2023
DOI: 10.1108/ijrdm-01-2023-0051
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Predictable inventory management within dairy supply chain operations

Abstract: PurposeWith the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further imp… Show more

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Cited by 8 publications
(3 citation statements)
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“…This indicates a growing recognition of the role of AI in enhancing the efficiency and responsiveness of agricultural supply chains. Huerta-Soto et al (2023) analyze the modernization of the dairy industry's supply chain, focusing on inventory management. The study examines how machine learning maximizes the movement of commodities and facilitates waste reduction and quality improvement, thereby reducing operational expenses.…”
Section: Evolution and Future Trends In Ai-based Predictive Analytics...mentioning
confidence: 99%
“…This indicates a growing recognition of the role of AI in enhancing the efficiency and responsiveness of agricultural supply chains. Huerta-Soto et al (2023) analyze the modernization of the dairy industry's supply chain, focusing on inventory management. The study examines how machine learning maximizes the movement of commodities and facilitates waste reduction and quality improvement, thereby reducing operational expenses.…”
Section: Evolution and Future Trends In Ai-based Predictive Analytics...mentioning
confidence: 99%
“…c) Data Pre-processing and Cleaning: Before applying ML algorithms, the collected data needs to undergo pre-processing and cleaning to ensure its quality and reliability. The methodology elaborates on the data preprocessing steps, including data transformation, handling missing values, outlier detection, and normalization [8]. Cleaning the dataset helps to remove noise and inconsistencies, ensuring that the ML models receive accurate and meaningful data.…”
Section: B) Data Collection and Sourcesmentioning
confidence: 99%
“…Reinforcement Learning (RL) in combination with more sophisticated ML techniques has already been applied successfully in supply chains of material distribution and inventory replacement in other industrial sectors (recent example, Cuartas and Aguilar, 2023;Geevers et al, 2023). Relevant to dairy farming, dairy processing supply chains have also seen a rise in employment of RL-based techniques in decision making over traditional mathematical model (recent example, Huerta-Soto et al, 2023). Compared to traditional mathematical models like dynamic programming, RL is scalable to large problems with computationally higher dimensions since it does not require computing the values of all state-action space.…”
Section: Methodological Approachesmentioning
confidence: 99%