A Comprehensive Synthesis of Theoretical and Applied Approaches to Modern Cold-Chain Logistics: Leveraging Digital Intelligence, Predictive Modeling, and Resilient Operations
DOI:
https://doi.org/10.37547/Keywords:
Cold-chain logistics, predictive modeling, reinforcement learning, blockchain, IoT, shelf-life prediction, supply chain resilienceAbstract
This article synthesizes contemporary theoretical developments and applied innovations in cold-chain logistics, integrating digital intelligence, predictive modeling, third-party logistics (3PL) strategies, and resilient operational frameworks. The objective is to construct a unified conceptual and practical architecture that reconciles demand forecasting, temperature and shelf-life prediction, resource scheduling, vehicle routing, warehouse orchestration, and digital transformation (including blockchain and IoT) under the specific operational constraints of perishable goods and pharmaceutical products. Drawing on empirical and methodological work spanning neural forecasting, Bi-LSTM and hybrid grey models, reinforcement learning for vehicle routing and warehouse control, Newtonian thermal models, and blockchain-enabled coordination, I map how these methods interrelate, where their complementarities and trade-offs lie, and how they must be combined to produce robust cold-chain performance. The structured synthesis advances a layered framework: (1) sensing and acquisition at the edge (temperature, humidity, product condition), (2) short- and medium-term demand forecasting and shelf-life estimation, (3) dynamic routing and scheduling under risk constraints, (4) warehouse and pick/pack optimization integrating hybrid simulation and reinforcement learning, (5) digital coordination and trust-building via blockchain, and (6) governance and resilience strategies grounded in contingent resource-based theory. For each layer I discuss algorithmic choices, data requirements, performance metrics, failure modes, and realistic implementation pathways. The article further elaborates limitations, including model-data mismatches, computational and integration costs, and regulatory and compliance challenges for pharmaceutical cold chains. Finally, I propose targeted avenues for empirical validation and phased deployment in urban and export-oriented cold-chain contexts. This synthesis is intended as a conceptual blueprint for researchers and practitioners aiming to design next-generation cold-chain systems that balance efficiency, compliance, and resilience.
Downloads
References
1.Abd-alrazaq, A.A., Alajlani, M., Alhuwail, D., Erbad, A., Giannicchi, A., Shah, Z., Hamdi, M., Househ, M. (2020). Blockchain technologies to mitigate COVID-19 challenges: a scoping review. Comput. Methods Progr. Biomed. Update 100001.
2.Albeyatti, A. (2018). White Paper: Medicalchain. MedicalChain self-publication.
3.Alonso, S.G., Arambarri, J., Lopez-Coronado, M., de la Torre Díez, I. (2019). Proposing new blockchain challenges in ehealth. J. Med. Syst., 43(3), 64.
4.Antipova, T., Rocha, A. (2019). Digital Science. Springer.
5.Babalola, A.O., Sundarakani, B., Ganesh, K. (2011). Cold chain logistics in the floral industry. Int. J. Enterprise Netw. Manag., 4(4), 400–413.
6.Bahga, A., Madisetti, V.K. (2016). Blockchain platform for industrial internet of things. J. Software Eng. Appl., 9(10), 533.
7.Bamakan, S.M.H., Faregh, N., ZareRavasan and Engineering, A. (2021). Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance. J. Comput. Des. Eng., 1–15.
8.Bamakan, S.M.H., Haddadpoor jahromi, M.J. (2021). Role of social responsibility in prevention of the COVID-19 outbreak from systems thinking perspective. Publ. Health, 190, e18.
9.Bamakan, S.M.H., Motavali, A., Bondarti, A.B. (2020). A survey of blockchain consensus algorithms performance evaluation criteria. Expert Syst. Appl., 113385.
10.Baysara, R.H. (2006). Cold chain management–an essential component of the global pharmaceutical supply chain. American Pharmaceutical Review, 9(1), 105–109.
11.Bedford, J., Enria, D., Giesecke, J., Heymann, D.L., Ihekweazu, C., Kobinger, G., Lane, H.C., Memish, Z., Oh, M.-d., Schuchat, A.J.T.L. (2020). COVID-19: towards controlling of a pandemic. Lancet, 395(10229), 1015–1018.
12.Ben-Daya, M., Hassini, E., Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. Int. J. Prod. Res., 57(15–16), 4719–4742.
13.Bishara, R.H. (2006). Cold chain management–an essential component of the global pharmaceutical supply chain. American Pharmaceutical Review, 9(1), 105–109.
14.Brandon-Jones, E., Squire, B., Autry, C.W., Petersen, K.J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73.
15.Brzozowska, A., Brzeszczak, A., Imiołczyk, J., Szymczyk, K. (2016). Managing cold supply chain. Proceedings of the 5th IEEE International Conference on Advanced Logistics and Transport, Kraków, Poland.
16.Bunakan, S.M.H. (2020). [See Bamakan entries for related work.]
17.Chaudhuri, A., Dukovska-Popovska, I., Subramanian, N., Chan, H.K., Bai, R. (2018). Decision-making in cold chain logistics using data analytics: a literature review. The International Journal of Logistics Management, 29(3), 839–861.
18.Chen, Y., Wu, Q., Shao, L. (2020). Urban cold-chain logistics demand predicting model based on improved neural network model. Int. J. Metrol. Qual. Eng., 2020, 2020003.
19.Chen, I.S., Fung, P.K., Yuen, S.S. (2019). Dynamic capabilities of logistics service providers: Antecedents and performance implications. Asia Pacific Journal of Marketing and Logistics, 31(4), 1058–1075.
20.Corey, L., Mascola, J.R., Fauci, A.S., Collins, F.S. (2020). A strategic approach to COVID-19 vaccine R&D. Science, 368(6494), 948–950.
21.Fan, Y., Chen, L., Shen, Z. (2022). Logistics Path Decision Optimization Method of Fresh Product Export Cold Chain Considering Transportation Risk. Comput. Intell. Neurosci., 2022, 8924938.
22.Huang, W., Wang, X., Xia, J., Li, Y., Zhang, L., Feng, H., Zhang, X. (2023). Flexible sensing enabled agri-food cold chain quality control: A review of mechanism analysis, emerging applications, and system integration. Trends Food Sci. Technol., 133, 189–204.
23.Iurii, K., André, L., Henrik, L. (2021). Real-time temperature prediction in a cold supply chain based on Newton’s law of cooling. Decision Support Systems, 141, 113451.
24.Leon, J., Li, Y., Martin, X., Calvet, L., Panadero, J., Juan, A. (2023). A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations. Algorithms, 16, 408.
25.Liu, P., Dong, L., Cao, A. (2020). Design of Medical Cold Chain Information Acquisition System Based on Linear Prediction. Wireless Personal Communications, 115, 1197–1209.
26.Lopes, C., Oliveira, A. (2024). Minimization of Costs with Picking and Storage Operations. Systems, 12, 158.
27.Ni, S., Peng, Y., Liu, Z. (2022). Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model. Comput. Commun., 10, 51–65.
28.Phiboonbanakit, T., Horanont, T., Huynh, V.-N., Supnithi, T. (2021). A Hybrid Reinforcement Learning-Based Model for the Vehicle Routing Problem in Transportation Logistics. IEEE Access, 9, 163325–163347.
29.Ren, X., Tan, J., Qiao, Q., Wu, L., Ren, L., Meng, L. (2022). Demand forecast and influential factors of cold chain logistics based on a grey model. Mathematical Biosciences and Engineering, 19, 7669–7686.
30.Tsironi, T., Dermesonlouoglou, E., Giannoglou, M., Gogou, E., Katsaros, G., Taoukis, P. (2017). Shelf-life prediction models for ready-to-eat fresh cut salads: Testing in real cold chain. International Journal of Food Microbiology, 240, 131–140.
31.Wang, H., Li, W., Li, M., Yang, X., Wang, Z., Zhao, Z., Wang, L. (2022). Intelligent selection of delivery parties for fresh agricultural product based on third-party logistics in smart city. Sustainable Energy Technologies and Assessments, 52, 102151.
32.Wazahat Ahmed Chowdhury. (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
33.Xu, R., Lan, H. (2020). Demand forecasting model of aquatic cold chain logistics based on GWO-SVM. Proceedings of the 8th International Symposium on Project Management, Beijing, China, 4–5 July 2020.
34.Zhang, Q., Li, N., Duan, J., Qin, J., Zhou, Y. (2024). Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing. Systems, 12, 133.
35.Zhang, X., Li, Z., Li, G. (2023). Impacts of blockchain-based digital transition on cold supply chains with a third-party logistics service provider. Transportation Research Part E: Logistics and Transportation Review, 170, 103014.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 John H. Mercer
This work is licensed under a Creative Commons Attribution 4.0 International License.
