Navigating Ambiguity: Noble Applications in Unbalanced Agri-Food Supply Chain Management

Abstract

In the modern era, the agri-food sector has undergone a profound transformation due to rapid advancements in technology. The continuous evolution of modern tools, particularly those associated with Industry 4.0 and Industry 5.0, has revolutionized the agri-food supply chain management (SCM). Real-time visibility and traceability, powered by AI and machine learning, are now pivotal in enhancing efficiency and profitability within the industry. The collaborators and suppliers still face the situation under the uncertainty. This study identifies the problems and tackle by strategies. These technologies enable industries to achieve higher output with reduced manpower, turning even uncertain scenarios into opportunities for growth. The novelty is to use Grey GRA for sophisticated problems and the prioritization is transformed by SWARA for obtaining weights and TOPSIS is applied for obtaining the performance score. The industries can navigate these uncertainties with reliable and automated solutions. In this study the primary objective is to identify the optimal strategy among various alternatives, ensuring that the chosen approach demonstrates 100% robustness in the face of challenges. This focus on top-tier prioritization has led to a significant increase in profits. Thirteen challenges identified and the preferences are given by the five various group of experts. The output of this study is to set the strategies for collaborators and suppliers for ensuring top most thirteen problems. The importance of this research lies in its ability to provide a strategic framework that ensures robustness and profitability in the agri-food supply chain. By utilizing modern technologies and methodologies, the research offers a pathway for industries, decision-maker, researchers and practitioners to maximize their output under minimum risks even in uncertain conditions.