Impact of AI and Machine Learning on Supply Chain Optimization in Developing Economies

Abstract

Background: Emerging economies face various unsolved issues that limit supply chain development, such as inefficiency and unhealthy competition, lack of transparency, and an underdeveloped technological infrastructure.

Purpose: This paper describes how Artificial Intelligence and Machine Learning can solve these problems with the help of supply chain management in various areas. In developing economies, Artificial Intelligence and Machine Learning are transforming supply chain management, offering exceptional opportunities for optimization. In this paper, the innovative potential of AI and Ml is explored, as how this technology enhances supply chain efficiency, minimizing operational cost and optimizing decision-making in resource bottleneck environments. There are so many unique challenges in developing economies that impact supply chain performance such as gaps in infrastructures, partial access to data, and irregular market conditions.

Methods: A literature review of recent studies and reports on AI and ML applications. This paper discusses the concept of the supply chain, artificial intelligence, and machine learning, and recent applications of Artificial Intelligence and Machine Learning in the processes of the supply chain, it analyses critical constraints to adoption, skill gaps, investment hurdles as well as technological readiness.

Results: AI and Machine learning-driven technologies are strengthening organizations in various fields to better presume trends in the market such as decreasing the lead time, enhancing the level of inventory, probabilistic analytics, and market forecasting. This paper studies the continuous with a discussion of how supply chain optimization using AI and ML might promote long-term economic growth in developing nations and policy recommendations to encourage wider usage of these technologies.

Conclusion: In the end, Artificial Intelligence and Machine learning are critical instruments for enhancing supply chain competitiveness and resilience in the face of external economic challenges. Even though there are some hurdles such as technological adaptation and infrastructural requirements, the implementation of AI and ML can help improve supply chain efficiency and enable economic growth in emerging economies.

  • Page Number : 1-7

  • Published Date : 2023-10-15

  • Keywords
    Supply chain, Artificial Intelligence, Machine learning, Cost reduction, Market demand forecasting

  • DOI Number
    10.15415/jtmge/2023.142001

  • Authors
    Neha Soni

References

  • Belhadi, A., & Landry, S. (2022). A review of artificial intelligence in supply chain management. International Journal of Production Research, 60(17), 6221-6243.
  • Blackhurst, J., Rungtusanatham, M. J., Scheibe, K., & Ambulkar, S. (2018). Supply chain vulnerability assessment: A network based visualization and clustering analysis approach. Journal of Purchasing and Supply Management, 24(1), 21-30. https://doi.org/10.1016/j.pursup.2017.10.004
  • Bughin, J., Hazan, E., Sree Ramaswamy, P., & Chu, M. (2017). Artificial intelligence the next digital frontier. McKinsey Global Institute.
  • Burgess, A. (2018). AI in Action. In: The Executive Guide to Artificial Intelligence. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-50722-9
  • Chopra, S., & Meindl, S. M. (2016). Supply chain management: Strategy, planning, and operation. Pearson Education Limited.
  • Dale, M. (2018). Automating grocery shopping. Imaging and Machine Vision Europe, (85), 16-20.
  • Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3).
  • Dauvergne, P. (2022). Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs. Review of International Political Economy, 29(3), 696-718. https://doi.org/10.1080/09692290.2020.1814381
  • De Jesus, A. (2019). AI for Pricing. Comparing 5 Current Applications. https://emerj.com/ai-sector-overviews/ai-for-pricing-comparing-5-current-applications/2019
  • Deb, S. K., Jain, R., & Deb, V. (2018). Artificial intelligence―creating automated insights for customer relationship management. 2018 8th international conference on cloud computing, data science & engineering (Confluence), 758-764. https://doi.org/10.1109/CONFLUENCE.2018.8442900
  • Druehl, C., Carrillo, J., & Hsuan, J. (2018). Innovation and Supply Chain Management Technological Innovations: Impacts on Supply Chains. Springer. https://doi.org/10.1007/978-3-319-74304-2_12
  • Ghiani, G., Laporte, G., & Musmanno, R. (2023). Forecasting for supply chain management. Springer International Publishing
  • Mahraz, M. I., Benabbou, L., & Berrado, A. (2022). Machine learning in supply chain management: A systematic literature review. International Journal of Supply and Operations Management, 9(4), 398416.  https://doi.org/10.22034/ijsom.2021.109189.2279
  • Zhao, P., Li, S., & Liu, S. (2022). A survey of reinforcement learning for supply chain management. Enterprise Information Systems, 16(7), 1327-1352.