Decision Intelligence in Collaborative Intelligent Manufacturing of smart and sustainable materials: A Comprehensive Review of Artificial Intelligence, Optimization, Digital Twins, and Human-Centered Decision-Making
DOI:
https://doi.org/10.65904/3083-3604.2026.02.09Keywords:
Decision Intelligence, Collaborative Manufacturing, Industry 5.0, Artificial Intelligence, Optimization, Digital Twin, Human-Centered Manufacturing, Smart ManufacturingAbstract
The rapid transition from Industry 4.0 to Industry 5.0 has significantly increased the complexity of manufacturing systems while accelerating the development and deployment of smart and sustainable materials in advanced industrial applications. These emerging materials require intelligent decision-making approaches that integrate data, advanced analytics, optimization, digital twins, and human expertise throughout their design, production, monitoring, and lifecycle management. Although artificial intelligence, machine learning, digital twins, and mathematical optimization have independently advanced intelligent manufacturing, their isolated application often limits adaptability, explainability, and resilience. This study presents a systematic literature review of Decision Intelligence (DI) in collaborative intelligent manufacturing following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, synthesizing evidence from 140 peer-reviewed publications. The review comprehensively examines the evolution of Decision Intelligence, core enabling technologies, layered architectures, industrial applications, human-centered decision-making, implementation challenges, and emerging research trends. The findings indicate that effective Decision Intelligence requires the seamless integration of artificial intelligence, optimization, digital twins, knowledge graphs, explainable AI, and Human-in-the-Loop AI within a unified decision ecosystem that combines computational intelligence with human judgment. The proposed framework and research roadmap provide valuable theoretical insights and practical guidance for developing resilient, explainable, and human-centric Decision Intelligence systems that support the realization of Industry 5.0 manufacturing for smart and sustainable materials.
References
Abhilash, P. M., Luo, X., Liu, Q., Madarkar, R., & Walker, C. (2024). Towards next-gen smart manufacturing systems: The explainability revolution. Npj Advanced Manufacturing, 1(1), 8. https://doi.org/10.1038/s44334-024-00006-9
Adebayo, Y., Udoh, P., Kamudyariwa, X. B., & Osobajo, O. A. (2025). Artificial Intelligence in Construction Project Management: A Structured Literature Review of Its Evolution in Application and Future Trends. Digital, 5(3), 26. https://doi.org/10.3390/digital5030026
Ahmad, R., Tichadou, S., & Hascoet, J.-Y. (2017). A knowledge-based intelligent decision system for production planning. The International Journal of Advanced Manufacturing Technology, 89(5–8), 1717–1729. https://doi.org/10.1007/s00170-016-9214-z
Aissani, N., Bekrar, A., Trentesaux, D., & Beldjilali, B. (2012). Dynamic scheduling for multi-site companies: A decisional approach based on reinforcement multi-agent learning. Journal of Intelligent Manufacturing, 23(6), 2513–2529. https://doi.org/10.1007/s10845-011-0580-y
Akash Kadam, Diwakar Reddy Peddinti, & Aditya Gupta. (2025). The Evolution of Smart Factories: Integrating IOT and Machine Learning in Supply Chain and Manufacturing. Journal of Computer Science and Technology Studies, 7(5), 251–261. https://doi.org/10.32996/jcsts.2025.7.5.32
Alfaro-Viquez, D., Zamora-Hernandez, M., Fernandez-Vega, M., Garcia-Rodriguez, J., & Azorin-Lopez, J. (2025). A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions. Electronics, 14(4), 646. https://doi.org/10.3390/electronics14040646
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99, 101805. https://doi.org/10.1016/j.inffus.2023.101805
Almpani, S., & Stefaneas, P. (2026). Exploring the Role of Argumentation in Human–Centered Artificial Intelligence: Communication, Collaboration, and Trust. International Journal on Artificial Intelligence Tools, 2650014. https://doi.org/10.1142/S0218213026500144
Al-Siah, H., & Fioravanti, A. (2023). BIM and BPMN 2.0 Integration for Interoperability Challenge in Construction Industry. In E. Arbizzani, E. Cangelli, C. Clemente, F. Cumo, F. Giofrè, A. M. Giovenale, M. Palme, & S. Paris (Eds.), Technological Imagination in the Green and Digital Transition (pp. 227–235). Springer International Publishing. https://doi.org/10.1007/978-3-031-29515-7_21
Bakirtzis, G. A., Biskas, P. N., & Chatziathanasiou, V. (2012). Generation Expansion Planning by MILP considering mid-term scheduling decisions. Electric Power Systems Research, 86, 98–112. https://doi.org/10.1016/j.epsr.2011.12.008
Balaha, F., Albinali, H., Alrabiah, H., Ali, M., & Bahroun, Z. (2025). An analytical review of data integration for decision support in smart manufacturing. Decision Analytics Journal, 17, 100647. https://doi.org/10.1016/j.dajour.2025.100647
Barata, J., & Kayser, I. (2024). How will the digital twin shape the future of industry 5.0? Technovation, 134, 103025. https://doi.org/10.1016/j.technovation.2024.103025
Bitencourt, J., Wooley, A., & Harris, G. (2025). Verification and validation of digital twins: A systematic literature review for manufacturing applications. International Journal of Production Research, 63(1), 342–370. https://doi.org/10.1080/00207543.2024.2357741
Buggineni, V., Chen, C., & Camelio, J. (2024a). Enhancing manufacturing operations with synthetic data: A systematic framework for data generation, accuracy, and utility. Frontiers in Manufacturing Technology, 4, 1320166. https://doi.org/10.3389/fmtec.2024.1320166
Buggineni, V., Chen, C., & Camelio, J. (2024b). Enhancing manufacturing operations with synthetic data: A systematic framework for data generation, accuracy, and utility. Frontiers in Manufacturing Technology, 4, 1320166. https://doi.org/10.3389/fmtec.2024.1320166
Callaghan, V., Clarke, G., Colley, M., & Hagras, H. (2001). A Soft-Computing Distributed Artificial Intelligence Architecture for Intelligent Buildings. In V. Loia & S. Sessa (Eds.), Soft Computing Agents (Vol. 75, pp. 117–145). Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-1815-4_5
Carter, O. B. J., Loft, S., & Visser, T. A. W. (2024). Meaningful Communication but not Superficial Anthropomorphism Facilitates Human-Automation Trust Calibration: The Human-Automation Trust Expectation Model (HATEM). Human Factors: The Journal of the Human Factors and Ergonomics Society, 66(11), 2485–2502. https://doi.org/10.1177/00187208231218156
Carvalho, N., Chaim, O., Cazarini, E., & Gerolamo, M. (2018). Manufacturing in the fourth industrial revolution: A positive prospect in Sustainable Manufacturing. Procedia Manufacturing, 21, 671–678. https://doi.org/10.1016/j.promfg.2018.02.170
Chan, F. T. S., & Ding, K. (2023). Industrial intelligence-driven production and operations management. International Journal of Production Research, 61(13), 4215–4219. https://doi.org/10.1080/00207543.2023.2207956
Chander, B., Pal, S., De, D., & Buyya, R. (2022). Artificial Intelligence-based Internet of Things for Industry 5.0. In S. Pal, D. De, & R. Buyya (Eds.), Artificial Intelligence-based Internet of Things Systems (pp. 3–45). Springer International Publishing. https://doi.org/10.1007/978-3-030-87059-1_1
Chen, C., Zhao, K., Leng, J., Liu, C., Fan, J., & Zheng, P. (2025). Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities. Robotics and Computer-Integrated Manufacturing, 94, 102982. https://doi.org/10.1016/j.rcim.2025.102982
Chen, J., Lim, C. P., Tan, K. H., Govindan, K., & Kumar, A. (2025). Artificial intelligence-based human-centric decision support framework: An application to predictive maintenance in asset management under pandemic environments. Annals of Operations Research, 350(2), 493–516. https://doi.org/10.1007/s10479-021-04373-w
Chen, S.-C., Chen, H.-M., Chen, H.-K., & Li, C.-L. (2024). Multi-Objective Optimization in Industry 5.0: Human-Centric AI Integration for Sustainable and Intelligent Manufacturing. Processes, 12(12), 2723. https://doi.org/10.3390/pr12122723
Chirumalla, K., Oghazi, P., Nnewuku, R. E., Tuncay, H., & Yahyapour, N. (2025). Critical factors affecting digital transformation in manufacturing companies. International Entrepreneurship and Management Journal, 21(1), 54. https://doi.org/10.1007/s11365-024-01056-3
Crespi, N., Drobot, A. T., & Minerva, R. (2023). The Digital Twin: What and Why? In N. Crespi, A. T. Drobot, & R. Minerva (Eds.), The Digital Twin (pp. 3–20). Springer International Publishing. https://doi.org/10.1007/978-3-031-21343-4_1
Debnath, A., Faisal, S. M. F., Mural, H., Hossain, Md. M., & Hasan, M. (2025a). Aggregate Production Planning Optimization: Comparing the Efficiency of Particle Swarm Optimization and Simulated Annealing Algorithms. 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1–6. https://doi.org/10.1109/ECCE64574.2025.11013082
Debnath, A., Faisal, S. M. F., Mural, H., Hossain, Md. M., & Hasan, M. (2025b). Aggregate Production Planning Optimization: Comparing the Efficiency of Particle Swarm Optimization and Simulated Annealing Algorithms. 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1–6. https://doi.org/10.1109/ECCE64574.2025.11013082
Dehghan, S., Sattarpanah Karganroudi, S., Echchakoui, S., & Barka, N. (2025). The Integration of Additive Manufacturing into Industry 4.0 and Industry 5.0: A Bibliometric Analysis (Trends, Opportunities, and Challenges). Machines, 13(1), 62. https://doi.org/10.3390/machines13010062
Denno, P. (2024). Cognitive work in future manufacturing systems: Human-centered AI for joint work with models. Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science, 27(2), 71–82. https://doi.org/10.3233/JID-230035
Dey, P., Mural, H., Dash, A., Kabir, Md. F., Nuva, T. J., & Islam Seyam, M. J. (2024, December 21). Evaluation of Strategies for Eradicating Supply Chain Disruption in Steel and Rolling Industries in Bangladesh by MCDM Approaches. Proceedings of the International Conference on Industrial Engineering and Operations Management. 7th Bangladesh International Conference on Industrial Engineering and Operations Management, AIUB, Dhaka, Bangladesh. https://doi.org/10.46254/BA07.20240058
Dhanda, M., Rogers, B. A., Hall, S., Dekoninck, E., & Dhokia, V. (2025). Reviewing human-robot collaboration in manufacturing: Opportunities and challenges in the context of industry 5.0. Robotics and Computer-Integrated Manufacturing, 93, 102937. https://doi.org/10.1016/j.rcim.2024.102937
Ding, K., Chan, F. T. S., Zhang, X., Zhou, G., & Zhang, F. (2019). Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors. International Journal of Production Research, 57(20), 6315–6334. https://doi.org/10.1080/00207543.2019.1566661
El Kalach, F., Yousif, I., Wuest, T., Sheth, A., & Harik, R. (2025). Cognitive manufacturing: Definition and current trends. Journal of Intelligent Manufacturing, 36(6), 3695–3715. https://doi.org/10.1007/s10845-024-02429-9
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review. Journal of Manufacturing Systems, 39, 79–100. https://doi.org/10.1016/j.jmsy.2016.03.001
Fanfani, M., Palesi, L. A. I., & Nesi, P. (2026). Human-Centered AI for Decision Support Systems: A Systematic Review of Application Domains, Architecture Designs, Current Trends and Future Directions. Big Data and Cognitive Computing, 10(6), 186. https://doi.org/10.3390/bdcc10060186
Gálvez Del Postigo Gallego, L., Lazarova‐Molnar, S., Del Real Torres, A., & Acevedo Galicia, L. E. (2025). Decision Support Within Digital Twins in Manufacturing Ecosystems: A Review. IET Collaborative Intelligent Manufacturing, 7(1), e70041. https://doi.org/10.1049/cim2.70041
Glikson, E., & Woolley, A. W. (2020). Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
González Rodríguez, G., Gonzalez-Cava, J. M., & Méndez Pérez, J. A. (2020). An intelligent decision support system for production planning based on machine learning. Journal of Intelligent Manufacturing, 31(5), 1257–1273. https://doi.org/10.1007/s10845-019-01510-y
Grieves, M. W. (2023). Digital Twins: Past, Present, and Future. In N. Crespi, A. T. Drobot, & R. Minerva (Eds.), The Digital Twin (pp. 97–121). Springer International Publishing. https://doi.org/10.1007/978-3-031-21343-4_4
Hananto, A. L., Tirta, A., Herawan, S. G., Idris, M., Soudagar, M. E. M., Djamari, D. W., & Veza, I. (2024). Digital Twin and 3D Digital Twin: Concepts, Applications, and Challenges in Industry 4.0 for Digital Twin. Computers, 13(4), 100. https://doi.org/10.3390/computers13040100
Handoko, H., Indrajit, R. E., Mantoro, T., & Santoso, H. (2025). Transforming Educational Decision-Making through Human-Centered Information Systems. 2025 IEEE 11th International Conference on Computing, Engineering and Design (ICCED), 1–5. https://doi.org/10.1109/ICCED68324.2025.11324833
Haque, B. M. T., & Rahman, Md. A. (2025). Enterprise-Scale Risk Decision Intelligence Systems Using Large Language Models (LLMs) For Automated Governance, Compliance, and Policy Enforcement. American Journal of Interdisciplinary Studies, 06(03), 254–305. https://doi.org/10.63125/te050f20
Hartikainen, M., Spurava, G., & Väänänen, K. (2024). Human-AI Collaboration in Smart Manufacturing: Key Concepts and Framework for Design. In F. Lorig, J. Tucker, A. Dahlgren Lindström, F. Dignum, P. Murukannaiah, A. Theodorou, & P. Yolum (Eds.), Frontiers in Artificial Intelligence and Applications. IOS Press. https://doi.org/10.3233/FAIA240192
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16(1), 45–74. https://doi.org/10.1007/s12559-023-10179-8
Huang, H., Zheng, X., Yang, Y., Liu, J., Liu, W., & Wang, J. (2020). An integrated architecture for intelligence evaluation of automated vehicles. Accident Analysis & Prevention, 145, 105681. https://doi.org/10.1016/j.aap.2020.105681
Hussain, J., Lee, C.-C., & Lev, B. (2025). Optimizing AI-based emission reduction efficiency and subsidies in supply chain management: A sensitivity-based approach with duopoly game dynamics. Journal of Cleaner Production, 494, 144991. https://doi.org/10.1016/j.jclepro.2025.144991
Infant, S. S., Vickram, S., Saravanan, A., Mathan Muthu, C. M., & Yuarajan, D. (2025). Explainable artificial intelligence for sustainable urban water systems engineering. Results in Engineering, 25, 104349. https://doi.org/10.1016/j.rineng.2025.104349
Javaid, M., Haleem, A., & Suman, R. (2023). Digital Twin applications toward Industry 4.0: A Review. Cognitive Robotics, 3, 71–92. https://doi.org/10.1016/j.cogr.2023.04.003
Jiang, Z., Ma, N., Yao, X., Zhou, J., Wang, K., Xie, T., & Meng, J. (2026). From smart manufacturing in Industry 4.0 to industrial metaverse for Industry 5.0. International Journal of Computer Integrated Manufacturing, 39(4–5), 610–641. https://doi.org/10.1080/0951192X.2025.2556440
Kalinaki, K., Yahya, U., Malik, O. A., & Lai, D. T. C. (2024). A Review of Big Data Analytics and Artificial Intelligence in Industry 5.0 for Smart Decision-Making: In A. Hassan, P. K. Dutta, S. Gupta, E. Mattar, & S. Singh (Eds.), Advances in Business Information Systems and Analytics (pp. 24–47). IGI Global. https://doi.org/10.4018/979-8-3693-2647-3.ch002
Karkaria, V., Tsai, Y.-K., Chen, Y.-P., & Chen, W. (2025). An optimization-centric review on integrating artificial intelligence and digital twin technologies in manufacturing. Engineering Optimization, 57(1), 161–207. https://doi.org/10.1080/0305215X.2024.2434201
Kerrouchi, S., Aghezzaf, E.-H., & Cottyn, J. (2024). Production digital twin: A systematic literature review of challenges. International Journal of Computer Integrated Manufacturing, 37(10–11), 1168–1193. https://doi.org/10.1080/0951192X.2024.2314792
Khan, M. I., Yasmeen, T., Khan, M., Hadi, N. U., Asif, M., Farooq, M., & Al-Ghamdi, S. G. (2025). Integrating industry 4.0 for enhanced sustainability: Pathways and prospects. Sustainable Production and Consumption, 54, 149–189. https://doi.org/10.1016/j.spc.2024.12.012
Kim, D. B., Bajestani, M. S., Lee, J. Y., Shin, S.-J., Kim, G.-Y., Sajadieh, S. M. M., & Noh, S. (2025). Human-in-the-loop in smart manufacturing (H-SM): A review and perspective. Journal of Manufacturing Systems, 82, 178–199. https://doi.org/10.1016/j.jmsy.2025.05.020
Kumar, V., Prakash, R. R., & Pratihar, D. K. (2025). Intelligent ankle-foot prosthetics: From engineering fundamentals to integrated artificial intelligence systems. In Advancing Healthcare through Decision Intelligence (pp. 127–147). Elsevier. https://doi.org/10.1016/B978-0-443-26480-1.00008-4
Langås, E. F., Zafar, M. H., & Sanfilippo, F. (2026). Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: A step towards sustainable manufacturing. Journal of Intelligent Manufacturing, 37(3), 999–1022. https://doi.org/10.1007/s10845-025-02580-x
Lawniczak, A. T., & Di Stefano, B. N. (2010). Computational intelligence-based architecture for cognitive agents. Procedia Computer Science, 1(1), 2227–2235. https://doi.org/10.1016/j.procs.2010.04.249
Lee, D., & Cheng, C.-C. (2016). Energy savings by energy management systems: A review. Renewable and Sustainable Energy Reviews, 56, 760–777. https://doi.org/10.1016/j.rser.2015.11.067
Leng, J., Zheng, K., Li, R., Chen, C., Wang, B., Liu, Q., Chen, X., & Shen, W. (2026). AIGC-empowered smart manufacturing: Prospects and challenges. Robotics and Computer-Integrated Manufacturing, 97, 103076. https://doi.org/10.1016/j.rcim.2025.103076
Leng, J., Zhu, X., Huang, Z., Li, X., Zheng, P., Zhou, X., Mourtzis, D., Wang, B., Qi, Q., Shao, H., Wan, J., Chen, X., Wang, L., & Liu, Q. (2024). Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges. Journal of Manufacturing Systems, 73, 349–363. https://doi.org/10.1016/j.jmsy.2024.02.010
Li, H., Lu, Z., Zhang, Z., & Tanasescu, C. (2025). How does artificial intelligence affect manufacturing firms’ energy intensity? Energy Economics, 141, 108109. https://doi.org/10.1016/j.eneco.2024.108109
Li, H., & Tian, F. (2026). Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework. Group Decision and Negotiation, 35(2), 26. https://doi.org/10.1007/s10726-026-09980-1
Li, P., Zhang, Y., Yin, L., Yang, M., & Jiang, P. (2026). Smart manufacturing service towards industry 5.0: Framework, characteristics and industrial case studies. The International Journal of Advanced Manufacturing Technology, 143(9–10), 4595–4621. https://doi.org/10.1007/s00170-026-17743-0
Li, X., Gu, X., Huang, S., Wang, B., Leng, J., & Wang, L. (2026). Smart reconfigurable manufacturing towards Industry 5.0. Journal of Manufacturing Systems, 86, 178–202. https://doi.org/10.1016/j.jmsy.2026.02.023
Luu, N. T. M., Pham, T. H., Siriwardana, A., Nguyen, L. C., & Tran, D. L. A. (2025). A Bibliometric Review of Digitalization in Tourism Supply Chains in the Context of Industry 4.0. Strategic Change, 34(4), 577–595. https://doi.org/10.1002/jsc.2639
Lv, Z. (2023). Digital Twins in Industry 5.0. Research, 6, 0071. https://doi.org/10.34133/research.0071
Ma, M., Lin, W., Pan, D., Lin, Y., Wang, P., Zhou, Y., & Liang, X. (2018). Data and Decision Intelligence for Human-in-the-Loop Cyber-Physical Systems: Reference Model, Recent Progresses and Challenges. Journal of Signal Processing Systems, 90(8–9), 1167–1178. https://doi.org/10.1007/s11265-017-1304-0
Machado, C. G., Winroth, M. P., & Ribeiro Da Silva, E. H. D. (2020). Sustainable manufacturing in Industry 4.0: An emerging research agenda. International Journal of Production Research, 58(5), 1462–1484. https://doi.org/10.1080/00207543.2019.1652777
Mahmud, N., & Mural, H. (2026). Integrating ensemble machine learning with TOPSIS and VIKOR for inventory waste index prediction and driver prioritization: Evidence from Bangladesh. Waste Management Bulletin, 4(2), 100311. https://doi.org/10.1016/j.wmb.2026.100311
Martini, B., Bellisario, D., & Coletti, P. (2024). Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives. Sustainability, 16(13), 5448. https://doi.org/10.3390/su16135448
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025). Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0. Computers, Materials & Continua, 83(3), 3675–3712. https://doi.org/10.32604/cmc.2025.061411
Monferdini, L., Tebaldi, L., & Bottani, E. (2025). From Industry 4.0 to Industry 5.0: Opportunities, Challenges, and Future Perspectives in Logistics. Procedia Computer Science, 253, 2941–2950. https://doi.org/10.1016/j.procs.2025.02.018
Moosavi, S., Farajzadeh-Zanjani, M., Razavi-Far, R., Palade, V., & Saif, M. (2024). Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey. Electronics, 13(17), 3497. https://doi.org/10.3390/electronics13173497
Mural, H., Choudhury, I. A., Abid, Md. R., & Banik, S. C. (2026). Recent Advancements, Trends and Future Prospects of Upper Limb Prosthesis through 3D printing- A Systematic Review. Journal of Sustainable Smart Materials and Structural Systems, 2, 1–23. https://doi.org/10.65904/3083-3604.2026.02.01
Nazir, J., & Das, P. K. (2025). A systematic literature review on factors of perception impacting entrepreneurial success based on PRISMA framework. Journal of Innovation and Entrepreneurship, 14(1), 49. https://doi.org/10.1186/s13731-025-00491-y
Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: An architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2), 143–161. https://doi.org/10.1016/S0167-9236(01)00141-5
Nguyen, D. H., Nguyen, D. P. T., Ta, Q. C., Tran, K. D., & Tran, K. P. (2026). Human-Centered Explainable Anomaly Detection in Smart Manufacturing: Bridging AI and Human Decision-Making in Industry 5.0. In K. P. Tran (Ed.), Human-Centered Explainable Anomaly Detection for Smart Manufacturing in Industry 5.0 (pp. 93–108). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-13657-2_5
Nguyen, H. D., & Tran, K. P. (2023). Artificial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges. In K. P. Tran (Ed.), Artificial Intelligence for Smart Manufacturing (pp. 5–33). Springer International Publishing. https://doi.org/10.1007/978-3-031-30510-8_2
Nikiforidis, K., Kyrtsoglou, A., Vafeiadis, T., Kotsiopoulos, T., Nizamis, A., Ioannidis, D., Votis, K., Tzovaras, D., & Sarigiannidis, P. (2025). Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0. ICT Express, 11(1), 135–148. https://doi.org/10.1016/j.icte.2024.12.001
Nilsson, J., Javed, S., Albertsson, K., Delsing, J., Liwicki, M., & Sandin, F. (2024). AI Concepts for System of Systems Dynamic Interoperability. Sensors, 24(9), 2921. https://doi.org/10.3390/s24092921
Oraei Zare, S., Saghafian, B., & Shamsai, A. (2012). Multi-objective optimization for combined quality–quantity urban runoff control. Hydrology and Earth System Sciences, 16(12), 4531–4542. https://doi.org/10.5194/hess-16-4531-2012
Pan, Y., Huang, Z., Lev, B., Xu, L., & Olson, D. (2026). A literature review on the artificial intelligence in manufacturing systems under industry 5.0. International Journal of Production Research, 64(12), 5120–5161. https://doi.org/10.1080/00207543.2026.2623537
Panigutti, C., Beretta, A., Fadda, D., Giannotti, F., Pedreschi, D., Perotti, A., & Rinzivillo, S. (2023a). Co-design of Human-centered, Explainable AI for Clinical Decision Support. ACM Transactions on Interactive Intelligent Systems, 13(4), 1–35. https://doi.org/10.1145/3587271
Panigutti, C., Beretta, A., Fadda, D., Giannotti, F., Pedreschi, D., Perotti, A., & Rinzivillo, S. (2023b). Co-design of Human-centered, Explainable AI for Clinical Decision Support. ACM Transactions on Interactive Intelligent Systems, 13(4), 1–35. https://doi.org/10.1145/3587271
Pant, R., Singh, R., Gehlot, A., & Thakur, A. K. (2025). A Systematic Review of Emerging Industry 5.0 Technologies: Enhancing Agile Manufacturing for Sustainability. Operations Research Forum, 6(1), 29. https://doi.org/10.1007/s43069-025-00427-y
Parra-Martinez, F. A., Desmet, O. A., & Wai, J. (2023). The Evolution of Intelligence: Analysis of the Journal of Intelligence and Intelligence. Journal of Intelligence, 11(2), 35. https://doi.org/10.3390/jintelligence11020035
Patel, N. S., & Lim, J. T. (2025). Critical design futures thinking and GenerativeAI: A Foresight 3.0 approach in higher education to design preferred futures for the industry. Foresight, 27(2), 380–402. https://doi.org/10.1108/FS-11-2023-0228
Pradhan, A. K., Kumar, D., Mishra, M. K., & Singh, M. K. (2025). Edge, Fog, and Cloud Computing in Industry 5.0. In I. Sarkar, A. Hazra, & P. Maurya (Eds.), Industry 5.0 (pp. 1–27). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-87837-4_1
Protyai, Md. I. H., Shariar, R., & Mural, H. (2026). Digital Twin Integration in Project Life Cycle Management—A Review. Engineering Reports, 8(3), e70668. https://doi.org/10.1002/eng2.70668
Puthanveettil Madathil, A., Luo, X., Liu, Q., Walker, C., Madarkar, R., & Qin, Y. (2025). A review of explainable artificial intelligence in smart manufacturing. International Journal of Production Research, 63(23), 8654–8697. https://doi.org/10.1080/00207543.2025.2513574
Rahaman, Md. T., Pranta, A. D., & Ahmed, S. (2025). Transitioning from Industry 4.0 to Industry 5.0 for Sustainable and Additive Manufacturing of Clothing: Framework, Case Studies, Recent Advances, and Future Prospects. Materials Circular Economy, 7(1), 20. https://doi.org/10.1007/s42824-025-00176-7
Rajyakalshmi, G., Jishnu Prian, G., Srihari Reddy, P., Indumati, S., & Sreenivasulu, M. (2026). Industry 5.0: A New Era of Sustainable and Human-Centered Production. In Y. Kumar Singla, A. Kumar, & M. R. Maughan (Eds.), Digital Manufacturing in Industry 5.0 (pp. 453–467). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-91500-0_19
Rathor, S. K., & Saxena, D. (2020). Energy management system for smart grid: An overview and key issues. International Journal of Energy Research, 44(6), 4067–4109. https://doi.org/10.1002/er.4883
Rejeb, A., Rejeb, K., Zrelli, I., & Süle, E. (2025). Industry 5.0 as seen through its academic literature: An investigation using co-word analysis. Discover Sustainability, 6(1), 307. https://doi.org/10.1007/s43621-025-01166-0
Ren, M., Chen, N., & Qiu, H. (2023). Human-machine Collaborative Decision-making: An Evolutionary Roadmap Based on Cognitive Intelligence. International Journal of Social Robotics, 15(7), 1101–1114. https://doi.org/10.1007/s12369-023-01020-1
Rijwani, T., Kumari, S., Srinivas, R., Abhishek, K., Iyer, G., Vara, H., Dubey, S., Revathi, V., & Gupta, M. (2025). Industry 5.0: A review of emerging trends and transformative technologies in the next industrial revolution. International Journal on Interactive Design and Manufacturing (IJIDeM), 19(2), 667–679. https://doi.org/10.1007/s12008-024-01943-7
Rojas, L., Peña, Á., & Garcia, J. (2025). AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management. Applied Sciences, 15(6), 3337. https://doi.org/10.3390/app15063337
Rožanec, J. M., Novalija, I., Zajec, P., Kenda, K., Tavakoli Ghinani, H., Suh, S., Bian, S., Veliou, E., Papamartzivanos, D., Giannetsos, T., Menesidou, S. A., Alonso, R., Cauli, N., Meloni, A., Recupero, D. R., Kyriazis, D., Sofianidis, G., Theodoropoulos, S., Fortuna, B., … Soldatos, J. (2023). Human-centric artificial intelligence architecture for industry 5.0 applications. International Journal of Production Research, 61(20), 6847–6872. https://doi.org/10.1080/00207543.2022.2138611
Samal, U. (2025). Evolution of machine learning and deep learning in intelligent manufacturing: A bibliometric study. International Journal of System Assurance Engineering and Management, 16(9), 3134–3150. https://doi.org/10.1007/s13198-025-02846-w
Sanepalli, U. R. (2024). Enterprise Lakehouse Architecture for Customer Analytics: AI and Machine Learning - Synchronized Ingestion and Compute Optimization. World Journal of Advanced Research and Reviews, 23(2), 2949–2959. https://doi.org/10.30574/wjarr.2024.23.2.2418
Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022). Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access, 10, 84486–84517. https://doi.org/10.1109/ACCESS.2022.3197671
Sarkar, I., Kakarla, J. H. H., Hazra, A., Gupta, K., Kumari, P., & Munusamy, A. (2025). Machine Learning for Industry 5.0: A Survey. IEEE Internet of Things Journal, 1–1. https://doi.org/10.1109/JIOT.2025.3604413
Sartal, A., Bellas, R., Mejías, A. M., & García-Collado, A. (2020). The sustainable manufacturing concept, evolution and opportunities within Industry 4.0: A literature review. Advances in Mechanical Engineering, 12(5), 1687814020925232. https://doi.org/10.1177/1687814020925232
Saxena, V., Kumar, N., Manna, S., Rajput, S. K., Agarwal, K. L., Diwania, S., & Gupta, V. (2025). Modelling, solution and application of optimization techniques in HRES: From conventional to artificial intelligence. Applied Energy, 380, 125047. https://doi.org/10.1016/j.apenergy.2024.125047
Shabur, Md. A., Shahriar, A., & Ara, Mst. A. (2025). From automation to collaboration: Exploring the impact of industry 5.0 on sustainable manufacturing. Discover Sustainability, 6(1), 341. https://doi.org/10.1007/s43621-025-01201-0
Shahriar, K. A., Mural, H., & Hridoy, M. W. (2026a). Sustainable Supplier Selection of E‐Commerce Industry in Bangladesh: An Integrated TOPSIS‐AHP Approach. Engineering Reports, 8(2), e70636. https://doi.org/10.1002/eng2.70636
Shahriar, K. A., Mural, H., & Hridoy, M. W. (2026b). Sustainable Supplier Selection of E‐Commerce Industry in Bangladesh: An Integrated TOPSIS‐AHP Approach. Engineering Reports, 8(2), e70636. https://doi.org/10.1002/eng2.70636
Shapiro, J. F. (1993). Chapter 8 Mathematical programming models and methods for production planning and scheduling. In Handbooks in Operations Research and Management Science (Vol. 4, pp. 371–443). Elsevier. https://doi.org/10.1016/S0927-0507(05)80188-4
Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Silver, E. A. (1981). Operations Research in Inventory Management: A Review and Critique. Operations Research, 29(4), 628–645. https://doi.org/10.1287/opre.29.4.628
Singh, S., Mehla, S., Bhargava, S. K., & Ramakrishna, S. (2022). History and Evolution of Additive Manufacturing. In S. K. Bhargava, S. Ramakrishna, M. Brandt, & Pr. Selvakannan (Eds.), Additive Manufacturing for Chemical Sciences and Engineering (pp. 19–51). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2293-0_2
Spelt, P. F., Knee, H. E., & Glover, C. W. (1991). Hybrid artificial intelligence architecture for diagnosis and decision-making in manufacturing. Journal of Intelligent Manufacturing, 2(5), 261–268. https://doi.org/10.1007/BF01471174
Subrahmanyam, S. (2025). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: In V. Vyas, X. Bi, D. Pandey, Z. Xu, & B. K. Pandey (Eds.), Advances in Computational Intelligence and Robotics (pp. 319–344). IGI Global. https://doi.org/10.4018/979-8-3373-0442-7.ch021
Sun, X., & Song, Y. (2025). Unlocking the Synergy: Increasing productivity through Human-AI collaboration in the industry 5.0 Era. Computers & Industrial Engineering, 200, 110657. https://doi.org/10.1016/j.cie.2024.110657
Suri, S., Mokashi, S. P., Guntupalli, B. K. R., Kiran, K. R., Linkalapalli, K., & Chaudhary, V. (2025). Intelligent Computing Framework for Real-Time Decision-Making in Industry 5.0 Applications. 2025 3rd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), 1–7. https://doi.org/10.1109/ICAICCIT68829.2025.11434024
Szeszák, B. M., Kerékjártó, I. G., Soltész, L., & Galambos, P. (2025). Industrial Revolutions and Automation: Tracing Economic and Social Transformations of Manufacturing. Societies, 15(4), 88. https://doi.org/10.3390/soc15040088
Talukder, I. H. R., Mural, H., Faisal, S. M. F., Talukdar, A., Das, S. C., & Hassan, M. (2026). Autonomous Planetary Rover Navigation and Crater‐Based Map Augmentation for Path Planning and Rock Localization Using Space ROS. Engineering Reports, 8(3), e70681. https://doi.org/10.1002/eng2.70681
Tao, F., Li, Y., Wei, Y., Zhang, C., & Zuo, Y. (2025). Data–Model Fusion Methods and Applications Toward Smart Manufacturing and Digital Engineering. Engineering, 55, 36–50. https://doi.org/10.1016/j.eng.2024.12.034
Teoh, Y. K., Gill, S. S., & Parlikad, A. K. (2023). IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning. IEEE Internet of Things Journal, 10(3), 2087–2094. https://doi.org/10.1109/JIOT.2021.3050441
Trihandaru, S., Susetyo, Y. A., Parhusip, H. A., & Susanto, B. (2025). Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models. International Journal of Advances in Intelligent Informatics, 11(4), 602. https://doi.org/10.26555/ijain.v11i4.1676
Tudorache, L., Babur, Ö., Lucas, S. S., & Van Den Brand, M. (2025). Current approaches to digital twins in additive manufacturing: A systematic literature review. Progress in Additive Manufacturing, 10(12), 10819–10853. https://doi.org/10.1007/s40964-025-01262-7
Varma Alluri, A. K. K. (2026). Governed Agentic AI for Salesforce CRM Platforms: A Reference Architecture for Data Grounding, Decision Intelligence, Trust Controls, and Lifecycle Reliability. International Journal of Emerging Trends in Computer Science and Information Technology, 7, 374–382. https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P153
Vinitha, K., Ambrose Prabhu, R., Bhaskar, R., & Hariharan, R. (2020). Review on industrial mathematics and materials at Industry 1.0 to Industry 4.0. Materials Today: Proceedings, 33, 3956–3960. https://doi.org/10.1016/j.matpr.2020.06.331
Wang, H., Wang, C., Liu, Q., Zhang, X., Liu, M., Ma, Y., Yan, F., & Shen, W. (2024). A data and knowledge driven autonomous intelligent manufacturing system for intelligent factories. Journal of Manufacturing Systems, 74, 512–526. https://doi.org/10.1016/j.jmsy.2024.04.011
Wang, M., Tong, Y., Zhuang, C., & Du, X. (2025). Towards industry 5.0: A framework of reconfigurable matrix-structured manufacturing system. Journal of Manufacturing Systems, 82, 114–136. https://doi.org/10.1016/j.jmsy.2025.06.002
Wang, T., Liu, Z., Wang, L., Li, M., & Wang, X. V. (2025). A design framework for high-fidelity human-centric digital twin of collaborative work cell in Industry 5.0. Journal of Manufacturing Systems, 80, 140–156. https://doi.org/10.1016/j.jmsy.2025.02.018
Wang, Z., Yan, J., Yan, G., & Yu, B. (2025). Multi-scale control and action recognition based human-robot collaboration framework facing new generation intelligent manufacturing. Robotics and Computer-Integrated Manufacturing, 91, 102847. https://doi.org/10.1016/j.rcim.2024.102847
Xiang, W., Yu, K., Han, F., Fang, L., He, D., & Han, Q.-L. (2024). Advanced Manufacturing in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends. IEEE Transactions on Industrial Informatics, 20(2), 1055–1068. https://doi.org/10.1109/TII.2023.3274224
Xu, J., Sun, Q., Han, Q.-L., & Tang, Y. (2025). When Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing. IEEE/CAA Journal of Automatica Sinica, 12(3), 485–501. https://doi.org/10.1109/JAS.2025.125327
Yang, W., Wei, Y., Wei, H., Chen, Y., Huang, G., Li, X., Li, R., Yao, N., Wang, X., Gu, X., Amin, M. B., & Kang, B. (2023). Survey on Explainable AI: From Approaches, Limitations and Applications Aspects. Human-Centric Intelligent Systems, 3(3), 161–188. https://doi.org/10.1007/s44230-023-00038-y
Yanytska, L. (2025). The rise of human-centric manufacturing in the industry 5.0 era. The International Journal of Advanced Manufacturing Technology, 139(9–10), 5067–5077. https://doi.org/10.1007/s00170-025-16192-5
Yin, Y., Stecke, K. E., & Li, D. (2018). The evolution of production systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1–2), 848–861. https://doi.org/10.1080/00207543.2017.1403664
Yu, W., Ouyang, Z., Zhang, Y., Lu, Y., Wei, C., Tu, Y., & He, B. (2025). Research progress on the artificial intelligence applications in food safety and quality management. Trends in Food Science & Technology, 156, 104855. https://doi.org/10.1016/j.tifs.2024.104855
Zakoldaev, D. A., Shukalov, A. V., & Zharinov, I. O. (2019). From Industry 3.0 to Industry 4.0: Production modernization and creation of innovative digital companies. IOP Conference Series: Materials Science and Engineering, 560(1), 012206. https://doi.org/10.1088/1757-899X/560/1/012206
Zeng, H., Wang, J., Das, A., He, J., Han, K., Hu, H., & Sun, M. (2024). Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4107–4118. https://doi.org/10.1145/3637528.3671783
Zhai, B., Perez-Pozuelo, I., Clifton, E. A. D., Palotti, J., & Guan, Y. (2020). Making Sense of Sleep: Multimodal Sleep Stage Classification in a Large, Diverse Population Using Movement and Cardiac Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(2), 1–33. https://doi.org/10.1145/3397325
Zhang, J. (2025). The Application of Artificial Intelligence Technology in Human Centered Manufacturing in Industry 5.0. Scalable Computing: Practice and Experience, 26(3), 1242–1256. https://doi.org/10.12694/scpe.v26i3.4130
Zhang, L., Liu, J., & Zhuang, C. (2024). Digital Twin Modeling Enabled Machine Tool Intelligence: A Review. Chinese Journal of Mechanical Engineering, 37(1), 47. https://doi.org/10.1186/s10033-024-01036-2
Zhang, W., Li, H., Qian, L., & Wang, X. (2025). How intelligent manufacturing improves corporate ESG performance: A three-dimensional analysis based on “Environment,” “Society,” and “Governance.” Journal of Environmental Management, 380, 125171. https://doi.org/10.1016/j.jenvman.2025.125171
Zhao, J., Ren, R., Nie, Y., & Wang, D. (2025). Decision Intelligence Empowering Resource Management for 6G-V2X Communications. IEEE Internet of Things Magazine, 8(6), 88–96. https://doi.org/10.1109/MIOT.2025.3575883
