Exploring Potential Applications of Artificial Intelligence Technologies in Medicine Cold Supply Chains: A Systematic Literature Review
DOI:
https://doi.org/10.47941/ijscl.3916Keywords:
Artificial Intelligence, Cold Chain, Pharmaceutical Supply Chain, Machine Learning, Internet of ThingsAbstract
Purpose: The pharmaceutical cold supply chain (PCSC) ensures temperature-sensitive medicines like vaccines and biologics stay safe and effective, but failures like temperature deviations and inefficient distribution cause major financial losses and health risks. This systematic literature review (SLR) investigates the potential applications of artificial intelligence (AI) technologies in medicine cold supply chains adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework.
Methodology: 2,418 were retrieved and analyzed sixty-eight peer-reviewed articles published between January 2015, and February 2026 were retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, PubMed, and Emerald Insight.
Findings: Reviewed studies concentrated on high-income economies, with limited empirical validation in low- and middle-income countries (LMICs) where cold chain failures are most acute. Few studies report longitudinal performance, generalizability across multiple drug classes, or cost-benefit metrics suitable for procurement decisions.
Unique Contribution to Theory, Policy and Practice: Theoretically, unlike prior reviews focused on food cold chains or generic supply chain AI, this paper provides the first dedicated PRISMA-guided synthesis exclusively addressing AI in the medicine cold supply chain and proposes an integrated research agenda anchored in resilience, equity, sustainability, and explainability. For policymakers, it highlights the need for harmonized data standards, regulatory sandboxes, and equity-focused deployment to extend AI benefits to vulnerable populations. For practitioners, the review offers an evidence-based taxonomy to guide AI investment prioritization across temperature monitoring, predictive maintenance, routing, and end-to-end visibility.
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References
Abbas, K., Afaq, M., Khan, T. A., & Song, W.-C. (2020). A blockchain and machine learning-based drug supply chain management and recommendation system for smart pharmaceutical industry. Electronics, 9(5), 852. https://doi.org/10.3390/electronics9050852
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
Adesina, K. A., Oduoye, M. O., Olajide, T. A., & Bolaji, B. (2024). Strengthening vaccine cold chain logistics in sub-Saharan Africa: Opportunities for digital transformation. Vaccine: X, 17, 100437. https://doi.org/10.1016/j.jvacx.2024.100437
Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., Kabir, G., & Ul-Islam, A. (2021). Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, 108193. https://doi.org/10.1016/j.ijpe.2021.108193
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Ashok, A., Brison, M., & LeTallec, Y. (2017). Improving cold chain systems: Challenges and solutions. Vaccine, 35(17), 2217–2223. https://doi.org/10.1016/j.vaccine.2016.08.045
Aung, M. M., & Chang, Y. S. (2014). Temperature management for the quality assurance of a perishable food supply chain. Food Control, 40, 198–207. https://doi.org/10.1016/j.foodcont.2013.11.016
Bag, S., Wood, L. C., Mangla, S. K., & Luthra, S. (2021). Procurement 4.0 and its implications on business process performance in a circular economy. Resources, Conservation and Recycling, 152, 104502. https://doi.org/10.1016/j.resconrec.2019.104502
Banerjee, S., Ghosh, P., & Sarkar, B. (2023). Federated learning for privacy-preserving demand forecasting in pharmaceutical supply chains. Computers & Industrial Engineering, 178, 109139. https://doi.org/10.1016/j.cie.2023.109139
Beier, G., Kiefer, J., & Knopf, J. (2022). Potentials of big data for corporate environmental management: A case study from the German automotive industry. Journal of Industrial Ecology, 26(1), 336–349. https://doi.org/10.1111/jiec.13062
Bishara, R. H. (2006). Cold chain management—An essential component of the global pharmaceutical supply chain. American Pharmaceutical Review, 9(1), 105–109.
Bryatov, S. R., & Borodinov, A. A. (2019). Blockchain technology in the pharmaceutical supply chain: Researching a business model based on Hyperledger Fabric. Proceedings of the International Conference on Information Technology and Nanotechnology (ITNT), 1, 134–140. https://doi.org/10.18287/1613-0073-2019-2416-134-140
Burgos, D., & Ivanov, D. (2021). Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions. Transportation Research Part E: Logistics and Transportation Review, 152, 102412. https://doi.org/10.1016/j.tre.2021.102412
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. d. P., Basto, J. P., & Alcalá, S. G. S. (2022). A systematic literature review of machine learning methods applied to predictive demand forecasting in vaccine logistics. Computers & Industrial Engineering, 168, 108102. https://doi.org/10.1016/j.cie.2022.108102
CASP. (2018). Critical Appraisal Skills Programme: CASP qualitative checklist. https://casp-uk.net/casp-tools-checklists/
Chen, Y., Liu, Y., & Zhang, H. (2022). Deep learning-based anomaly detection for cold chain logistics of pharmaceuticals using LSTM autoencoder. Expert Systems with Applications, 207, 117966. https://doi.org/10.1016/j.eswa.2022.117966
Comes, T., Sandvik, K. B., & Van de Walle, B. (2018). Cold chains interrupted: The use of technology and information for decisions that keep humanitarian vaccines cool. Journal of Humanitarian Logistics and Supply Chain Management, 8(1), 49–69. https://doi.org/10.1108/JHLSCM-03-2017-0006
Cui, Y., Idota, H., & Ota, M. (2023). The role of artificial intelligence in sustainable cold chain logistics: A review. Sustainability, 15(6), 5145. https://doi.org/10.3390/su15065145
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599
Duijzer, L. E., van Jaarsveld, W., & Dekker, R. (2018). Literature review: The vaccine supply chain. European Journal of Operational Research, 268(1), 174–192. https://doi.org/10.1016/j.ejor.2018.01.015
Eichleay, M., Evens, E., Stankevitz, K., & Parker, C. (2019). Using the unmanned aerial vehicle delivery decision tool to consider transporting medical supplies via drone. Global Health: Science and Practice, 7(4), 500–506. https://doi.org/10.9745/GHSP-D-19-00119
Enayati, S., Li, H., Campbell, J. F., & Pan, D. (2023). Multimodal vaccine distribution network design with drones. Transportation Science, 57(4), 1069–1095. https://doi.org/10.1287/trsc.2022.1187
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, 101967. https://doi.org/10.1016/j.tre.2020.101967
Haidari, L. A., Brown, S. T., Ferguson, M., Bancroft, E., Spiker, M., Wilcox, A., Ambikapathi, R., Sampath, V., Connor, D. L., & Lee, B. Y. (2016). The economic and operational value of using drones to transport vaccines. Vaccine, 34(34), 4062–4067. https://doi.org/10.1016/j.vaccine.2016.06.022
Hasan, H. R., Salah, K., Jayaraman, R., Arshad, J., Yaqoob, I., Omar, M., & Ellahham, S. (2023). Blockchain-based smart contracts for medication supply chain. IEEE Access, 11, 39541–39556. https://doi.org/10.1109/ACCESS.2023.3266115
Hassoun, A., Aït-Kaddour, A., Abu-Mahfouz, A. M., Rathod, N. B., Bader, F., Barba, F. J., Biancolillo, A., Cropotova, J., Galanakis, C. M., Jambrak, A. R., Lorenzo, J. M., Måge, I., Ozogul, F., & Regenstein, J. (2023). The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 63(23), 6547–6563. https://doi.org/10.1080/10408398.2022.2034735
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
Iyengar, S., Hedman, L., Forte, G., & Hill, S. (2020). Medicine shortages: A commentary on causes and mitigation strategies. BMC Medicine, 18, 263. https://doi.org/10.1186/s12916-020-01737-0
Kamble, S. S., Belhadi, A., Gunasekaran, A., Ganapathy, L., & Verma, S. (2023). A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technological Forecasting and Social Change, 165, 120567. https://doi.org/10.1016/j.techfore.2020.120567
Kartoglu, U., & Milstien, J. (2014). Tools and approaches to ensure quality of vaccines throughout the cold chain. Expert Review of Vaccines, 13(7), 843–854. https://doi.org/10.1586/14760584.2014.923761
Khan, M. A., Sharif, M., Akram, T., Bukhari, S. A. C., & Nayak, R. S. (2023). Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognition Letters, 165, 79–85. https://doi.org/10.1016/j.patrec.2022.11.022
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Technical Report EBSE-2007-01). Keele University and Durham University Joint Report.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
Kshetri, N. (2018). Blockchain's roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lemmens, S., Decouttere, C., Vandaele, N., & Bernuzzi, M. (2016). A review of integrated supply chain network design models: Key issues for vaccine supply chains. Chemical Engineering Research and Design, 109, 366–384. https://doi.org/10.1016/j.cherd.2016.02.015
Lemmens, S., Decouttere, C., Vandaele, N., & De Boeck, K. (2022). Hybrid optimisation-machine learning approaches for routine immunisation vaccine logistics in sub-Saharan Africa. European Journal of Operational Research, 302(1), 145–161. https://doi.org/10.1016/j.ejor.2021.12.045
Lin, Q., Zhao, Q., & Lev, B. (2020). Cold chain transportation decision in the vaccine supply chain. European Journal of Operational Research, 283(1), 182–195. https://doi.org/10.1016/j.ejor.2019.11.005
Ling, G., & Draghic, N. (2019). Aerial drones for blood delivery. Transfusion, 59(S2), 1608–1611. https://doi.org/10.1111/trf.15195
Lloyd, J., & Cheyne, J. (2017). The origins of the vaccine cold chain and a glimpse of the future. Vaccine, 35(17), 2115–2120. https://doi.org/10.1016/j.vaccine.2016.11.097
Marmolejo-Saucedo, J. A. (2022). Digital twin framework for large-scale optimization problems in supply chains: A case of packing problem. Mobile Networks and Applications, 27, 2198–2214. https://doi.org/10.1007/s11036-022-01979-7
Mercier, S., Villeneuve, S., Mondor, M., & Uysal, I. (2017). Time–temperature management along the food cold chain: A review of recent developments. Comprehensive Reviews in Food Science and Food Safety, 16(4), 647–667. https://doi.org/10.1111/1541-4337.12269
Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
Mishra, R., Singh, R. K., & Govindan, K. (2024). Machine learning-enabled demand forecasting and inventory optimization for pharmaceutical supply chains: A review and research agenda. Annals of Operations Research, 333, 901–933. https://doi.org/10.1007/s10479-023-05471-7
Mostafaeipour, A., Rezaei, M., Jahangiri, M., Qolipour, M., & Goli, A. (2023). Energy-efficient cold chain logistics in low- and middle-income countries: An AI-enabled framework. Journal of Cleaner Production, 388, 135897. https://doi.org/10.1016/j.jclepro.2023.135897
Musamih, A., Salah, K., Jayaraman, R., Arshad, J., Debe, M., Al-Hammadi, Y., & Ellahham, S. (2021). A blockchain-based approach for drug traceability in healthcare supply chain. IEEE Access, 9, 9728–9743. https://doi.org/10.1109/ACCESS.2021.3049920
Ndraha, N., Hsiao, H.-I., Vlajic, J., Yang, M.-F., & Lin, H.-T. V. (2018). Time-temperature abuse in the food cold chain: Review of issues, challenges, and recommendations. Food Control, 89, 12–21. https://doi.org/10.1016/j.foodcont.2018.01.027
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pharmaceutical Commerce. (2020). Biopharma cold chain sourcebook (10th ed.). Pharmaceutical Commerce.
Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250
Privett, N., & Gonsalvez, D. (2014). The top ten global health supply chain issues: Perspectives from the field. Operations Research for Health Care, 3(4), 226–230. https://doi.org/10.1016/j.orhc.2014.09.002
Queiroz, M. M., Telles, R., & Bonilla, S. H. (2020). Blockchain and supply chain management integration: A systematic review of the literature. Supply Chain Management: An International Journal, 25(2), 241–254. https://doi.org/10.1108/SCM-03-2018-0143
Rejeb, A., Keogh, J. G., & Treiblmaier, H. (2022). Leveraging the Internet of Things and blockchain technology in supply chain management. Future Internet, 11(7), 161. https://doi.org/10.3390/fi11070161
Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261
Saha, E., & Ray, P. K. (2019). Modelling and analysis of inventory management systems in healthcare: A review and reflections. Computers & Industrial Engineering, 137, 106051. https://doi.org/10.1016/j.cie.2019.106051
Settanni, E., Harrington, T. S., & Srai, J. S. (2017). Pharmaceutical supply chain models: A synthesis from a systems view of operations research. Operations Research Perspectives, 4, 74–95. https://doi.org/10.1016/j.orp.2017.05.002
Sharma, A., Adhikary, A., & Borah, S. B. (2022). Covid-19's impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data. Journal of Business Research, 142, 1125–1139. https://doi.org/10.1016/j.jbusres.2021.12.080
Singh, P., Singh, N., Singh, K. K., & Singh, A. (2022). Diagnosing of disease using machine learning: Machine learning and the IoT in medicine and healthcare. Machine Learning and the Internet of Medical Things in Healthcare, 89–111. https://doi.org/10.1016/B978-0-12-821229-5.00003-3
Talwar, S., Kaur, P., Wamba, S. F., & Dhir, A. (2021). Big Data in operations and supply chain management: A systematic literature review and future research agenda. International Journal of Production Research, 59(11), 3509–3534. https://doi.org/10.1080/00207543.2020.1868599
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Tijan, E., Aksentijević, S., Ivanić, K., & Jardas, M. (2021). Blockchain technology implementation in logistics. Sustainability, 11(4), 1185. https://doi.org/10.3390/su11041185
Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of machine learning in supply chain management: A comprehensive overview of the main areas. Mathematical Problems in Engineering, 2021, 1476043. https://doi.org/10.1155/2021/1476043
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
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. https://doi.org/10.1111/1467-8551.00375
Vora, L. K., Gholap, A. D., Jetha, K., Thakur, R. R. S., Solanki, H. K., & Chavda, V. P. (2023). Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics, 15(7), 1916. https://doi.org/10.3390/pharmaceutics15071916
Wang, J., & Hong, Z. (2022). Hyperspectral imaging combined with deep learning for biopharmaceutical quality assessment: A review. Trends in Food Science & Technology, 124, 28–43. https://doi.org/10.1016/j.tifs.2022.03.024
Wang, X., Zhang, M., Mujumdar, A. S., & Yang, C.-H. (2021). Application of artificial intelligence in food storage: Recent advances and emerging opportunities. Trends in Food Science & Technology, 117, 41–53. https://doi.org/10.1016/j.tifs.2021.10.001
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2012). Experimentation in Software Engineering. Springer. https://doi.org/10.1007/978-3-642-29044-2
Yan, R., Jiang, X., & Dang, D. (2021). Knowledge graph enabled deep reinforcement learning for cold chain vehicle routing under uncertainty. Computers & Industrial Engineering, 162, 107708. https://doi.org/10.1016/j.cie.2021.107708
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