| Title: |
LEVERAGING AI IN SUPPLY CHAIN ANALYTICS: A CASE STUDY FROM FOOD INDUSTRY. |
| Authors: |
Mirčetić, Dejan1 dejan.mircetic@ivi.ac.rs; Maslaric, Marinko2 marinko@uns.ac.rs; Bojić, Sanja2 s_bojic@uns.ac.rs; Nikolicic, Svetlana2 cecan@uns.ac.rs; Rakić, Branka3 branka.rakic@ivi.ac.rs |
| Source: |
Proceedings of International Scientific Conference Business Logistics in Modern Management. 2024, p321-335. 15p. |
| Subject Terms: |
*Supply chain management; *Food industry; *COVID-19 pandemic; *Economic forecasting; *Artificial intelligence |
| Abstract: |
In today's complex and evolving landscape, supply chains (SCs) face significant challenges in accurately forecasting market demand, particularly following the disruptions caused by the COVID-19 pandemic. This paper aims to investigate whether current demand patterns are merely temporary fluctuations or indicative of a new reality. To address this, we implemented several algorithms to predict future market demand, with a special emphasis on testing the capabilities and performance of artificial intelligence (AI) algorithms in SC contexts. Among the various AI models examined, we selected feed-forward neural networks (NN) as a key representative. Paper includes analysis of the NN against state-of-the-art forecasting models, specifically Facebook's Prophet model and the autoregressive integrated moving average model (ARIMA). The results indicate that the NN outperformed traditional models, demonstrating superior accuracy in demand forecasting. This enhancement in predictive capabilities highlights the potential for integrating advanced AI technologies into SC practices. Overall, this study underscores the importance of leveraging AI in navigating the complexities of modern market demands. By improving demand forecasting accuracy, organizations can better optimize their SC processes, ultimately leading to enhanced operational efficiency and competitive advantage. [ABSTRACT FROM AUTHOR] |
| : |
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| Database: |
Business Source Premier |