IOT, Blockchain, and AI Integration for Enhanced Traceability and Operational Performance in Food Supply Chains: A Systematic Review and TOE Framework Analysis in Thailand
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Abstract
Food safety incidents, food fraud, and increasing trust deficits are exerting pressure on food supply chains, thereby necessitating robust traceability capabilities as a competitive advantage. Relying on a single digital technology remains insufficient in scope. Therefore, the integration of the Internet of Things (IoT), Blockchain, and Artificial Intelligence (AI) is critically important for Thailand as a major agro-food exporter to achieve comprehensive supply chain transparency, reliable data, and timely predictive insights. However, adoption rates among small and medium-sized enterprises (SMEs) remain modest due to high costs, skills gaps, and inadequate digital infrastructure coverage. This systematic literature review employs PRISMA-guided procedures to synthesize both quantitative and qualitative peer-reviewed studies published between 2019 to 2025, examining the integrated application of all three technologies, including pairwise combinations, within food supply chain contexts. It utilizes the Technology–Organization–Environment (TOE) framework to analyze Thailand-specific enablers and barriers and to interpret the connections between enhanced traceability capabilities and measurable operational performance outcomes. The synthesis reveals that technology integration strengthens traceability through three core pillars: real-time data visibility from IoT sensing and tracking systems, tamper-resistant and verifiable records through blockchain, and provenance verification through AI analytics and pattern recognition. These elements collectively translate into tangible operational benefits, including expedited decision-making and product recall processes, more precise and efficient quality control mechanisms, and cost reductions through automation, waste minimization, and comprehensive risk management. Within the Thai context, key enablers include government policy support, established industry standards, and multi-stakeholder collaborative frameworks, while significant barriers encompass high initial investment requirements, uneven digital infrastructure distribution, skill development gaps, and regulatory compliance burdens—particularly challenging for small-scale operators. In conclusion, this review proposes a conceptual framework positioning traceability as a mediating mechanism linking technology integration with operational performance improvements in speed, quality, and cost reduction dimensions, and provides actionable policy recommendations regarding infrastructure investment and capability enhancement initiatives to accelerate the responsible and inclusive advancement of Thailand’s food industry in the digital era.
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References
Baker, J. (2011). The technology–organization–environment framework. In Information systems theory (pp. 121–125). Springer.
Bhat, M. A., Rather, M. Y., Singh, P., Hassan, S., & Hussain, N. (2025). Advances in smart food authentication for enhanced safety and quality. Trends in Food Science & Technology, 155, 104800.
Bryan, J. D., & Zuva, T. (2021). A review on TAM and TOE framework progression and how these models integrate. Advances in Science, Technology and Engineering Systems Journal, 6(3), 137–145.
Čapla, J., Zajác, P., & Čurlej, J. (2025). The current state of carbon footprint quantification and tracking in the agri-food industry. Scifood, 19, 110–127.
Chandran, P. J. I., Khalil, H. A., Hashir, P. K., & Veerasingam, S. (2025). Smart technologies in aquaculture: An integrated IoT, AI, and blockchain framework for sustainable growth. Aquacultural Engineering, 111, 102584.
Dhal, S. B., & Kar, D. (2025). Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: A comprehensive review. Discover Applied Sciences, 7, 75.
Ellahi, R. M., Wood, L. C., & Bekhit, A. E.-D. A. (2023). Blockchain-based frameworks for food traceability: A systematic review. Foods, 12(16), 3026.
Ellahi, R. M., Wood, L. C., Khan, M., & Bekhit, A. E.-D. A. (2025). Integrity challenges in Halal meat supply chain: Potential Industry 4.0 technologies as catalysts for resolution. Foods, 14(7), 1135.
El-tahlawy, A. S., Alawam, A. S., Rudayn, H. A., Allam, A. A., Mahmoud, R., Abd El-Raheem, H., & Alahmad, W. (2025). Advanced analytical and digital approaches for proactive detection of food fraud as an emerging contaminant threat. Talanta Open, 12, 100499.
Femimol, R., & Joseph, L. N. (2025). A comprehensive review of blockchain with artificial intelligence integration for enhancing food safety and quality control. Innovative Food Science and Emerging Technologies, 102, 104019.
Frikha, T., Ktari, J., Zalila, B., Ghorbel, O., & Ben Amor, N. (2023). Integrating blockchain and deep learning for intelligent greenhouse control and traceability. Alexandria Engineering Journal, 79, 259–273.
Halder, S., Islam, M. R., Mamun, Q., Mahboubi, A., Walsh, P., & Islam, M. Z. (2025). A comprehensive survey on AI-enabled secure social industrial Internet of Things in the agri-food supply chain. Smart Agricultural Technology, 11, 100902.
Hassoun, A. (2025). Food sustainability 4.0: Harnessing fourth industrial revolution technologies for sustainable food systems. Discover Food, 5, 171.
Hassoun, A., Kamiloglu, S., Garcia-Garcia, G., Parra-López, C., Trollman, H., Jagtap, S., Aadil, R. M., & Esatbeyoglu, T. (2023). Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0. Food Chemistry, 409, 135303.
Holzapfel, S., & Hampel-Milagrosa, A. (2020). Global and national food safety and quality standards: Implications and impacts for farmers in Thailand and India. In Sustainability standards and global governance: Experiences of emerging economies (pp. 163–186). Springer.
Issa, A., Mekanna, A. N., Doumit, J., & Bou-Mitri, C. (2024). Redefining food safety: The confluence of Web 3.0 and AI technologies in the meat supply chain—A systematic review. International Journal of Food Science and Technology, 59(8), 1–14.
Jiang, W., Liu, C., Liu, W., & Zheng, L. (2025). Advancements in intelligent sensing technologies for food safety detection. Research, 8, Article 0713.
Kanwal, N., Zhang, M., Zeb, M., Hussain, M., & Wang, D. (2025). Revolutionizing food safety in the airline industry: AI-powered smart solutions. Trends in Food Science & Technology, 159, 104970.
Kittipanya-ngam, P., & Tan, K. H. (2020). A framework for food supply chain digitalization: Lessons from Thailand. Production Planning & Control, 31(2-3), 158–172.
Liberty, J. T., Bromage, S., Peter, E., Ihedioha, O. C., Alsalman, F. B., & Odogwu, T. S. (2025). Smart technology for public health: Reshaping the future of food safety. Food Control, 176, 111378.
Liu, Z., Yu, X., Liu, N., Liu, C., Jiang, A., & Chen, L. (2025). Integrating AI with detection methods, IoT, and blockchain to achieve food authenticity and traceability from farm-to-table. Trends in Food Science & Technology, 158, 104925.
Monteiro, E. S., da Rosa Righi, R., Barbosa, J. L. V., & Alberti, A. M. (2021). APTM: A model for pervasive traceability of agrochemicals. Applied Sciences, 11(17), 8149.
Nawaz, A., Afzal, A., Khatibi, A., Shankar, A., Madan, H., Faisal, H. S., Shahbaz, A., Usman, I., Zulfiqar, N., Saeed, F., Ahmed, A., Imran, A., Afzaal, M., Karni, A., Ahmed, F., Akram, N., Rasheed, M., & Islam, F. (2025). Role of artificial intelligence in halal authentication and traceability: A concurrent review. Food Control, 169, 111003.
Qiao, J., Zhang, M., Qiu, L., Mujumdar, A. S., & Ma, Y. (2024). Visual early warning and prediction of fresh food quality deterioration: Research progress and application in supply chain. Food Bioscience, 58, 103671.
Rajan, S. S., & Wani, K. M. (2025). A review of smart food and packaging technologies: Revolutionizing nutrition and sustainability. Food and Humanity, 4, 100593.
Rajput, D. V., More, P. R., Adhikari, P. A., & Arya, S. S. (2025). Blockchain technology in the food supply chain: A way towards circular economy and sustainability. Sustainable Food Technology. Advance online publication.
Rossi, S., Gemma, S., Borghini, F., Perini, M., Butini, S., Carullo, G., & Campiani, G. (2025). Agri-food traceability today: Advancing innovation towards efficiency, sustainability, ethical sourcing, and safety in food supply chains. Trends in Food Science & Technology, 163, 105154.
Rui, F., & Sundram, V. P. K. (2024). Technological innovation for sustainable supply chain management in the food industry. Information Management and Business Review, 16(3), 892–903.
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.
Shahbazi, Z., & Byun, Y.-C. (2021). A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic. Electronics, 10(1), 41.
Singh, K. A., Patra, F., Ghosh, T., Mahnot, N. K., Dutta, H., & Duary, R. K. (2025). Advancing food systems with industry 5.0: A systematic review of smart technologies, sustainability, and resource optimization. Sustainable Futures, 9, 100694.
Sridhar, A., Ponnuchamy, M., Kumar, P. S., Kapoor, A., Vo, D.-V. N., & Rangasamy, G. (2023). Digitalization of the agro-food sector for achieving sustainable development goals: A review. Sustainable Food Technology, 1, 783–802.
Stranieri, S., Riccardi, F., Meuwissen, M. P. M., & Soregaroli, C. (2021). Exploring the impact of blockchain on the performance of agri-food supply chains. Food Control, 119, 107495.
Sun, F., Wang, P., Zhang, Y., & Kar, P. (2025). βFSCM: An enhanced food supply chain management system using hybrid blockchain and recommender systems. Blockchain: Research and Applications, 6, 100245.
Sunmola, F., Baryannis, G., Tan, A., Co, K., & Papadakis, E. (2025). Holistic framework for blockchain-based Halal compliance in supply chains enabled by artificial intelligence. Systems, 13(1), 21.
Surasak, T., Watakakosol, R., Ngarmnil, A., Nemoto, R., & Yamada, S. (2019). Thai agriculture products traceability system using blockchain and Internet of Things. International Journal of Advanced Computer Science and Applications, 10(9), 578–583.
Talpur, S., Abbas, A., Khan, N., Irum, S., & Ali, J. (2023). Improving opportunities in supply chain processes using the Internet of Things and blockchain technology. International Journal of Interactive Mobile Technologies (iJIM), 17(8), 23–38.
Tao, Q., Cai, Z., & Cui, X. (2023). A technological quality control system for rice supply chain. Food and Energy Security, 12(1), e382.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
Tsolakis, N., Schumacher, R., Dora, M., & Kumar, M. (2023). Artificial intelligence and blockchain implementation in supply chains: A pathway to sustainability and data monetisation? Annals of Operations Research, 327(1), 157–210.
Vilas-Boas, J. L., Rodrigues, J. J. P. C., & Alberti, A. M. (2023). Convergence of distributed ledger technologies with digital twins, IoT, and AI for fresh food logistics: Challenges and opportunities. Journal of Industrial Information Integration, 31, 100393.
Yang, Y., Song, Y., Duan, Y., Wang, X., Duan, X., Lu, W., Guo, Q., & Liu, Z. (2025). A blockchain and IPFS-based system for monitoring the geographical authenticity of Codonopsis pilosula. Food Bioscience, 66, 106091.
Yin, B., Tan, G., Muhammad, R., Liu, J., & Bi, J. (2025). AI-powered innovations in food safety from farm to fork. Foods, 14(11), 1973.
Yooyativong, T., & Kamyod, C. (2023). IoT technology and digital upskilling framework for farmers in the northern rural area of Thailand. Journal of Mobile Multimedia, 19(5), 1129–1152.
Zhang, Y., Wu, X., Ge, H., Jiang, Y., Sun, Z., Ji, X., Jia, Z., & Cui, G. (2023). A blockchain-based traceability model for grain and oil food supply chain. Foods, 12(17), 3235.
Zhou, X., Zhu, Q., & Xu, Z. (2022). The mediating role of supply chain quality management for traceability and performance improvement: Evidence among Chinese food firms. International Journal of Production Economics, 254, 108630.