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, Cairo 13511, Egypt
DOI: https://doi.org/10.59429/scr.v4i1.15078
Keywords: artificial intelligence; AI-Driven sustainable operations management; manufacturing supply chains; sustainable manufacturing; digital transformation; supply chain resilience
Abstract
Artificial Intelligence (AI) is increasingly transforming manufacturing supply chains by enabling intelligent decision-making, adaptive operations, and more efficient resource utilization. Its potential to advance sustainable operations management, however, extends beyond technological adoption to the organizational integration of AI capabilities into operational processes and sustainability-oriented strategies. Despite growing scholarly attention, research in this field remains fragmented across technological applications, manufacturing functions, organizational conditions, and sustainability dimensions. In particular, limited conceptual integration exists regarding how AI capabilities are translated into operational transformation and under what organizational and technological conditions such transformation generates sustainable value. To address these limitations, this study conducts a systematic literature review and structured research gap analysis of peer-reviewed studies published between 2016 and 1 June 2026. The review examines the intellectual evolution of the field, synthesizes dominant research themes, evaluates AI applications across manufacturing and supply chain operations, and critically assesses their implications for operational, environmental, economic, and social sustainability. The synthesis highlights an emerging shift from isolated, function-specific AI applications towards interconnected and increasingly autonomous operational ecosystems, supported by the convergence of AI with the Internet of Things (IoT), digital twins, cyber-physical systems, and advanced analytics. These developments create opportunities for predictive and prescriptive decision-making, intelligent production planning, predictive maintenance, quality optimization, resource efficiency, supply chain visibility, and disruption management. Nevertheless, the realization of these benefits remains contingent upon organizational readiness, digital infrastructure maturity, data quality, workforce capabilities, effective governance, and the alignment of technological investments with sustainability priorities. The review further identifies persistent theoretical, methodological, technological, organizational, and empirical gaps, particularly concerning human–AI collaboration, implementation scalability, algorithmic accountability, sustainability trade-offs, and the assessment of long-term performance outcomes. Building on these insights, the study develops an AI-Driven Sustainable Operations Management Strategic Framework that integrates AI technological capabilities, organizational and digital enablers, AI-enabled operational transformation mechanisms, sustainability-oriented governance, and multidimensional performance outcomes. By connecting technological potential with organizational transformation and sustainability performance, the proposed framework provides an integrative conceptual foundation for understanding AI-enabled sustainable value creation across manufacturing supply chains. It also establishes a structured agenda for future research and offers strategic implications for manufacturing organizations and policymakers seeking to advance responsible AI adoption, strengthen operational resilience, and achieve sustainable long-term competitiveness.
References
1.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.
2.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.
3.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.
4.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.
5.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.
6.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.
7.Felsberger, A. and Reiner, G., 2020. Sustainable industry 4.0 in production and operations management: a systematic literature review. Sustainability, 12(19), p.7982.
8.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.
9.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.
10.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.
11.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.
12.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.
13.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
14.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
15.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.
16.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.
17.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.
18.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.
19.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.
20.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.
21.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.
22.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.
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.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.
25.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.
26.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.
27.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.
28.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.
29.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.
30.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.
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.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.
33.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.
34.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.
35.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.
36.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.
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.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.
39.Fatorachian, H. and Kazemi, H., 2021. Impact of Industry 4.0 on supply chain performance. Production Planning & Control, 32(1), pp.63-81.
40.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.
41.Dyba, W. and De Marchi, V., 2022. On the road to Industry 4.0 in manufacturing clusters: the role of business support organizations. Competitiveness Review: An International Business Journal, 32(5), pp.760-776.
42.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.
43.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.
44.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.
45.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.
46.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.
47.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).
48.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.
49.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.
50.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.
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.