Chapter 3: Investigating critical success factors in artificial intelligence-based supply chains
Keywords:
supply chain management, artificial intelligence-based supply chains, artificial intelligenceAbstract
Artificial intelligence (AI) has the potential to transform many aspects of business operations. This technology can be used in various fields such as data analysis and demand forecasting, improving logistics and transportation routes, and identifying inefficient points in the supply chain. This ultimately leads to improved responsiveness to demand changes, reduced delivery times, and lower costs. This chapter will examine and analyze the applications of artificial intelligence in supply chain management and the effects of this technology on this field.
Downloads
References
Aliahmadi, A., & Nozari, H. (2023, January). Evaluation of security metrics in AIoT and blockchain-based supply chain by Neutrosophic decision-making method. In Supply Chain Forum: An International Journal (Vol. 24, No. 1, pp. 31-42). Taylor & Francis.
Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J. (2022). AIoT-based sustainable smart supply chain framework. International journal of innovation in management, economics and social sciences, 2(2), 28-38.
Bayanati, M. (2023). Business Model of Internet of Things and Blockchain Technology in Developing Countries. International Journal of Innovation in Engineering, 3(1), 13-22.
Bayanati, M. (2023). Security and privacy analysis based on Internet of Things in the fourth industrial generation (Industry 4.0). International Journal of Innovation in Management, Economics and Social Sciences, 3(2), 1-12.
Bayanati, M., & Rahmaty, M. (2023). A locating model for small e-shop distribution centers in conditions of uncertainty. Journal of Industrial and Systems Engineering, 15(1), 110-133.
Daneshvar, A., Radfar, R., Ghasemi, P., Bayanati, M., & Pourghader Chobar, A. (2023). Design of an Optimal Robust Possibilistic Model in the Distribution Chain Network of Agricultural Products with High Perishability under Uncertainty. Sustainability, 15(15), 11669.
Ghahremani-Nahr, J., Aliahmadi, A., & Nozari, H. (2022). An IoT-based sustainable supply chain framework and blockchain. International Journal of Innovation in Engineering, 2(1), 12-21.
Jafari Eskandari, M., Nozari, H., & Mokhtari Saghinsara, M. (2020). Implementation of fuzzy-robust programming method in the locating-routing and allocation multi-objective pharmaceutical supply chain problem under uncertainty. Journal of Decisions and Operations Research, 5(1), 71-48.
Lotfi, F. H. Z., Najafi, S. E., & Nozari, H. (Eds.). (2016). Data envelopment analysis and effective performance assessment. IGI Global.
Najafi, S. E., Nozari, H., & Edalatpanah, S. A. (2022). Artificial Intelligence of Things (AIoT) and Industry 4.0–Based Supply Chain (FMCG Industry). A Roadmap for Enabling Industry 4.0 by Artificial Intelligence, 31-41.
Nozari, H., & Edalatpanah, S. A. (2023). Smart Systems Risk Management in IoT-Based Supply Chain. In Advances in Reliability, Failure and Risk Analysis (pp. 251-268). Singapore: Springer Nature Singapore.
Nozari, H., Ghahremani-Nahr, J., & Najafi, E. (2023). The Role of Internet of Things and Blockchain in the Development of Agile and Sustainable Supply Chains. In Digital Supply Chain, Disruptive Environments, and the Impact on Retailers (pp. 271-282). IGI Global.
Nozari, H., Ghahremani-Nahr, J., & Szmelter-Jarosz, A. (2023). AI and machine learning for real-world problems. Advances In Computers, (online first).
Obaid, H. S., & Nozari, H. (2022). Examining Dimensions and Components and Application of Supply Chain Financing (In Chain Stores). International Journal of Innovation in Management, Economics and Social Sciences, 2(4), 81-88.
Sadeghi, M. E., Nozari, H., Dezfoli, H. K., & Khajezadeh, M. (2021). Ranking of different of investment risk in high-tech projects using TOPSIS method in fuzzy environment based on linguistic variables. arXiv preprint arXiv:2111.14665.
Seyed Bathaee, M. S., Ghahremani-Nahr, J., Nozari, H., & Najafi, S. E. (2022). Designing a Mathematical Model of a Collaborative Production System Based on Make to Order under Uncertainty. Journal of Industrial Management Perspective, 12(1, Spring 2022), 193-224.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Hamed Nozari
This work is licensed under a Creative Commons Attribution 4.0 International License.
