Transformation of the Dairy Supply Chain Through Artificial Intelligence: A Systematic Review

Gabriela Joseth Serrano-Torres (), Alexandra Lorena López-Naranjo, Pedro Lucas Larrea-Cuadrado and Guido Mazón-Fierro
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Gabriela Joseth Serrano-Torres: Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba 060101, Ecuador
Alexandra Lorena López-Naranjo: Facultad de Ciencias Políticas y Administrativas, Universidad Nacional de Chimborazo, Riobamba 060101, Ecuador
Pedro Lucas Larrea-Cuadrado: Facultad de Ciencias Políticas y Administrativas, Universidad Nacional de Chimborazo, Riobamba 060101, Ecuador
Guido Mazón-Fierro: Facultad de Administración de Empresas, Escuela Superior Politécnica de Chimborazo, Riobamba 060101, Ecuador

Sustainability, 2025, vol. 17, issue 3, 1-21

Abstract: The dairy supply chain encompasses all stages involved in the production, processing, distribution, and delivery of dairy products from farms to end consumers. Artificial intelligence (AI) refers to the use of advanced technologies to optimize processes and make informed decisions. Using the PRISMA methodology, this research analyzes AI technologies applied in the dairy supply chain, their impact on process optimization, the factors facilitating or hindering their adoption, and their potential to enhance sustainability and operational efficiency. The findings show that artificial intelligence (AI) is transforming dairy supply chain management through technologies such as artificial neural networks, deep learning, IoT sensors, and blockchain. These tools enable real-time planning and decision-making optimization, improve product quality and safety, and ensure traceability. The use of machine learning algorithms, such as Tabu Search, ACO, and SARIMA, is highlighted for predicting production, managing inventories, and optimizing logistics. Additionally, AI fosters sustainability by reducing environmental impact through more responsible farming practices and process automation, such as robotic milking. However, its adoption faces barriers such as high costs, lack of infrastructure, and technical training, particularly in small businesses. Despite these challenges, AI drives operational efficiency, strengthens food safety, and supports the transition toward a more sustainable and resilient supply chain. It is important to note that the study has limitations in analyzing long-term impacts, stakeholder resistance, and the lack of comparative studies on the effectiveness of different AI approaches.

Keywords: machine learning; demand prediction; algorithms; predictive models; milk (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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