Paper Contents
Abstract
The shrimp business is one of the fastest-growing fields in the world of aquaculture; however, its logistics and supply chain management processes are plagued by persistent challenges, including high perishability rates, unstable demand, stringent quality control practices, and extensive global trade networks. Traditional models of the supply chain often struggle to overcome the inefficiencies of cold-chain custodianship, transportation bottlenecks, inventory forecasting errors, and the increasing demands of sustainability. Against this background, AI and ML are emerging as major revolutionary technologies that can rationalize shrimp logistics in the whole chain of production, both primary and post-harvest, through distribution channels to the retail hierarchies. Offering a very precise forecast of demand and supply volatility, AI-based predictive analytics will put the interested parties in a better position to make evidence-based decisions when choosing whether or not to harvest, process allocations, or inventory positioning. The machine-learning algorithms, in their turn, can be used to actualize cold-chain monitoring by detecting anomalies of the temperature and humidity parameters, which can be used to minimize the spoilage rates and to ensure strict compliance with international quality standards, respectively. In addition, smart route-optimization systems utilizing real-time traffic and weather information work well by reducing transit time and limiting the logistical cost. In combination with blockchain technology, AI integration ensures end-to-end transparency, traceability, and confidence in the supply chain, in response to the fact that there is increasing consumer demand for seafood supply chains based on sustainable and ethical production. Moreover, the use of computer vision, based on ML, can improve the rigor of the quality inspection process by automated grading and defect identification, which reduces human error and human involvement. Artificial intelligence-based predictive maintenance also enhances the logistical infrastructure by reducing downtimes of equipment in both the design and transport fleet. Finally, integrating AI and ML into the shrimp logistics results in increased efficiency, reduced costs, increased sustainability, and the ability to overcome the fluctuations of the global market. This dissertation investigates such applications, with greater emphasis on how AI and ML are transforming the future of shrimp supply-chain management.
Copyright
Copyright © 2025 Dr. B.Sankar Naik. This is an open access article distributed under the Creative Commons Attribution License.
