International Journal of Value Engineering

AI-driven sustainable operations management in manufacturing supply chains: A systematic review, research gap analysis, and strategic framework for future directions

Attia Hussien Gomaa

Mechanical Engineering Department, Faculty of Engineering. Shubra, Benha University

Keywords: artificial intelligence; AI-driven sustainable operations management; manufacturing supply chains; sustainable manufacturing; digital transformation; supply chain resilience

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Abstract

Artificial Intelligence (AI) is reshaping sustainable operations management by helping manufacturing supply chains become more intelligent, adaptive, resilient, and resource-efficient. As manufacturers pursue operational excellence while addressing environmental and social sustainability objectives, AI technologies are increasingly used to improve decision-making, process efficiency, resource utilization, and supply chain resilience. However, the existing literature remains fragmented across AI technologies, operational functions, sustainability dimensions, and industrial contexts, limiting theoretical integration and a comprehensive understanding of how AI capabilities enable sustainable operational transformation. This study addresses this gap through a systematic literature review and research gap analysis of AI-driven sustainable operations management in manufacturing supply chains, covering peer-reviewed studies published between 2016 and 1 June 2026. The review traces the intellectual development of the field, identifies dominant research themes, examines AI applications across manufacturing and supply chain operations, and evaluates their contributions to operational, environmental, economic, and social sustainability. The findings indicate a shift from isolated AI applications toward integrated intelligent operational ecosystems in which AI, the Internet of Things (IoT), digital twins, cyber-physical systems, and advanced analytics support predictive intelligence, autonomous decision-making, adaptive production, real-time operational visibility, resource optimization, and resilient supply chain coordination. Based on the identified research gaps, this study proposes an AI-Driven Sustainable Operations Management Strategic Framework integrating AI capabilities, organizational readiness, digital infrastructure, operational transformation mechanisms, sustainability enablers, and multidimensional performance outcomes. The framework provides a unified basis for understanding AI-enabled sustainable value creation and establishes a structured agenda for future research, while offering strategic insights for researchers, practitioners, and policymakers seeking to advance sustainable digital transformation and strengthen the long-term resilience, competitiveness, and sustainability of manufacturing supply chains.

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References

[1] Caiado, R.G.G., Scavarda, L.F., Gavião, L.O., Ivson, P., de Mattos Nascimento, D.L. and Garza-Reyes, J.A., 2021. A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management. International journal of production economics, 231(1), p.107883.

[2] Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y. and Wang, X., 2016. Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Transactions on Smart Grid, 8(4), pp.1943-1955.

[3] Chauhan, S., Singh, R., Gehlot, A., Akram, S.V., Twala, B. and Priyadarshi, N., 2022. Digitalization of supply chain management with industry 4.0 enabling technologies: a sustainable perspective. Processes, 11(1), p.96.

[4] Choi, T.M., Kumar, S., Yue, X. and Chan, H.L., 2022. Disruptive technologies and operations management in the Industry 4.0 era and beyond. Production and operations management, 31(1), pp.9-31.

[5] Ding, K., Chan, F.T., Zhang, X., Zhou, G. and 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), pp.6315-6334.

[6] Dyba, W. and De Marchi, V., 2022. On the road to Industry 4.0 in manufacturing clusters: the role of business support organisations. Competitiveness Review: An International Business Journal, 32(5), pp.760-776.

[7] Eunike, A., Wang, K.J., Chiu, J. and Hsu, Y., 2022. Real-time resilient scheduling by digital twin technology in a flow-shop manufacturing system. Procedia CIRP, 107(1), pp.668-674.

[8] Fatorachian, H. and Kazemi, H., 2021. Impact of Industry 4.0 on supply chain performance. Production Planning & Control, 32(1), pp.63-81.

[9] Felsberger, A. and Reiner, G., 2020. Sustainable industry 4.0 in production and operations management: a systematic literature review. Sustainability, 12(19), p.7982.

[10] Frazzon, E.M., Albrecht, A., Pires, M., Israel, E., Kück, M. and Freitag, M., 2018. Hybrid approach for the integrated scheduling of production and transport processes along supply chains. International Journal of Production Research, 56(5), pp.2019-2035.

[11] Frazzon, E.M., Rodriguez, C.M.T., Pereira, M.M., Pires, M.C. and Uhlmann, I., 2019. Towards supply chain management 4.0. Brazilian Journal of Operations & Production Management, 16(2), pp.180-191.

[12] Gomaa, A.H., 2025a. LSS 4.0: A Conceptual Framework for Integrating Lean Six Sigma and Industry 4.0 for Smart Manufacturing Excellence. Indian Journal of Management and Language (IJML), 5(1), pp. 8-29.

[13] Gomaa, A.H., 2025b. SCM 4.0 Excellence: A Strategic Framework for Smart and Competitive Supply Chains. International Journal of Management and Humanities (IJMH), 11(8), pp. 24-44.

[14] Gomaa, A., 2025c. Applications of Industry 4.0 in Smart Manufacturing (Manufacturing 4.0): A Comprehensive Review, Gap Analysis, and Strategic Framework. Innovative Computing Perspectives, 1(1), pp.1-14.

[15] Gomaa, A.H., 2026a. Sustainable supply chain management in Industry 4.0: A review, research gaps, and conceptual framework.Clean Technologies and Recycling, 6 (2), pp.139-160.

[16] Gomaa, A.H., 2026b. Artificial Intelligence Applications in Sustainable Reverse Supply Chains: A Systematic Review, Gap Analysis, and Future Research Agenda. Interdisciplinary Systems for Global Management, 2 (2), pp.127-149.

[17] Govindan, K., Kannan, D., Jørgensen, T.B. and Nielsen, T.S., 2022. Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 164(1), p.102725.

[18] Helo, P. and Hao, Y., 2022. Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), pp.1573-1590.

[19] Hofmann, E., Sternberg, H., Chen, H., Pflaum, A. and Prockl, G., 2019. Supply chain management and Industry 4.0: conducting research in the digital age. International Journal of Physical Distribution & Logistics Management, 49(10), pp.945-955.[20] Huang, K., Wang, K., Lee, P.K. and Yeung, A.C., 2023. The impact of industry 4.0 on supply chain capability and supply chain resilience: A dynamic resource-based view. International Journal of Production Economics, 262(1), p.108913.

[21] Koh, L., Orzes, G. and Jia, F., 2019. The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management. International Journal of Operations & Production Management, 39(6), pp.817-828.

[22] Lee, J., Azamfar, M. and Bagheri, B., 2021. A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems. Manufacturing Letters, 27(1), pp.87-91.

[23] Li, D., Tang, H., Wang, S. and Liu, C., 2017. A big data enabled load-balancing control for smart manufacturing of Industry 4.0. Cluster Computing, 20(2), pp.1855-1864.

[24] Liu, L., Song, W. and Liu, Y., 2023. Leveraging digital capabilities toward a circular economy: Reinforcing sustainable supply chain management with Industry 4.0 technologies. Computers & Industrial Engineering, 178(1), p.109113.

[25] Mhaskey, S.V., 2024. SCM 4.0: Navigating the Impact of Industry 4.0 on Supply Chain Management through Digitalization and Technology Integration.International Journal of Computer Engineering in Research Trends. 11(10), pp. 1-12.

[26] Mithas, S., Chen, Z.L., Saldanha, T.J. and De Oliveira Silveira, A., 2022. How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?. Production and Operations Management, 31(12), pp.4475-4487.

[27] Morariu, C., Morariu, O., Răileanu, S. and Borangiu, T., 2020. Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry, 120(1), p.103244.

[28] Mubarik, M.S. and Khan, S.A., 2024. Firms Intellectual Capital and Digital Supply Chain Management.In The Theory, Methods and Application of Managing Digital Supply Chains (pp. 77-92). Emerald Publishing Limited.

[29] Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M. and Moher, D., 2021. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. Journal of clinical epidemiology, 134, pp.103-112.

[30] Pasi, B.N., Mahajan, S.K. and Rane, S.B., 2020. Smart supply chain management: a perspective of industry 4.0. Supply Chain Management, 29(5), pp.3016-3030.

[31] Patel, K.R., 2023. Enhancing global supply chain resilience: Effective strategies for mitigating disruptions in an interconnected world. BULLET: Jurnal Multidisiplin Ilmu, 2(1), pp.257-264.

[32] Pozzi, R., Rossi, T. and Secchi, R., 2023. Industry 4.0 technologies: critical success factors for implementation and improvements in manufacturing companies. Production Planning & Control, 34(2), pp.139-158.

[33] Rad, F.F., Oghazi, P., Palmié, M., Chirumalla, K., Pashkevich, N., Patel, P.C. and Sattari, S., 2022. Industry 4.0 and supply chain performance: A systematic literature review of the benefits, challenges, and critical success factors of 11 core technologies. Industrial Marketing Management, 105(1), pp.268-293.

[34] Ren, S., Zhao, X., Huang, B., Wang, Z. and Song, X., 2019. A framework for shopfloor material delivery based on real-time manufacturing big data. Journal of Ambient Intelligence and Humanized Computing, 10(3), pp.1093-1108.

[35] Rossini, M., Powell, D.J. and Kundu, K., 2023. Lean supply chain management and Industry 4.0: a systematic literature review. International Journal of Lean Six Sigma, 14(2), pp.253-276.

[36] Saif-Ur-Rehman, K., Barson, N. and Hamdan, Y.H., 2024. Industry 4.0 technologies and firm performance with digital supply chain platforms and supply chain capabilities. Pakistan Journal of Commerce and Social Sciences (PJCSS), 18(4), pp.893-924.

[37] Samper, M.G., Florez, D.G., Borre, J.R. and Ramirez, J., 2022. Industry 4.0 for sustainable supply chain management: Drivers and barriers. Procedia Computer Science, 203(1), pp.644-650.

[38] Saqlain, M., Piao, M., Shim, Y. and Lee, J.Y., 2019. Framework of an IoT-based industrial data management for smart manufacturing. Journal of Sensor and Actuator Networks, 8(2), p.25.

[39] Serrano-Ruiz, J.C., Mula, J. and Poler, R., 2022. Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective. Journal of Manufacturing Systems, 63(1), pp.185-202.

[40] Shadravan, A. and Parsaei, H.R., 2023, June. Applications of Industry 4.0 in Supply Chain Management: A Systematic Literature Review. In Proceedings of the 8th North American International Conference on Industrial Engineering and Operations Management (IEOM Society), (Vol. 20230419).

[41] Tan, Q., Tong, Y., Wu, S. and Li, D., 2019. Modeling, planning, and scheduling of shop-floor assembly process with dynamic cyber-physical interactions: a case study for CPS-based smart industrial robot production. The International Journal of Advanced Manufacturing Technology, 105(9), pp.3979-3989.

[42] Teoh, Y.K., Gill, S.S. and Parlikad, A.K., 2021. 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),pp.2087-2094.

[43] Tjahjono, B., Esplugues, C., Ares, E. and Pelaez, G., 2017. What does industry 4.0 mean to supply chain?. Procedia manufacturing, 13(1), pp.1175-1182.

[44] Tripathi, V. and Bhadauria, A., 2025. Leveraging the competency of condition monitoring system for achieving excellence in operations management. Discover Sustainability, 6(1), p.452.

[45] Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A.K., Saraswat, S., Sharma, S., Li, C., Rajkumar, S. and Georgise, F.B., 2022. A novel smart production management system for the enhancement of industrial sustainability in Industry 4.0. Mathematical Problems in Engineering, 2022(1), p.6424869.

[46] Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A.K., Saraswat, S., Sharma, S., Li, C. and Rajkumar, S., 2022. Development of a data‐driven decision‐making system using lean and smart manufacturing concept in industry 4.0: a case study. Mathematical Problems in Engineering, 2022(1), p.3012215.

[47] Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A.K., Sharma, S., Singh, J., Pimenov, D.Y. and Giasin, K., 2021. An innovative agile model of smart lean–green approach for sustainability enhancement in Industry 4.0. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), p.215.

[48] Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A.K., Sharma, S., Li, C., Singh, S., Hussan, W.U., Salah, B., Saleem, W. and Mohamed, A., 2022. A sustainable productive method for enhancing operational excellence in shop floor management for industry 4.0 using hybrid integration of lean and smart manufacturing: An ingenious case study. Sustainability, 14(12), p.7452.

[49] Wang, J.B., Wang, J., Wu, Y., Wang, J.Y., Zhu, H., Lin, M. and Wang, J., 2018. A machine learning framework for resource allocation assisted by cloud computing. IEEE Network, 32(2), pp.144-151.

[50] Wang, Y., Yang, Y., Qin, Z., Yang, Y. and Li, J., 2023. A literature review on the application of digital technology in achieving green supply chain management. Sustainability, 15(11), p.8564.

[51] Yavuz, O., Uner, M.M., Okumus, F. and Karatepe, O.M., 2023. Industry 4.0 technologies, sustainable operations practices and their impacts on sustainable performance. Journal of Cleaner Production, 387(1), p.135951.

[52] Yuan, X.M. and Xue, A., 2023. Supply chain 4.0: new generation of supply chain management. Logistics, 7(1), p.9.

[53] Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benghabrit, Y. and Garza-Reyes, J.A., 2021. Supply chain management 4.0: a literature review and research framework. Benchmarking: An International Journal, 28(2), pp.465-501.

[54] Zhang, G., Yang, Y. and Yang, G., 2023. Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America. Annals of operations research, 322(2), pp.1075-1117.

[55] Zhang, J., Liu, J., Zhuang, C., Guo, H. and Ma, H., 2024. A data-driven smart management and control framework for a digital twin shop floor with multi-variety multi-batch production. The International Journal of Advanced Manufacturing Technology, 131(11), pp.5553-5569.