Optimizing Supply Chains in the Food and Beverage Industry through Digital Transformation
DOI:
https://doi.org/10.71222/nt2xhy40Keywords:
digital transformation, supply chain optimization, internet of things, Artificial Intelligence, blockchainAbstract
This article explores the role of digital transformation in optimizing supply chains within the food and beverage industry. It highlights the key technologies driving supply chain efficiency, including the Internet of Things (IoT), artificial intelligence (AI), blockchain, and automation. By implementing these technologies, companies are overcoming supply chain challenges such as inefficiency, high costs, inventory management, and product traceability. The article also examines best practices for implementing digital solutions, along with real-world examples from leading food and beverage companies that have successfully integrated digital technologies. These companies have enhanced transparency, improved demand forecasting, reduced waste, and streamlined operations. Looking ahead, advancements in 5G, edge computing, and sustainability initiatives are expected to shape the future of supply chain optimization. Digital transformation is no longer optional for food and beverage companies aiming to stay competitive and responsive in a rapidly evolving market.
References
1. A. Ramírez-Villamil, J. R. Montoya-Torres, A. Jaegler, and J. M. Cuevas-Torres, "Reconfiguration of last-mile supply chain for parcel delivery using machine learning and routing optimization," Computers & Industrial Engineering, 2023, doi: 10.1016/J.CIE.2023.109604
2. A. Mohammed, M. S. Al-shaibani, and S. O. Duffuaa, "A meta-heuristic-based algorithm for designing multi-objective mul-ti-echelon supply chain network," Applied Soft Computing, 2023, doi: 10.1016/J.ASOC.2023.110774
3. M. Malatji, "Accelerating the African continental free trade area through optimization of digital supply chains," Engineering Reports, vol. 2, 2023, doi: 10.1002/ENG2.12711
4. H. Shambayati, M. S. Nikabadi, S. M. A. Khatami Firouzabadi, M. Rahmanimanesh, and S. Saberi, "Optimization of virtual closed-loop supply chain under uncertainty: application of IoT," Kybernetes, vol. 5, pp. 1745-1777, 2023, doi: 10.1108/K-06-2021-0487
5. B. Aljabhan and M. A. Obaidat, "Privacy-Preserving Blockchain Framework for Supply Chain Management: Perceptive Craving Game Search Optimization (PCGSO)," Sustainability, vol. 8, 2023, doi: 10.3390/SU15086905
6. J. Sitoshna, N. Singh, I. E. Grossmann, L. S. de Assis, C. Galanopoulos, P. Garcia-Herreros, and N. Tran, "Diagnosis of linear programming supply chain optimization models: Detecting infeasibilities and minimizing changes for new parameter values," Computers and Chemical Engineering, 2023, doi: 10.1016/J.COMPCHEMENG.2023.108139
7. T. P. da Costa, J. Gillespie, X. CamaMoncunill, S. Ward, J. Condell, R. Ramanathan, and F. Murphy, "A systematic review of real-time monitoring technologies and its potential application to reduce food loss and waste: Key elements of food supply chains and IoT technologies," Sustainability, vol. 1, p. 614, 2022, doi: 10.3390/SU15010614
8. C. O. Iloeje, X. A. Santos, J. Atkins, K. Sun, and A. Gallier, "Protocol for designing, optimizing, and analyzing reverse supply chains using RELOG," STAR Protocols, vol. 1, p. 101913, 2023, doi: 10.1016/J.XPRO.2022.101913
9. N. Gupta, G. Dutta, and M. K. Tiwari, "An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company," Int. J. Prod. Res., vol. 17, pp. 5866-5882, 2018, doi: 10.1080/00207543.2018.1456698.
10. H. Kendall, B. Clark, C. Rhymer, S. Kuznesof, J. Hajslova, M. Tomaniova, and L. Frewer, "A systematic review of consumer perceptions of food fraud and authenticity: A European perspective," Trends in Food Science & Technology, pp. 79-90, 2019, doi: 10.1016/j.tifs.2019.10.005
