Accurate modeling of wheat supply chains is of great importance. The methods for forecasting them can be utilized as strategic planning tools to manage sustainable and balanced supply chains, ensuring a high level of food security, economic growth, and social development. In this paper, we focus on wheat international trade indicators, and a regression model is a crucial component for the chain modeling. Trade indicators in the wheat market are inherently complex and exhibit significant stochasticity and non-stationarity due to the intricate interplay of various trade flows and factors, which pose challenges for accurate market forecasting. We proposed a novel hybrid recurrent and graph-transformer-based model to tackle these challenges. We collected and combined data from international providers such as UN FAOSTAT and UN Comtrade for all the world’s wheat exporters. The experiments show that the proposed model can accurately predict wheat export levels. We have also analyzed how the proposed model can be utilized to predict exports in the case of some pre-defined trade limitations. In the future, the proposed model could be naturally extended to various derivative products of wheat, supporting real-world grain chain models. Our forecasting methods could be used to create an analytical tool to support strategic decision-making in cognitive situation centers, taking into account the national interests and priorities of actors in the international wheat market.
Download the article (PDF) from the MDPI publisher's website: https://www.mdpi.com/2079-8954/13/11/1026/pdf
eLibrary: https://www.elibrary.ru/item.asp?id=87241501
ResearchGate: https://www.researchgate.net/publication/397687558_Hybrid_Supply_Chain_Model_for_Wheat_Market
Otmakhova, Y.; Devyatkin, D.; Zhou, H. Hybrid Supply Chain Model for Wheat Market // Systems 2025, 13, 1026