A new study published in the journal Sustainability highlights how artificial intelligence (AI) is transforming the dairy supply chain, improving efficiency, sustainability, and logistics.
Study: Transformation of the Dairy Supply Chain Through Artificial Intelligence: A Systematic Review. Image Credit: Dedy_SW/Shutterstock.com
Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, researchers examined AI-driven advancements such as neural networks, deep learning, the Internet of Things (IoT), and blockchain. While AI offers substantial benefits in inventory management and product traceability, challenges such as high implementation costs and infrastructure limitations continue to hinder widespread adoption, particularly for small businesses.
Background
The dairy supply chain is a complex network involving multiple stakeholders, including farmers, processors, distributors, and retailers. Effective management requires optimizing logistics, maintaining product quality, and ensuring food safety through disease prevention and hygienic handling. AI is emerging as a key tool in this sector, offering solutions for logistics optimization, real-time monitoring, predictive analytics, and automation.
Prior research has explored AI’s role in livestock monitoring, food traceability, and supply chain decision-making. While traditional mathematical models have long supported supply chain management (SCM), AI-driven approaches, particularly machine learning and deep learning, are proving more effective in enhancing efficiency and decision-making.
Despite AI’s potential, existing research lacks a comprehensive mapping of its applications in the dairy supply chain, especially regarding adoption challenges and sustainability impacts. This study aims to address these gaps by systematically reviewing AI applications, evaluating their effectiveness, and identifying key factors influencing their adoption and future advancements.
Theoretical Framework and Methodology
Supply chain management integrates suppliers, factories, and distribution centers to streamline the movement of materials, products, and information. AI enhances this process by improving efficiency, reducing costs, and fostering collaboration.
Several theoretical frameworks support AI adoption in SCM:
- Resource-Based View (RBV): Highlights the importance of leveraging unique resources and capabilities.
- General Systems Theory (GST): Provides a holistic perspective on supply chain interactions.
- Transaction Cost Economics (TCE): Evaluates transaction costs to guide outsourcing and integration decisions.
- Porter’s Competitive Theory and Green SCM: Emphasize strategic positioning and sustainability.
AI further strengthens supply chain management through generative models for demand forecasting, transfer learning for adapting machine learning models to new datasets, and explainable AI for greater transparency in decision-making.
Following the PRISMA methodology, the researchers analyzed studies from Scopus and Web of Science, refining their keyword selection through bibliometric analysis in R Studio. The selection process included reviewing titles, abstracts, and full texts to assess relevance and potential bias, ensuring a structured synthesis of SCM trends and innovations.
AI in Dairy Supply Chain Management: Trends, Challenges, and Opportunities
The study highlights AI technologies such as artificial neural networks (ANN), machine learning, genetic algorithms (GA), and IoT-based monitoring systems, which enhance various aspects of dairy supply chain management. These technologies improve milk quality prediction, optimize resource allocation, and strengthen product traceability. AI also supports decision-making, production planning, logistics, and food safety, ultimately reducing costs and improving operational efficiency. Real-time monitoring and predictive analytics further help mitigate risks and support sustainability initiatives.
Bibliometric data shows a growing interest in AI applications within the dairy sector, with India, the United Kingdom, and Italy leading research contributions. Several factors drive AI adoption, including increased data availability, advancements in digital tools, market pressures, regulatory requirements, and interdisciplinary collaborations.
However, widespread adoption faces several barriers, particularly among small enterprises and in developing regions. Challenges include high implementation costs, lack of digital infrastructure, resistance to change, data privacy concerns, and technical complexity. Additionally, a shortage of AI specialists and limited economic incentives slow adoption efforts.
AI’s Role in Sustainability and Supply Chain Optimization
Despite these challenges, AI plays a critical role in improving sustainability by reducing waste, optimizing logistics, and minimizing environmental impact. Automated processes and predictive analytics enhance inventory management, food safety, and production efficiency. AI-driven sustainability initiatives also support local communities and promote eco-friendly practices within the dairy industry.
AI contributes to sustainability in several ways:
- Reducing waste through demand forecasting and smart inventory management.
- Optimizing logistics with AI-powered route planning, lowering emissions and transportation costs.
- Enhancing resource efficiency by improving water and energy usage in dairy production.
- Improving livestock health using AI-driven predictive models for disease detection and prevention.
- Strengthening supply chain transparency by integrating blockchain for traceability and fraud prevention.
Insights and Future Outlook
As the dairy supply chain grows more complex, AI, machine learning, and digital technologies continue to drive improvements in efficiency and sustainability. India, as the world’s largest milk producer, leads research in AI-driven dairy optimization. Technologies such as Tabu search and ant colony optimization enhance transportation efficiency, while deep learning models improve milk quality monitoring and contaminant detection. Blockchain and IoT play a key role in strengthening traceability, ensuring transparency, and reducing fraud.
Machine learning enhances demand forecasting, inventory management, and real-time monitoring, lowering operational costs and boosting supply chain resilience. The integration of advanced sensors, AI-powered route optimization, and workforce training is expected to further accelerate AI adoption in the dairy sector.
However, to fully realize AI’s benefits, economic and infrastructure challenges must be addressed. Overcoming stakeholder concerns, developing cost-effective solutions, and fostering industry-wide collaboration will be essential for maximizing AI’s impact.
Conclusion
AI is reshaping the dairy supply chain by optimizing logistics, quality control, and resource management. While challenges such as high costs, infrastructure limitations, and resistance to change persist, AI presents a significant opportunity for a more efficient and competitive dairy industry.
Future research should focus on AI’s long-term impact, ethical considerations, and interdisciplinary integration to support widespread adoption. Strategic implementation of AI-driven solutions will be key in shaping a resilient, transparent, and sustainable dairy supply chain.
Journal Reference
Serrano-Torres, G. J., López-Naranjo, A. L., Larrea-Cuadrado, P. L., & Mazón-Fierro, G. (2025). Transformation of the Dairy Supply Chain Through Artificial Intelligence: A Systematic Review. Sustainability, 17(3), 982. DOI:10.3390/su17030982 https://www.mdpi.com/2071-1050/17/3/982
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