This article develops a comprehensive, publication-ready framework for managing pharmaceutical cold chain logistics with a focus on temperature integrity, risk monitoring, and low-carbon routing. Drawing strictly from the provided literature on environmental monitoring, Internet-of-Things (IoT) systems, radio-frequency identification (RFID), machine learning applications, and green logistics strategies (WHO, 2015; Yan & Lee, 2009; Aung & Chang, 2023; Tinytag, 2023; Mohanraj et al., 2019; Chowdhury, 2025; Tsang et al., 2018; MadgeTech, 2014; AKCP, 2023; FedEx, 2024; DHL, 2024; Jovanovic et al., 2022; Shi et al., 2022; Chen et al., 2021; Li & Li, 2023; Zhang et al., 2021; Guo et al., 2022; Liu et al., 2020; Ren et al., 2021; Song et al., 2020), the paper synthesizes empirical and theoretical insights into a unified model that addresses operational reliability, regulatory compliance, sustainability, and decision-making under uncertainty. The framework emphasizes four interlinked components: (1) rigorous fixed-area environmental monitoring aligned with international standards (WHO, 2015; MadgeTech, 2014), (2) IoT-enabled risk detection and data-driven alerting mechanisms (Tsang et al., 2018; Mohanraj et al., 2019; Yan & Lee, 2009), (3) application of machine learning for anomaly detection and quality assurance (Chowdhury, 2025; Ren et al., 2021), and (4) routing and scheduling optimization that internalizes carbon emissions and traffic dynamics (Shi et al., 2022; Chen et al., 2021; Guo et al., 2022; Liu et al., 2020). A detailed methodological approach is presented describing how systems integration, data governance, and multi-objective optimization can be operationalized in pharmaceutical distribution networks. The results section offers descriptive analyses of how each component contributes to reduced spoilage risk, improved traceability, and lower carbon footprints, supported by the literature. Limitations, such as data quality constraints, technology interoperability, and regulatory heterogeneity, are acknowledged and translated into a research agenda and actionable managerial recommendations. The article concludes by articulating how combining temperature-focused compliance measures with digital risk monitoring and low-carbon logistics strategies yields resilient, compliant, and sustainable pharmaceutical cold chains compatible with modern supply chain objectives (WHO, 2015; FedEx, 2024; DHL, 2024).

Pharmaceutical cold chain, temperature monitoring, IoT risk monitoring, RFID, machine learning, low-carbon routing

WHO, “Temperature and humidity monitoring systems for fixed storage areas,” Tech. Rep., 2015. https://cdn.who.int/media/docs/default-source/medicines/norms-and-standards/guidelines/distribution/trs961-annex9-supp6.pdf?sfvrsn=f7bf90112&download=true

Yan, B., & Lee, D., “Application of RFID in cold chain temperature monitoring system,” in 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, Sanya, China, 2009, pp. 258–261. https://doi.org/10.1109/CCCM.2009.5270408

Aung, M. M., & Chang, Y. S., “Cold chain monitoring tools,” in Cold Chain Management, 2023, pp. 77–91. https://doi.org/10.1007/978-3-031-09567-2_5

Tinytag, “Environmental monitoring in pharmaceutical warehouses,” 2023. https://www.geminidataloggers.com/info/temperature-rh-monitoring-pharmaceutical-warehouse

Mohanraj, K., Vijayalakshmi, S., Balaji, N., Chithrakkannan, R., & Karthikeyan, R., “Smart warehouse monitoring using IoT,” Int. J. Eng. Adv. Tech., vol. 8, no. 6, pp. 3597–3600, 2019. https://doi.org/10.35940/ijeat.F9355.088619

Chowdhury, Wazahat Ahmed. (2025). “Machine Learning in Cold Chain Logistics: Ensuring Compliance and Quality in Pharmaceutical Supply Chains.” International Journal of Medical Science and Public Health Research, 6(09), 40–45. https://doi.org/10.37547/ijmsphr/Volume06Issue09-05

Tsang, Y. P., Choy, K. L., Wu, C. H., Ho, G. T. S., Lam, C. H. Y., & Koo, P. S., “An internet of things (IoT)-based risk monitoring system for managing cold supply chain risks,” Ind. Manag. Data Syst., vol. 118, no. 7, pp. 1432–1462, 2018. https://doi.org/10.1108/IMDS-09-2017-0384

MadgeTech, “Warehouse monitoring and mapping – A step by step guide for good manufacturing practices,” 2014. https://www.valutemp.net/MadgeTech%20Warehouse%20Brochure-p.pdf

AKCP, “Wireless warehouse temperature monitoring,” 2023. https://www.akcp.com/solutions/warehouse-temperature-monitoring/

FedEx, “Real-time tracking with FedEx SenseAware,” 2024. https://www.fedex.com/en-us/senseaware.html

DHL, “Setting new standards in temperature controlled shipping - DHL air thermonet,” 2024. https://www.dhl.com/content/dam/dhl/global/dhl-global-forwarding/documents/pdf/dhl-glo-dgf-air-thermonet-brochure.pdf

Jovanovic, S., Pajić, V., Kilibarda, M., & Andrejić, M., “Organizacija vazdušnog transporta robe pod temperaturnim režimom,” in VI Medunarodna Naucna Konferencija Regionalni Razvoj i Prekogranična Saradnja, 2022, pp. 327–341.

DHL, “Ocean secure - Increasing your visibility and control,” 2019. https://www.dhl.com/content/dam/dhl/global/dhl-global-forwarding/documents/pdf/dhl-glo-dgf-ocean-secure-brochure-june19.pdf

Shi, Y.; Lin, Y.; Lim, M.; Tseng, M.; Tan, C.; Li, Y., “An intelligent green scheduling system for sustainable cold chain logistics,” Expert Syst. Appl., 2022, 209, 118378.

Chen, J.; Liao, W.; Yu, C., “Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission,” Comput. Ind. Eng., 2021, 161, 107663.

Li, D.; Li, K., “A multi-objective model for cold chain logistics considering customer satisfaction,” Alex. Eng. J., 2023, 67, 513–523.

Zhang, S.; Chen, N.; She, N.; Li, K., “Location optimization of a competitive distribution center for urban cold chain logistics in terms of low-carbon emissions,” Comput. Ind. Eng., 2021, 154, 107120.

Guo, X.; Zhang, W.; Liu, B., “Low-carbon routing for cold-chain logistics considering the time-dependent effects of traffic congestion,” Transp. Res. Part D Transp. Environ., 2022, 113, 103502.

Liu, G.; Hu, J.; Yang, Y.; Xia, S.; Lim, M., “Vehicle routing problem in cold Chain logistics: A joint distribution model with carbon trading mechanisms,” Resour. Conserv. Recycl., 2020, 156, 104715.

Ren, J.; Li, H.; Zhang, M.; Wu, C., “Massive-scale graph mining for e-commerce cold chain analysis and optimization,” Future Gener. Comput. Syst., 2021, 125, 526–531.

Song, M.; Li, J.; Han, Y.; Han, Y.; Liu, L.; Sun, Q., “Metaheuristics for solving the vehicle routing problem with the time windows and energy consumption in cold chain logistics,” Appl. Soft Comput., 2020, 95, 106561.

Maxwell, D., “Functional and systems aspects of the sustainable product and service development approach for industry,” Journal of Cleaner Production, 2006.

Salin, N. R., “A cold chain network for food exports to developing countries,” International Journal of Physical Distribution & Logistics Management, 2002.

Bishara, R., “Cold chain management - essential component of the global pharmaceutical supply chain,” January/February 2006. http://portugues.sensitech.com/assets/articles/lsbisharaapr.pdf

Joshi, R., “Indian cold chain : modelling the inhibitors,” British Food Journal, 2009.

James, S., “The food cold-chain and climate change,” Food Research International, Vol. 43 No. 7, 2010.