Journal of Primeasia

Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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RESEARCH ARTICLE   (Open Access)

Industry 4.0 and Manufacturing Supply Chains: A Process-Oriented, Data-Driven Review

Md Sohel Masud 1*, Md. Fazle Alahi Bhuiyan 2

+ Author Affiliations

Journal of Primeasia 4 (1) 1-8 https://doi.org/10.25163/primeasia.4110842

Submitted: 29 May 2023 Revised: 07 August 2023  Published: 14 August 2023 


Abstract

Background: Manufacturing supply chains have long operated as loosely coordinated sequences of procurement, production, inventory, logistics, and distribution activities, often constrained by slow information flow and reactive decision-making. Industry 4.0 technologies, including the Internet of Things, artificial intelligence, big data analytics, cyber-physical systems, cloud computing, and digital twins, are reshaping this arrangement, though the manner and extent of that change is not always well specified in the literature.

Methods: This narrative review draws on peer-reviewed journal articles, conference papers, and academic reports published since 2018, retrieved from Scopus, Web of Science, IEEE Xplore, SpringerLink, and Google Scholar. A process-oriented analytical framework was used to map enabling technologies onto core supply chain functions, and findings were organized around four recurring performance dimensions: efficiency, flexibility, resilience, and sustainability.

Results: The reviewed evidence indicates that Industry 4.0 technologies meaningfully improve supply chain visibility, coordination, and forecasting accuracy. AI and machine learning strengthen demand planning, IoT and RFID enhance real-time tracking, and digital twins support scenario simulation and risk mitigation. These gains, however, are tempered by persistent barriers, including high implementation costs, cybersecurity exposure, interoperability challenges, and a shortage of adequately skilled workers, particularly among small and medium-sized enterprises.

Conclusion: Industry 4.0 is reorganizing manufacturing supply chains into more intelligent, adaptive systems, but realizing this potential appears to depend as much on organizational readiness, culture, training, and change management, as on the underlying technology itself. Future research would benefit from closer attention to scalable adoption pathways and sector-specific cybersecurity frameworks.

Keywords: Industry 4.0; Manufacturing Supply Chain; Data-Driven Decision-Making; Process-Oriented Framework; Smart Manufacturing

References


AlMetwally, S. A. H., Hassan, M. K., & Mourad, M. H. (2020). Real Time Internet of Things (IoT) Based Water Quality Management System. Procedia CIRP, 91, 478–485. https://doi.org/10.1016/j.procir.2020.03.107

Andronie, M., Lazaroiu, G., Iatagan, M., U?a, C., ?tefanescu, R., & Coco?atu, M. (2021). Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics, 10(20), 2497. https://doi.org/10.3390/electronics10202497

Andronie, M., Lazaroiu, G., ?tefanescu, R., U?a, C., & Dijmarescu, I. (2021). Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review. Sustainability, 13(10), 5495. https://doi.org/10.3390/su13105495

Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research, 58(11), 3330–3341. https://doi.org/10.1080/00207543.2019.1685705

Cedillo-Campos, M. G., González-Ramírez, R. G., Mejía-Argueta, C., & González-Feliu, J. (2020). Special issue: Data-driven decision making in supply chains. Computers & Industrial Engineering, 139, 106022. https://doi.org/10.1016/j.cie.2019.106022

Coito, T., Firme, B., Martins, M. S. E., Vieira, S. M., Figueiredo, J., & Sousa, J. M. C. (2021). Intelligent Sensors for Real-Time Decision-Making. Automation, 2(2), 62–82. https://doi.org/10.3390/automation2020004

Epiphaniou, G., Bottarelli, M., Al-Khateeb, H., Ersotelos, N. Th., Kanyaru, J., & Nahar, V. (2020). Smart Distributed Ledger Technologies in Industry 4.0: Challenges and Opportunities in Supply Chain Management (pp. 319–345). https://doi.org/10.1007/978-3-030-35746-7_15

Fatima, Z., Tanveer, M. H., Waseemullah, Zardari, S., Naz, L. F., Khadim, H., Ahmed, N., & Tahir, M. (2022). Production Plant and Warehouse Automation with IoT and Industry 5.0. Applied Sciences, 12(4), 2053. https://doi.org/10.3390/app12042053

Fatorachian, H., & Kazemi, H. (2021). Impact of Industry 4.0 on supply chain performance. Production Planning & Control, 32(1), 63–81. https://doi.org/10.1080/09537287.2020.1712487

Goli, A., Golmohammadi, A., & Edalatpanah, S. A. (2022). Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0. In A Roadmap for Enabling Industry 4.0 by Artificial Intelligence (pp. 43–55). Wiley. https://doi.org/10.1002/9781119905141.ch4

Gupta, S., Bag, S., Modgil, S., Beatriz Lopes de Sousa Jabbour, A., & Kumar, A. (2022). Examining the influence of big data analytics and additive manufacturing on supply chain risk control and resilience: An empirical study. Computers & Industrial Engineering, 172, 108629. https://doi.org/10.1016/j.cie.2022.108629

Hellani, H., Sliman, L., Samhat, A. E., & Exposito, E. (2021). On Blockchain Integration with Supply Chain: Overview on Data Transparency. Logistics, 5(3), 46. https://doi.org/10.3390/logistics5030046

Helo, P., & Shamsuzzoha, A. H. M. (2020). Real-time supply chain — A blockchain architecture for project deliveries. Robotics and Computer-Integrated Manufacturing, 63, 101909. https://doi.org/10.1016/j.rcim.2019.101909

Hughes, L., Dwivedi, Y. K., Rana, N. P., Williams, M. D., & Raghavan, V. (2022). Perspectives on the future of manufacturing within the Industry 4.0 era. Production Planning & Control, 33(2–3), 138–158. https://doi.org/10.1080/09537287.2020.1810762

Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450

Jahani, N., Sepehri, A., Vandchali, H. R., & Tirkolaee, E. B. (2021). Application of Industry 4.0 in the Procurement Processes of Supply Chains: A Systematic Literature Review. Sustainability, 13(14), 7520. https://doi.org/10.3390/su13147520

Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions. Applied Sciences, 11(12), 5725. https://doi.org/10.3390/app11125725

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study. Journal of Industrial Integration and Management, 7(1), 83–111. https://doi.org/10.1142/S2424862221300040

Kumar, N. M., Chand, A. A., Malvoni, M., Prasad, K. A., Mamun, K. A., Islam, F. R., & Chopra, S. S. (2020). Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids. Energies, 13(21), 5739. https://doi.org/10.3390/en13215739

Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2(1), 34–36. https://doi.org/10.1049/iet-cim.2020.0009

Li, W. (2022). Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining. Computational Intelligence and Neuroscience, 2022, 1–11. https://doi.org/10.1155/2022/4828108

Masip-Bruin, X., Marín-Tordera, E., Ruiz, J., Jukan, A., Trakadas, P., Cernivec, A., Lioy, A., López, D., Santos, H., Gonos, A., Silva, A., Soriano, J., & Kalogiannis, G. (2021). Cybersecurity in ICT Supply Chains: Key Challenges and a Relevant Architecture. Sensors, 21(18), 6057. https://doi.org/10.3390/s21186057

Mathur, A., Dabas, A., & Sharma, N. (2022). Evolution From Industry 1.0 to Industry 5.0. 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 1390–1394. https://doi.org/10.1109/ICAC3N56670.2022.10074274

Melnyk, S. A., Schoenherr, T., Speier-Pero, C., Peters, C., Chang, J. F., & Friday, D. (2022). New challenges in supply chain management: cybersecurity across the supply chain. International Journal of Production Research, 60(1), 162–183. https://doi.org/10.1080/00207543.2021.1984606

Pei Breivold, H. (2020). Towards factories of the future: migration of industrial legacy automation systems in the cloud computing and Internet-of-things context. Enterprise Information Systems, 14(4), 542–562. https://doi.org/10.1080/17517575.2018.1556814

Radanliev, P., De Roure, D., Nicolescu, R., Huth, M., & Santos, O. (2022). Digital twins: artificial intelligence and the IoT cyber-physical systems in Industry 4.0. International Journal of Intelligent Robotics and Applications, 6(1), 171–185. https://doi.org/10.1007/s41315-021-00180-5

Radanliev, P., De Roure, D., Page, K., Nurse, J. R. C., Mantilla Montalvo, R., Santos, O., Maddox, L., & Burnap, P. (2020). Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains. Cybersecurity, 3(1), 13. https://doi.org/10.1186/s42400-020-00052-8

Raja Santhi, A., & Muthuswamy, P. (2022). Pandemic, War, Natural Calamities, and Sustainability: Industry 4.0 Technologies to Overcome Traditional and Contemporary Supply Chain Challenges. Logistics, 6(4), 81. https://doi.org/10.3390/logistics6040081

Sedhom, B. E., El-Saadawi, M. M., El Moursi, M. S., Hassan, M. A., & Eladl, A. A. (2021). IoT-based optimal demand side management and control scheme for smart microgrid. International Journal of Electrical Power & Energy Systems, 127, 106674. https://doi.org/10.1016/j.ijepes.2020.106674

Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1), 53. https://doi.org/10.1186/s40537-020-00329-2

Sobb, T., Turnbull, B., & Moustafa, N. (2020). Supply Chain 4.0: A Survey of Cyber Security Challenges, Solutions and Future Directions. Electronics, 9(11), 1864. https://doi.org/10.3390/electronics9111864

Stergiou, C. L., & Psannis, K. E. (2022). Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments. Virtual Reality & Intelligent Hardware, 4(4), 279–291. https://doi.org/10.1016/j.vrih.2022.05.003

Wan, J., Al-awlaqi, M. A. A. H., Li, M., O'Grady, M., Gu, X., Wang, J., & Cao, N. (2018). Wearable IoT enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018(1), 298. https://doi.org/10.1186/s13638-018-1308-x

Zennaro, I., Finco, S., Calzavara, M., & Persona, A. (2022). Implementing E-Commerce from Logistic Perspective: Literature Review and Methodological Framework. Sustainability, 14(2), 911. https://doi.org/10.3390/su14020911