Smart Multi-Species Milk Bank System (SM²BS) Integrating Cold Chain Management, Digital Traceability, and Nutritional Profiling of Cow, Goat, Sheep, Mare, and Buffalo Milk
DOI:
https://doi.org/10.61230/reflection.v4i3.169Keywords:
Smart Multi-Species Milk Bank System, Cold Chain Management, Digital Traceability, Nutritional Profiling, Milk Supply Chain, Dairy InnovationAbstract
The Smart Multi-Species Milk Bank System (SM²BS) is proposed as an integrated digital platform to improve milk supply chain management through cold chain management, digital traceability, and nutritional profiling. This study adopts a qualitative approach involving literature review, observation of milk handling processes, and stakeholder discussions to identify operational challenges and develop a conceptual framework for managing cow, goat, sheep, mare, and buffalo milk. Building on the existing Smart Milk Bank System (SMBS) prototype and preliminary goat milk quality testing conducted with CV Cahaya Firdaus, the proposed system integrates batch identification, quality records, storage conditions, and comparative nutritional data. Its disruptive novelty lies in connecting multi-species nutritional intelligence with cold chain monitoring and end-to-end traceability within a unified digital ecosystem. The framework is expected to support improved transparency, quality management, inventory coordination, and informed decision-making across milk supply chains. However, the proposed multi-species functionality and its operational benefits require further implementation and validation. This study provides a conceptual foundation for developing a more integrated, data-driven milk bank system and strengthening dairy supply chain resilience.
References
Al-Khatib, M., Haji, M., Haouari, M., & Kharbeche, M. (2024). Building resilience in the infant formula milk supply chain. Food Control, 165, 110641. https://doi.org/10.1016/J.FOODCONT.2024.110641
Becchi, P. P., Rocchetti, G., & Lucini, L. (2025). Advancing dairy science through integrated analytical approaches based on multi-omics and machine learning. Current Opinion in Food Science, 63, 101289. https://doi.org/10.1016/J.COFS.2025.101289
Cromwell, J., Turkson, C., Dora, M., & Yamoah, F. A. (2025). Digital technologies for traceability and transparency in the global fish supply chains: A systematic review and future directions. Marine Policy, 178, 106700. https://doi.org/10.1016/j.marpol.2025.106700
Danet, A. F., & Badea, M. (2004). Industrial Applications. Encyclopedia of Analytical Science: Second Edition, 89–97. https://doi.org/10.1016/B0-12-369397-7/00160-6
Edo, G. I., Ali, A. B. M., Owheruo, J. O., Iwanegbe, I., Orogu, J. O., Yousif, E., Igbuku, U. A., Oghroro, E. E. A., Opiti, R. A., Essaghah, A. E. A., Makia, R. S., Umar, H., Ahmed, D. S., Alamiery, A. A., Aliyu, M. R., & Fuwape, I. A. (2026). The role of cold chain logistics in reducing postharvest losses. Next Energy, 12, 100697. https://doi.org/10.1016/j.nxener.2026.100697
Hutahuruk, M. B., Junaedi, A. T., Renaldo, N., Prayetno, M. P., Prihastomo, A. D., Andi, A., Putri, N. Y., Fransisca, L., Faruq, U., & Musa, S. (2026). Educational Strategies for Fortified Goat Milk Development Supported by Digital Financial Ecosystems. Reflection: Education and Pedagogical Insights, 3(1), 19–24. https://doi.org/10.61230/reflection.v3i1.143
Iyer, P., & Robb, D. (2025). Cold chain optimisation models: A systematic literature review. Computers & Industrial Engineering, 204, 110972. https://doi.org/10.1016/J.CIE.2025.110972
Junaedi, A. T., Panjaitan, H. P., Renaldo, N., Nyoto, N., Jahrizal, J., Dalil, M., Koto, J., Musa, S., Wahid, N., Veronica, K., & Faruq, U. (2025). Smart Processing Machines and Business Efficiency in Goat Milk Agro-Enterprises. Luxury: Landscape of Business Administration, 3(2), 88–97. https://doi.org/10.61230/luxury.v3i2.137
Kelly, A. L. (2007). Milk. Cheese Problems Solved, 1–10. https://doi.org/10.1533/9781845693534.1
Malik, M., Gahlawat, V. K., Mor, R. S., & Singh, M. K. (2024). Unlocking dairy traceability: Current trends, applications, and future opportunities. Future Foods, 10, 100426. https://doi.org/10.1016/J.FUFO.2024.100426
Nyoto, N., Renaldo, N., Jahrizal, J., Aziz, A., Dalil, M., Junaedi, A. T., Yusrizal, Y., Hajjah, A., Musa, S., & Cecilia, C. (2026). Digital Transformation of Goat Milk Supply Chains through an Integrated Smart Cold-Chain Logistics System. Journal of Applied Business and Technology, 7(2), 133–143. https://doi.org/10.35145/re8a7652
Prihastomo, A. D., Renaldo, N., Junaedi, A. T., Panjaitan, H. P., Nyoto, N., Hutahuruk, M. B., Faruq, U., Prayetno, M. P., Wati, Y., & Fransisca, L. (2026). Value Co-Creation and Digital Marketplace Strategy in Enhancing Commercialization Performance, An Educational Perspective from Fortified Goat Milk Entrepreneurship. Reflection: Education and Pedagogical Insights, 3(1), 32–40. https://doi.org/10.61230/reflection.v3i1.151
Renaldo, N., Junaedi, A. T., Suhardjo, S., Aziz, A., Dalil, M., Siregar, E., Jahrizal, J., Faruq, U., Koto, J., & Veronica, K. (2026). Beyond Cold Storage and Smart Milk Bank as a Disruptive Supply Chain Innovation for Strengthening National Dairy Industry Resilience. Journal of Applied Business and Technology, 7(2), 144–154. https://doi.org/10.35145/b277se34
Yang, M., Işık, C., & Yan, J. (2023). Analysis of collaborative control of dairy product supply chain quality based on evolutionary games: Perspectives from government intervention and market failure. Heliyon, 9(12), e23024. https://doi.org/10.1016/J.HELIYON.2023.E23024
Yar, M. S., Ibeogu, I. H., Regmi, A., Zhang, N., & Li, C. (2025). Advances in intelligent time-temperature indicators for cold chain monitoring: mechanisms, challenges, and applications. Trends in Food Science & Technology, 163, 105128. https://doi.org/10.1016/j.tifs.2025.105128
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