Leveraging Blockchain and ANFIS for Optimal Supply Chain Management
<|reference_start|>Leveraging Blockchain and ANFIS for Optimal Supply Chain Management: The supply chain is a critical segment of the product manufacturing cycle, continuously influenced by risky, uncertain, and undesirable events. Optimizing flexibility in the supply chain presents a complex, multi-objective, and nonlinear programming challenge. In the poultry supply chain, the development of mass customization capabilities has led manufacturing companies to increasingly focus on offering tailored and customized services for individual products. To safeguard against data tampering and ensure the integrity of setup costs and overall profitability, a multi-signature decentralized finance (DeFi) protocol, integrated with the IoT on a blockchain platform, is proposed. Managing the poultry supply chain involves uncertainties that may not account for parameters such as delivery time to retailers, reorder time, and the number of requested products. To address these challenges, this study employs an adaptive neuro-fuzzy inference system (ANFIS), combining neural networks with fuzzy logic to compensate for the lack of data training in parameter identification. Through MATLAB simulations, the study investigates the average shop delivery duration, the reorder time, and the number of products per order. By implementing the proposed technique, the average delivery time decreases from 40 to 37 minutes, the reorder time decreases from five to four days, and the quantity of items requested per order grows from six to eleven. Additionally, the ANFIS model enhances overall supply chain performance by reducing transaction times by 15\% compared to conventional systems, thereby improving real-time responsiveness and boosting transparency in supply chain operations, effectively resolving operational issues.<|reference_end|>
@article{farhadi2024leveraging,
    title={Leveraging Blockchain and ANFIS for Optimal Supply Chain Management},
    author={Amirfarhad Farhadi, Homayoun Safarpour Motealegh Mahalegi, Abolfazl
  Pourrezaeian Firouzabad, Azadeh Zamanifar, Majid Sorouri},
    journal={arXiv preprint arXiv:2408.17161},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17161},
    primaryClass={eess.SY cs.CE cs.SY}
}