Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility
DOI:
https://doi.org/10.47604/ijscm.2547Keywords:
Supply Chain Planning, Artificial Intelligence, Machine Learning, Big Data Analytics, Decision-Making, Forecasting Accuracy, Supply Chain Agility, Emerging Technologies, Digital TransformationAbstract
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.
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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
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Copyright (c) 2024 Jayapal Reddy Vummadi, Krishna Chaitanya Raja Hajarath
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