Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility

Authors

  • Jayapal Reddy Vummadi
  • Krishna Chaitanya Raja Hajarath

DOI:

https://doi.org/10.47604/ijscm.2547

Keywords:

Supply Chain Planning, Artificial Intelligence, Machine Learning, Big Data Analytics, Decision-Making, Forecasting Accuracy, Supply Chain Agility, Emerging Technologies, Digital Transformation

Abstract

Purpose: The aim of this research was to discuss the use of artificial intelligence (AI), machine learning (ML), and big data analytics as fundamental pillars of strategic supply chain management, for better decision-making, more precise forecasting, and higher supply chain agility.

Methodology: The paper reviewed existing literature and industry reports to get an in-depth insight into the modern supply chain planning environment, the problems that it faces, and the efficiency of traditional techniques. It then analyzed the opportunities of utilization of AI, ML and big data analytics as well as the certain technologies or techniques that could be utilized, such as the predictive/prescriptive analytics, digital twins and blockchain.

Findings: The study concluded that the traditional supply chain planning processes are becoming more and more out of style and inefficient, taking into account the business environment that are constantly changing, global supply chains, and technological advancements. It emphasized the risks to long-term performance associated to relying too much on the past practices and a call for action for progressive modernization of supply chain planning mechanisms.

Unique Contribution to Theory, Practice and Policy: The report pointed to innovative ways such as AI, ML, and big data analytics for the integration into the supply chain operations for increasing the productivity, resilience and competitiveness. Moreover, it promoted the increase of budgeting on the talent side in order to obtain an appropriate use of technology and to explore new paths in the market.

Downloads

Download data is not yet available.

References

Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://www.mdpi.com/2071-1050/15/20/15088

Aljohani. A., (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. https://www.mdpi.com/2071-1050/15/20/15088

Andronie, M., Lăzăroiu, G., Iatagan, M., Uță, C., Ștefănescu, R., & Cocoșatu, M. (2021). Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20), 2497. https://www.mdpi.com/2079-9292/10/20/2497

Awan, U., Kanwal, N., Alawi, S., Huiskonen, J., & Dahanayake, A. (2021). Artificial intelligence for supply chain success in the era of data analytics. The fourth industrial revolution: Implementation of artificial intelligence for growing business success, 3-21. https://link.springer.com/chapter/10.1007/978-3-030-62796-61

Bag, S., Dhamija, P., Singh, R. K., Rahman, M. S., & Sreedharan, V. R. (2023). Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study. Journal of Business Research, 154, 113315. https://www.sciencedirect.com/science/article/pii/S0148296322007706

Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487-4507. https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.1950935

Chatterjee, S., Chaudhuri, R., Gupta, S., Sivarajah, U., & Bag, S. (2023). Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm. Technological Forecasting and Social Change, 196, 122824. https://www.sciencedirect.com/science/article/pii/S0040162523005097

Choudhury, A., Behl, A., Sheorey, P. A., & Pal, A. (2021). Digital supply chain to unlock new agility: a TISM approach. Benchmarking: an international journal, 28(6), 2075-2109. https://www.emerald.com/insight/content/doi/10.1108/BIJ-08-2020-0461/full/html

Darvazeh, S. S., Vanani, I. R., & Musolu, F. M. (2020). Big data analytics and its applications in supply chain management. New Trends in the Use of Artificial Intelligence for the Industry, 4, 175. https://library.oapen.org/bitstream/handle/20.500.12657/43835/1/externalcontent.pdf#page=189

Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., & Foropon, C. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics, 250, 108618. https://www.sciencedirect.com/science/article/pii/S0925527322002018

Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., ... & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International journal of production economics, 226, 107599. https://www.sciencedirect.com/science/article/pii/S0925527319304347

Jha, A. K., Agi, M. A., & Ngai, E. W. (2020). A note on big data analytics capability development in supply chain. Decision Support Systems, 138, 113382. https://www.sciencedirect.com/science/article/pii/S0167923620301378

Leewayhertz.com, (2024). AI IN DATA ANALYTICS: UNLOCKING THE FUTURE OF DECISION-MAKING. https://www.leewayhertz.com/ai-in-data-analytics/

Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, 33(4), 1246-1268. https://www.emerald.com/insight/content/doi/10.1108/IJLM-02-2021-0094/full/html

Mohiuddin Babu, M., Akter, S., Rahman, M., Billah, M. M., & Hack-Polay, D. (2022). The role of artificial intelligence in shaping the future of Agile fashion industry. Production Planning & Control, 1-15. https://www.tandfonline.com/doi/abs/10.1080/09537287.2022.2060858

Sadeghi, K., Ojha, D., Kaur, P., Mahto, R. V., & Dhir, A. (2024). Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180, 114194. https://www.sciencedirect.com/science/article/pii/S0167923624000277

Shah, H. M., Gardas, B. B., Narwane, V. S., & Mehta, H. S. (2023). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes, 52(5), 1643-1697. https://www.tandfonline.com/doi/abs/10.1080/00207543.2021.1950935

Sudeep. S., (2024). The Role of Artificial Intelligence in Supply Chain Management. https://appinventiv.com/blog/ai-in-supply-chain-analytics/

Younis, H., Sundarakani, B., & Alsharairi, M. (2022). Applications of artificial intelligence and machine learning within supply chains: systematic review and future research directions. Journal of Modelling in Management, 17(3), 916-940. https://www.emerald.com/insight/content/doi/10.1108/JM2-12-2020-0322/full/html

Downloads

Published

2024-05-07

How to Cite

Vummadi, J., & Hajarath, K. (2024). Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility. International Journal of Supply Chain Management, 9(2), 77–87. https://doi.org/10.47604/ijscm.2547

Issue

Section

Articles