Advanced Computational Forecasting for Agri-Business Supply Chain Resilience

Methodology

This study's methodology rigorously examines how advanced computational forecasting strengthens agri-business supply chain resilience in India. It combines theoretical frameworks, empirical analysis, and practical applications for a comprehensive understanding of the research problem.

A comprehensive literature review focuses on theoretical foundations, conceptual frameworks, and empirical evidence of advanced computational forecasting in agri-business supply chains, emphasizing computational modeling, predictive analytics, machine learning, and optimization. Assessing their relevance in the Indian agricultural context illuminates their potential to address supply chain challenges. Case studies offer real-world insights into computational forecasting's impact, spanning diverse commodities, supply chains, and regions, while empirical analysis evaluates performance in predicting demand, optimizing inventory, and managing disruptions, using statistical techniques and computational tools to derive actionable insights for improved supply chain efficiency and resilience.

Collaborative workshops facilitate knowledge exchange and solution co-creation among stakeholders in India's agri-business sector. They bring together diverse groups to discuss trends, share best practices, and explore opportunities for innovation. Through interactive sessions, participants tackle challenges in adopting advanced computational forecasting techniques to enhance supply chain resilience [Osumba et al. (2021)].