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Advanced Computational Forecasting for Agri-Business Supply Chain Resilience
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Author(s): Kali Charan Rath (Department of Mechanical Engineering, GIET University, India), Lakshmi Prasad Panda (Government College of Engineering, Kalahandi, India), N. V. Jagannadha Rao (School of Management Studies, GIET University, Gunupur, India), Gopal Krushna Mohanta (Department of Mechanical Engineering; GIET University, Gunupur, India)and Anmol Panda (GIET University, India)
Copyright: 2024
Pages: 20
Source title:
Advanced Computational Methods for Agri-Business Sustainability
Source Author(s)/Editor(s): Suchismita Satapathy (KIIT University (Deemed), India)and Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea)
DOI: 10.4018/979-8-3693-3583-3.ch003
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Abstract
This chapter focuses on using advanced statistical methods to improve predictions in the agri-business sector. It integrates cutting-edge computational techniques and statistical models to address supply chain disruptions in agriculture. The main goal is to create a robust forecasting framework that predicts market trends, demand fluctuations, and enhances supply chain resilience. The novelty lies in combining advanced statistical methodologies like time series analysis, predictive modeling, and data-driven insights for a comprehensive approach. This aims to improve supply chain management in agri-business by fostering adaptability and resilience in changing market conditions.
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