovation Valley support program (2023-DD-RD-0152) supervised by the Innovation Foundation.