Enhancing supply chain resilience with multi-agent systems and machine learning: a framework for adaptive decision-making

Аннотация

The research focuses on how Multi-Agent Systems (MAS) coupled with Machine Learning (ML) can help manage the challenges and risks associated with COPYRIGHT new-generation supply chains networks. The proposed © 2025 Original content from this work may be used under the terms of the creative commons attributes 4.0 License. MAS-ML framework improves flexibility, adaptability, and predictiveness in essential roles in supply chain management (SCM), including demand forecasting, inventory management, production planning, and SCM logistics. The framework is based on decentralised decision-making where each agent is responsible for a particular supply chain activity but employs real-time data foresight from the ML model to streamline the activities. This decentralisation enables resilience in supply chains, which can experience events such as demand variability and transportation disruptions. MAS-ML is presented in this paper as the solution capable of enhancing supply chain performance, reliability, and cost optimisation in situations characterised by risk and uncertainty, such as the current global pandemic. In addition, this paper presents potential research areas, such as the integration of more enhanced deep learning algorithms, the extension of proposing MAS-ML into other sectors, and the addressing of ethical and transparency concerns associated with AI-based decision-making systems. The proposed MAS-ML framework improves the adaptability and resiliency of supply chains, providing a flexible solution for modern supply chain problems. Keywords: Multi-Agent Systems (MAS), Machine Learning (ML), Supply Chain Management, Demand Forecasting, Inventory Management, Production The American Journal of Engineering and Technology 6 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology Planning, Logistics Optimisation, Supply Chain can be ineffective in many settings because they do not Flexibility, Decentralised Decision-Making, Supply allow one to predict the appearance of a new factor in Chain Resilience, Predictive Analytics, Random Forest, the process. That is why, with increasing levels of supply Gradient Boosting. chain integration, the impact of such inefficiencies is much more significant. Thus, new approaches, which may effectively accommodate flexibility emerging due Introduction: Decision-making in today's global supply to uncertainty, are deemed necessary. chain environment involves robust strategic This work proposes a novel approach known as MAS- framework and techniques than traditional supply ML o address these challenges affecting supply chain chain models (Katsaliaki et al., 2022). The proposed management in the current environment of fluctuating MAS-ML framework is a holistic method that addresses uncertainty level. The integration of Multi-Agent the adaptability incorporating predictive Systems with Machine Learning enables the computational ability. Supply chains are the principles enhancement of reliability and flexibility of supply chain. of the global economy, which allow the generation of Integrating MAS and ML for the development of the materials, the distribution of products, and the MAS-ML approach makes it possible to increase delivery of them to clients worldwide. More recently, flexibility and adaptability that include demand though, the supply chains have been threatened in forecasting, inventory management, production ways that have opened the supply chain experts to planning, and logistics functions. Therefore, logistics new forms of management. COVID-19 has disrupted agents can use of predictive models in order to find operations worldwide, resulting in factory closures, optimal routes that can improve efficiency in delivery of transportation constraints, and heightened demand products. MAS linked with ML provides a strong for specific goods, including personal protection background useful for addressing more complicated and equipment (Camur et al., 2023). The pandemic caused supply chains (Pasupuleti et al., 2024). severe supply chain disruptions, with many companies facing delays in production and shipping (Xu et al., MAS and ML add a new perspective in supply chain 2020). For instance, global automotive manufacturing management due to the flexibility of handling the slowed significantly due to factory shutdowns in key modern supply chain nature, which is full of regions like China, the US, and Europe. uncertainties (Stoychev, 2023). Implementation of the MAS framework would enable each of the supply chain Many businesses were forced to adopt new strategies, agents to operate autonomously, therefore ensuring such as digitalisation to maintain supply chain the fast and effective decision-making regarding the resilience during the crisis. Supply chains grow in size real-time data that constantly flows into the system and sophistication, and demand fluctuation, inventory (Rajbala et al., 2023). This research presents a significant status