Open Access
A Review of Smart Manufacturing Supply Chain Management Focusing on Automation Predictive Analytics Sustainability Resilience
4
Assistant Professor, Department of Computer Sciences and Applications, Mandsaur University, Mandsaur, India
Abstract
Smart Manufacturing Supply Chain Management (SM-SCM) is one of the most important parts of Industry 4.0. It uses smart digital technologies to make manufacturing networks more sustainable, resilient, and efficient. This review examines the fundamental concepts, data-driven supply chain functions, automation technologies, and core digital innovations that support modern manufacturing systems. It discusses the role of data in procurement, planning, production, inventory management, transportation, and customer service for enhancing real-time visibility and decision-making. The examination delves deeper into the topic of automation by way of smart factories, robots, cyber-physical systems (CPS), the Industrial Internet of Things (IIoT), PLCs, SCADA, and digital twin technologies. Analysis also takes a look at the ways in which important supporting technologies like blockchain, AI, ML, cloud, and the Internet of Things have impacted predictive analytics, safe data sharing, and smart manufacturing. The paper also highlights practical application scenarios in automotive, aerospace, electronic equipment, manufacturing equipment, and energy and process industries, demonstrating how these technologies improve productivity, flexibility, quality, and resource utilization. Finally, the review identifies current challenges and future opportunities for developing sustainable, resilient, and autonomous manufacturing supply chains, providing valuable insights for researchers, industry practitioners, and policymakers working toward next-generation smart manufacturing ecosystems.
Keywords
Smart Manufacturing,
Supply Chain Management,
Automation
References
S. Ren, Y. Zhang, Y. Liu, T. Sakao, D. Huisingh, and C. M. V. B. Almeida, “A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions,” J. Clean. Prod., 2019, doi: 10.1016/j.jclepro.2018.11.025.
D. Zimon, J. Tyan, and R. Sroufe, “Implementing sustainable supply chain management: Reactive, cooperative, and dynamic models,” Sustain., vol. 11, no. 24, 2019, doi: 10.3390/SU11247227.
S. P.Sivashanmugam, S. Kumara, A. Mohile, and D. R. Suram, “Improving Detection Accuracy of Phishing Attacks with Feature Selection and Machine learning Techniques in Cybersecurity,” in 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT), IEEE, Apr. 2026, pp. 1–6. doi: 10.1109/ETAACT69135.2026.11542138.
J. M. López-Sanz, A. Penelas-Leguía, P. Gutiérrez-Rodríguez, and P. Cuesta-Valiño, “Rural Tourism and the Sustainable Development Goals. A Study of the Variables That Most Influence the Behavior of the Tourist.,” Front. Psychol., vol. 12, p. 722973, 2021, doi: 10.3389/fpsyg.2021.722973.
P. R. Marapatla, “Next-Gen Enterprise BI : A Strategic Guide to AI-Infused Reporting Solutions,” TPM – Testing, Psychom. Methodol. Appl. Psychol., vol. 32, no. S7, pp. 248–262, 2025.
E. M. Frazzon, C. M. T. Rodriguez, M. M. Pereira, M. C. Pires, and I. Uhlmann, “TOWARDS SUPPLY CHAIN MANAGEMENT 4.0,” Brazilian J. Oper. Prod. Manag., 2019, doi: 10.14488/BJOPM.2019.v16.n2.a2.
A. Joon, S. A. Pahune, S. Mathur, and S. Rongala, “Designing Predictive Analytics Frameworks with ML for ERP-Supported E-Commerce Systems,” in 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), IEEE, May 2025, pp. 1–6. doi: 10.1109/ASSIC64892.2025.11158452.
S. K. Anumula, “Optimizing Supply Chain Management with AI-Powered Predictive Analytics,” J. Int. Commer. Law Technol., vol. 6, no. 1, pp. 244–252, Sep. 2025, doi: 10.61336/Jiclt/25-01-20.
A. A. Soni, M. Parikh, R. N. K. Dhenia, J. A. Soni, A. R. Jha, and S. M. Shah, “Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines,” in 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, Dec. 2025, pp. 638–644. doi: 10.1109/CICN67655.2025.11367872.
S. V. Yesha Patel, BT Prasanna, N Insozhan, “Graph Neural Networks with E-Commerce Security Through Elliptic Curve Digital Signature Algorithm,” 2026 3rd Int. Conf. Res. Methodol. Knowl. Manag. Artif. Intell. Telecommun. Eng., pp. 1–7, 2026.
J. B. Mehta, “AI-Driven Compliance Validation System And Framework for Multi-Cloud Enterprises,” 63/919,564, 2025
V. K. Sharma, “Federated Learning in Mobile and Edge Environments for Telecom Use Cases,” IJIRCT, vol. 10, no. 1, pp. 1–10, 2024.
K. Dixit, “Predictive Analytics in Business Intelligence for Sales Forecasting,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 03, no. 01, p. 981, Sep. 2023, doi: 10.48175/IJARSCT-12750G.
J. Huang and S. M. Li, “Data-Driven Analysis of Supply Chain Integration’s Impact on Procurement Performance in International EPC Projects,” Sustain., vol. 16, no. 23, 2024, doi: 10.3390/su162310729.
R. K. Kanneganti, “Security and Compliance Issues in Cloud-Based Deployments of Content and Workflow Management Systems,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 2, no. 01, pp. 131–138, Aug. 2024, doi: 10.58812/esiscs.v2i01.1122.
[16] H. Cañas, J. Mula, F. Campuzano-Bolarín, and R. Poler, “A conceptual framework for smart production planning and control in Industry 4.0,” Comput. Ind. Eng., vol. 173, p. 108659, Nov. 2022, doi: 10.1016/j.cie.2022.108659.
V. Gudur, C. Upadhyay, S. Sudarsan, R. Dandigam, A. Patel, and S. Tiwari, “Trust Deviation Metrics (TDM) for AI-Driven Cloud Orchestration,” in Proceedings of the 2026 9th International Conference on Information and Computer Technologies, New York, NY, USA: ACM, Mar. 2026, pp. 539–544. doi: 10.1145/3803291.3803293.
G. Atakok and M. Cun, “Management of the engineering data for manufacturing,” J. Eng. Res., vol. 10, no. 2, Part A, pp. 258–269, 2022, doi: https://doi.org/10.36909/jer.10233.
T. Shah, “Cloud-Based Data Warehousing for Marketing Agility: Lessons from FinTech Migrations to Snowflake and AWS,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, no. 4, pp. 1–11, 2024.
C. S. Pareek, “Chaos Testing: A Proactive Framework for System Resilience in Distributed Architectures,” Int. J. Sci. Res., vol. 13, no. 11, pp. 851–855, Nov. 2024, doi: 10.21275/SR241110081650.
C. Pellen et al., “Data management and sharing,” J. Clin. Epidemiol., vol. 180, Apr. 2025, doi: 10.1016/j.jclinepi.2025.111680.
I. Cho and Y. Ju, “Trend Analysis of Factory Automation Using Topic Modeling,” Processes, vol. 13, no. 7, p. 1952, Jun. 2025, doi: 10.3390/pr13071952.
D. Patel, “Hybrid Deep Learning Approaches for Personalized Product Recommendation in Online Retail Systems,” in 2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), IEEE, May 2026, pp. 1–6. doi: 10.1109/IRASET68627.2026.11538894.
H. Chen et al., “From Automation System to Autonomous System: An Architecture Perspective,” J. Mar. Sci. Eng., vol. 9, no. 6, p. 645, Jun. 2021, doi: 10.3390/jmse9060645.
L. I. Minchala, J. Peralta, P. Mata-Quevedo, and J. Rojas, “An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software,” Appl. Sci., vol. 10, no. 14, p. 4696, Jul. 2020, doi: 10.3390/app10144696.
S. K. Malaraju and S. K. Madishetty, “AI-Augmented Compiler Optimization for Energyefficient Software Execution on Embedded Systems,” in 2025 14th International Conference on System Modeling & Advancement in Research Trends (SMART), IEEE, Nov. 2025, pp. 1–6. doi: 10.1109/SMART66937.2025.11389313.
V. Chaturvedi, “Modern Software Development with Java, Spring Boot, and Python: A Survey of Frameworks and Best Practices,” ESP J. Eng. Technol. Adv., vol. 3, no. 4, pp. 188–197, 2023, doi: 10.56472/25832646/JETA-V3I8P121.
B. R. Ande, “Enhancing Cloud-Native AEM Deployments Using Kubernetes and Azure DevOps,” Int. J. Commun. Networks Inf. Secur., vol. 15, no. 8, 2023.
S. R. Chanthati, “Integrating Large Language Models (LLMs) in Cognitive Medical Robotics,” Zenodo.
M. Lanzini, I. Ferretti, and S. Zanoni, “Towards the Implementation and Integration of a Digital Twin in a Discrete Manufacturing Context,” Processes, vol. 12, no. 11, p. 2384, Oct. 2024, doi: 10.3390/pr12112384.
Mohit Mittal, “The Great Migration: Understanding the Cloud Revolution in IT,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 10, no. 6, pp. 2222–2228, Dec. 2024, doi: 10.32628/CSEIT2410612423.
S. D. Veeravalli, S. Balla Venkata, A. Ashirova, R. M S, S. Appachu Devanira Poovaiah, and D. Turaev, “Breaking the Swarm Paradigm: Single Candidate Optimization for Cloud Task Allocation,” in 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), 2025, pp. 1–7. doi: 10.1109/ISAC364032.2025.11156559.
S. Kakkar, B. Ojo, D. K. Rai, B. Mani, and A. Kakkar, “Supplier Performance Prediction in Big Data-Driven Smart Supply Chains Using AI-Based Machine Learning Models,” in 2026 World Conference on Computational Science and Technology (WcCST), IEEE, Mar. 2026, pp. 123–128. doi: 10.1109/WcCST67302.2026.11496273.
X. Li, D. Li, J. Wan, C. Liu, and M. Imran, “Adaptive Transmission Optimization in SDN-Based Industrial Internet of Things With Edge Computing,” IEEE Internet Things J., vol. 5, pp. 1351–1360, 2018, doi: 10.1109/JIOT.2018.2797187.
T. Suganuma, T. Oide, S. Kitagami, K. Sugawara, and N. Shiratori, “Multiagent-Based Flexible Edge Computing Architecture for IoT,” IEEE Netw., vol. 32, pp. 16–23, 2018, doi: 10.1109/MNET.2018.1700201.
S. Krishna Anumula, “Design-Based Supply Chain Operations Research Model: Fostering Resilience and Sustainability in Modern Supply Chains,” Comput. Sci. Inf. Technol. (CS IT), pp. 345–355, 2025, doi: 10.5121/csit.2025.151927.
S. Wang, L. Ouyang, Y. Yuan, X. Ni, X. Han, and F. Y. Wang, “Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 11, pp. 2266–2277, 2019, doi: 10.1109/TSMC.2019.2895123.
P. W. Khan and Y. Byun, “Secure Transactions Management Using Blockchain as a Service Software for the Internet of Things,” in Studies in Computational Intelligence, 2021, pp. 117–128. doi: 10.1007/978-3-030-64773-5_10.
S. Tyagi and A. Tyagi, “Evaluating the Performance with Deep Learning Techniques in Cybersecurity for Effective Threat Detection,” in Proceedings of International Conference on Information Technology and Artificial Intelligence, 2026, pp. 437–452. doi: 10.1007/978-981-96-8687-2_31.
C. Yang, W. Shen, and X. Wang, “The Internet of Things in Manufacturing: Key Issues and Potential Applications,” IEEE Syst. Man, Cybern. Mag., vol. 4, pp. 6–15, 2018, doi: 10.1109/MSMC.2017.2702391.
Y. Lu, X. Xu, and L. Wang, “Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios,” J. Manuf. Syst., vol. 56, no. July, pp. 312–325, Jul. 2020, doi: 10.1016/j.jmsy.2020.06.010.
D. Cheng et al., “Digital Twin-driven Inversion of Assembly Precision for Industrial Equipment: Challenges, Progress and Perspectives,” Chinese J. Mech. Eng., vol. 38, no. 1, p. 75, 2025, doi: 10.1186/s10033-025-01224-8.
A. Singh, R. Sampath, S. A. D. Raj, G. I. Mary, G. Aarthi, and R. K. M, “Federated Learning for Predictive Maintenance: Model Comparisons and Privacy Advantages,” in 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), 2024, pp. 1408–1415. doi: 10.1109/ICICNIS64247.2024.10823350.
P. H. Desai, P. Mahalle, and P. Chandre, “Intelligent Access Control Schemes for the Internet of Everything: A Survey of Techniques, Challenges, and Future Directions,” in World Conference on Information Systems for Business Management, Springer Nature Switzerland, 2026, pp. 95–105. doi: 10.1007/978-3-032-13196-6_9.
H. Guo, L. Song, F. Yang, and X. Guo, “Supply Chain Finance: A Strategic Enabler for Purchasing and Supply Chain Management,” IEEE Trans. Eng. Manag., vol. 72, pp. 4208–4224, 2025, doi: 10.1109/TEM.2025.3618762.
D. Kulasekara, D. Sandaruwan, T. Ekanayake, A. Perera, K. Wisenthige, and C. Aluthwala, “The Influence of IT Infrastructure and Supply Chain Flexibility on Supply Chain Performance in the Apparel Manufacturing Sector,” in 2025 International Research Conference on Smart Computing and Systems Engineering (SCSE), 2025, pp. 1–7. doi: 10.1109/SCSE65633.2025.11031051.
I. Kishor, U. Mamodiya, M. Sharma, G. Kumar, and P. Goyal, “Integrating Block Chain with IoT for Secure and Transparent Supply Chain Management in Sustainable Manufacturing,” in 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), 2025, pp. 1–6. doi: 10.1109/ICSIT65336.2025.11294611.
R. Bala, A. Kumar, and P. K. S. Nain, “Multi-Agent Based Smart System for Supply Chain Management,” in 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), 2024, pp. 1482–1486. doi: 10.1109/IC2PCT60090.2024.10486294.
A. O. Adewusi, A. M. Komolafe, E. Ejairu, I. A. Aderotoye, O. O. Abiona, and O. C. Oyeniran, “The Role of Predictive Analytics in Optimizing Supply Chain Resilience: A Review of Techniques and Case Studies,” Int. J. Manag. Entrep. Res., vol. 6, no. 3, pp. 815–837, Mar. 2024, doi: 10.51594/ijmer.v6i3.938.
R. Ejjami and Khaoula Boussalham, “Resilient Supply Chains in Industry 5.0: Leveraging AI for Predictive Maintenance and Risk Mitigation,” Int. J. Multidiscip. Res., vol. 6, no. 4, Jul. 2024, doi: 10.36948/ijfmr.2024.v06i04.25116.
A. Iftikhar, I. Ali, A. Arslan, and S. Tarba, “Digital Innovation, Data Analytics, and Supply Chain Resiliency: A Bibliometric-based Systematic Literature Review,” Ann. Oper. Res., vol. 333, no. 2–3, pp. 825–848, 2024, doi: 10.1007/s10479-022-04765-6.
Most read articles by the same author(s)
- Dr. Amelia R. Foster, AI-Driven Cloud-Native Intelligence for Cost-Efficient, Secure, and Domain-Specific Decision Systems: An Integrative Research Study Across Hybrid Cloud Optimization, Healthcare Analytics, Edge-IoT, and E-Learning , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Sachini Ekanayake, A Scalable Approach To Designing High-Availability Distributed Systems With Advanced Fault Mitigation Strategies , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dinesh Perera, Nethmi Fernando, AI-Enabled Test Case Generation and Optimization for Modern Software Development , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Andre Castillo, Role of Smart Digital Technologies in Enhancing Regulatory Alignment and Formal Documentation , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Rizky Pratama, Siti Rahmawati, CombiScale: A Large Language Model Framework for Scalable Combinatorial Constraint Solving , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Muhammad Arif Hidayat, Architectural Design and System-Level Solutions for Seamless Incorporation of Robotic Technologies into Existing Industrial Infrastructure Networks , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 06 (2026): Volume 03 Issue 06
- Dr. Eleanor M. Whitford, Deep Learning and Intelligent Control in High-Stakes Systems: An Integrative Research Study on Lung Cancer CT Diagnosis and AI-Enabled Electric Vehicle Grid Management , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Rico Fernandez, HARNESSING SOLAR ENERGY FOR COOLING: INNOVATIONS IN SOLAR THERMAL COOLING SYSTEMS , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Dr. Julian Thorne, The Interconnected Frontier of Systemic Risk: Integrating Cost-Benefit Analysis, Cybersecurity Governance, and Corporate Valuation in the Modern Regulatory Landscape , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Paul Hathaway, A Comparative Analysis of Data-Driven Decision Support Systems: Bridging Clinical Epidemiology, Public Health Informatics, And Predictive E-Commerce Analytics in The Era of Big Data , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
Similar Articles
- Aghasi Gevorgyan, Cybersecurity in Networks Supporting Card Payment Systems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Dr. Eleanor Whitmore, Cloud-Native Smart Health Platforms: Scalable Machine Learning Deployment for Cardiovascular Prediction through Heroku, Salesforce, and Urban Data Ecosystems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Ethan Williams, Dr. Olivia Carter, Dr. Liam Anderson, Autonomous Fault Management in Cloud Environments Through Deep Learning-Based Decision Making , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Theodore J. Blackmoor, An Intelligent Automation Paradigm For Behavior Driven Software Testing , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Elena V. Markovic, Dr. Omar N. Haddad, Integrated Predictive Intelligence for Critical Decision Systems: A Comparative Research Framework Linking Machine Learning in Residential Energy Management and Disease Risk Prediction , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Dr. Akmal Rakhimov, Role of Dashboard-Driven Insights in Client Management Documentation for Rural Lending Organizations , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Clara E. Whitmore, Artificial Intelligence for Resilient Decentralized Infrastructures: An Integrative Research Study on Hybrid Renewable Energy Management and Real-Time Digital Payment Fraud Detection , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Samuel T. Ridgeway, Factory-Grade GPU Diagnostic Automation in Digital Pathology and Computational Inference Systems: A Cross-Domain Theoretical and Applied Investigation , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Jean Paul Kazungu, Jean Pierre Ntayagabiri, Jeremie Ndikumagenge, M. Kokou Assogba, QUANTITATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE IN HOSPITAL MANAGEMENT: SYSTEMATIC REVIEW OF REAL-WORLD IMPLEMENTATIONS AND OUTCOMES (2019–2024) , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Jean Claude Ndayizeye, Analyzing Unseen Customer Attributes with Innovative Cohort Identification Techniques , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
You may also start an advanced similarity search for this article.