Artificial Intelligence for Lean Systems: Systematic Review, Antecedents, Conceptual Mapping, and Future Opportunities

Nasreddine Saadouli; Ahmed Elmelegy; Raghid Alhajj

doi:10.28991/ESJ-2025-09-02-030

Authors

Nasreddine Saadouli
saadouli.n@gust.edu.kw
Department of Business Administration, Gulf University for Science and Technology, Hawally,, Kuwait https://orcid.org/0000-0001-5615-9756
Ahmed Elmelegy Department of Business Administration, Gulf University for Science and Technology, Hawally,, Kuwait
Raghid Alhajj Department of Business Administration, Gulf University for Science and Technology, Hawally,, Kuwait

Vol. 9 No. 2 (2025): April

Review Articles

Downloads

PDF

Abstract
How to Cite
Metrics
References
License

Lean systems thrive on eliminating waste by minimizing all non-value-adding activities. Therefore, significant technological developments such as artificial intelligence (AI) are expected to be swiftly adopted to elevate their performance. While several recent studies have investigated the integration of generic Industry 4.0 tools into lean systems, there is no comprehensive study of the integration of AI in lean systems. Therefore, this study investigates the evolution of the research integrating AI into lean systems from 1993 to 2024 using a thorough bibliometric analysis of 186 peer-reviewed articles retrieved from the Scopus and Web of Science databases. In addition to identifying the body of research's prevalent intellectual and social structures, thematic clusters and thematic maps are constructed to describe the relevance and development of various research themes. The results reveal no comprehensive and integrative framework with unified terminology and distinct research clusters. Furthermore, the findings indicate a concentration of the research contributions in a small set of developed countries, necessitating the deliberate channeling of funds to enhance this research focus in less developed countries. This work is the first study that explicitly tracks the integration of AI in lean systems and creates a convergent realm of analysis and application by identifying the key research foci and corresponding future trends.

Doi: 10.28991/ESJ-2025-09-02-030

Full Text: PDF

Saadouli, N., Elmelegy, A., & Alhajj, R. (2025). Artificial Intelligence for Lean Systems: Systematic Review, Antecedents, Conceptual Mapping, and Future Opportunities. Emerging Science Journal, 9(2), 1110–1132. https://doi.org/10.28991/ESJ-2025-09-02-030

Download Citation

Rifqi, H., Zamma, A., Souda, S. Ben, & Hansali, M. (2021). Positive Effect of Industry 4.0 on Quality and Operations Management. International Journal of Online and Biomedical Engineering, 17(9), 133–147. doi:10.3991/ijoe.v17i09.24717.

Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2023). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216. doi:10.1016/j.eswa.2022.119456.

Mateo, F. W., & Redchuk, A. (2022). Artificial Intelligence as a Process Optimization Driver under Industry 4.0 Framework and the Role of IIoT, a Bibliometric Analysis. Journal of Industrial Integration and Management, 9(3), 1–16. doi:10.1142/S2424862222500130.

Simon, H. A. (1965). The logic of rational decision. British Journal for the Philosophy of Science, 16(63), 169–186. doi:10.1093/bjps/XVI.63.169.

Singh, H., & Singh, B. (2023). Industry 4.0 technologies integration with lean production tools: a review. TQM Journal, 36(8), 2507–2526. doi:10.1108/TQM-02-2022-0065.

Srinivasan, R. (2007). Artificial intelligence methodologies for agile refining: An overview. Knowledge and Information Systems, 12(2), 129–145. doi:10.1007/s10115-006-0057-z.

Singh, R. K., Choudhury, A. K., Tiwari, M. K., & Maull, R. S. (2006). An integrated fuzzy-based decision support system for the selection of lean tools: A case study from the steel industry. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(10), 1735–1749. doi:10.1243/09544054JEM494.

Vinodh, S., & Kumar, C. D. (2012). Development of computerized decision support system for leanness assessment using multi grade fuzzy approach. Journal of Manufacturing Technology Management, 23(4), 503–516. doi:10.1108/17410381211230457.

Yang, L., Jiang, G., Li, G., & Chen, X. (2019). Lean rate of posts in different departments based on ANP method. Journal of Advanced Computational Intelligence and Intelligent Informatics, 23(2), 317–322. doi:10.20965/jaciii.2019.p0317.

Hao, X., & Demir, E. (2024). Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol. Journal of Modelling in Management, 19(2), 605–629. doi:10.1108/JM2-01-2023-0009.

Bouzekri, H., Bara, N., Alpan, G., & Giard, V. (2022). An integrated Decision Support System for planning production, storage and bulk port operations in a fertilizer supply chain. International Journal of Production Economics, 252, 108561. doi:10.1016/j.ijpe.2022.108561.

Nabelsi, V., & Gagnon, S. (2017). Information technology strategy for a patient-oriented, lean, and agile integration of hospital pharmacy and medical equipment supply chains. International Journal of Production Research, 55(14), 3929–3945. doi:10.1080/00207543.2016.1218082.

Trabucco, M., & De Giovanni, P. (2021). Achieving resilience and business sustainability during COVID-19: The role of lean supply chain practices and digitalization. Sustainability (Switzerland), 13(22), 12369. doi:10.3390/su132212369.

Bucki, R., & Suchánek, P. (2019). The Cost-Based Lean Approach to the Information Logistics Business System Modelling. Journal of Computing and Information Technology, 27(1), 59–75. doi:10.20532/cit.2019.1004309.

Nguyen, L. T., Chansanam, W., Hunsapun, N., Chaichuay, V., Kanyacome, S., Takhom, A., ... & Li, C. (2024). Evaluating the Performance of Topic Modeling Techniques for Bibliometric Analysis Research: An LDA-based Approach. HighTech and Innovation Journal, 5(2), 312-330. doi:10.28991/HIJ-2024-05-02-07.

Prem, E. (1997). The behavior based firm: application of recent AI concepts to company management. Applied Artificial Intelligence, 11(3), 173–196. doi:10.1080/088395197118217.

Villalba-Díez, J., Molina, M., Ordieres-Meré, J., Sun, S., Schmidt, D., & Wellbrock, W. (2020). Geometric deep lean learning: Deep learning in industry 4.0 cyber–physical complex networks. Sensors (Switzerland), 20(3), 763. doi:10.3390/s20030763.

Ito, T., Abd Rahman, M. S., Mohamad, E., Abd Rahman, A. A., & Salleh, M. R. (2020). Internet of things and simulation approach for decision support system in lean manufacturing. Journal of Advanced Mechanical Design, Systems and Manufacturing, 14(2), 1-12. doi:10.1299/jamdsm.2020jamdsm0027.

Lotfi, R., & Weber, G. W. (2022). An Introduction to the Special Issue "Recent advances on supply chain network design.” Foundations of Computing and Decision Sciences, 47(4), 323–326. doi:10.2478/fcds-2022-0017.

Kumar, P., Bhadu, J., Singh, D., & Bhamu, J. (2021). Integration between Lean, Six Sigma and Industry 4.0 technologies. International Journal of Six Sigma and Competitive Advantage, 13(1–3), 19–37. doi:10.1504/IJSSCA.2021.120224.

Perera, A. D., Jayamaha, N. P., Grigg, N. P., Tunnicliffe, M., & Singh, A. (2021). The application of machine learning to consolidate critical success factors of lean six sigma. IEEE Access, 9, 112411–112424. doi:10.1109/ACCESS.2021.3103931.

Yadav, N., Shankar, R., & Singh, S. P. (2020). Impact of Industry4.0/ICTs, Lean Six Sigma and quality management systems on organisational performance. TQM Journal, 32(4), 815–835. doi:10.1108/TQM-10-2019-0251.

Ibrahim, A., & Kumar, G. (2024). Selection of Industry 4.0 technologies for Lean Six Sigma integration using fuzzy DEMATEL approach. International Journal of Lean Six Sigma, 15(5), 1025–1042. doi:10.1108/IJLSS-05-2023-0090.

Sharma, A., & Singh, B. J. (2023). Evolution of modified LSS 4.0 model for sustainable Indian textile industry: a narrative review. International Journal on Interactive Design and Manufacturing, 1–20. doi:10.1007/s12008-023-01534-y.

Yadav, N., Shankar, R., & Singh, S. P. (2021). Critical success factors for lean six sigma in quality 4.0. International Journal of Quality and Service Sciences, 13(1), 123–156. doi:10.1108/IJQSS-06-2020-0099.

Sharma, A., Mittal, K., Arora, D., & Ganti, S. S. (2021). A comprehensive review on strategies for new drug discovery and enhanced productivity in research and development: Recent advancements and future prospectives. Mini-Reviews in Organic Chemistry, 18(3), 361–382. doi:10.2174/1570193X17999200529100808.

Kaswan, M. S., Rathi, R., Antony, J., Cross, J., Garza-Reyes, J. A., Singh, M., Preet Singh, I., & Sony, M. (2024). Integrated Green Lean Six Sigma-Industry 4.0 approach to combat COVID-19: from literature review to framework development. International Journal of Lean Six Sigma, 15(1), 50–79. doi:10.1108/IJLSS-11-2022-0227.

Zulfiqar, M., Sony, M., Bhat, S., Antony, J., Salentijn, W., & McDermott, O. (2023). Unlocking the potential: empirical analysis of enablers, barriers, benefits and technologies for integrating Industry 4.0 and Lean Six Sigma in manufacturing organisations. TQM Journal, 36(8), 2360-2382.. doi:10.1108/TQM-05-2023-0130.

Hu, G., Wang, L., Fetch, S., & Bidanda, B. (2008). A multi-objective model for project portfolio selection to implement lean and Six Sigma concepts. International Journal of Production Research, 46(23), 6611–6625. doi:10.1080/00207540802230363.

Zimmerling, C., Poppe, C., Stein, O., & Kärger, L. (2022). Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning. Materials and Design, 214, 110423. doi:10.1016/j.matdes.2022.110423.

Koulouriotis, D. E., Xanthopoulos, A. S., & Tourassis, V. D. (2010). Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms. International Journal of Production Research, 48(10), 2887–2912. doi:10.1080/00207540802603759.

Hosseinzadeh, A., Frank Chen, F., Shahin, M., & Bouzary, H. (2023). A predictive maintenance approach in manufacturing systems via AI-based early failure detection. Manufacturing Letters, 35, 1179–1186. doi:10.1016/j.mfglet.2023.08.125.

Sordan, J. E., Oprime, P. C., Pimenta, M. L., Lombardi, F., & Chiabert, P. (2022). Symbiotic relationship between robotics and Lean Manufacturing: a case study involving line balancing. TQM Journal, 34(5), 1076–1095. doi:10.1108/TQM-03-2021-0073.

Shahin, M., Chen, F. F., Hosseinzadeh, A., Bouzary, H., & Shahin, A. (2024). Waste reduction via image classification algorithms: beyond the human eye with an AI-based vision. International Journal of Production Research, 62(9), 3193–3211. doi:10.1080/00207543.2023.2225652.

Ly Duc, M., & Bilik, P. (2022). Zero Defect Manufacturing Using Digital Numerical Control. Management and Production Engineering Review, 13(3), 61–74. doi:10.24425/mper.2022.142383.

Cheung, W. H., & Yang, Q. (2023). Fabric defect detection using AI and machine learning for lean and automated manufacturing of acoustic panels. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 238(12), 1827–1836. doi:10.1177/09544054231209782.

Mortada, A., & Soulhi, A. (2023). A Decision-Making Model for Quality Improvement Using Fuzzy Logic. Journal of Theoretical and Applied Information Technology, 101(13), 5172–5181.

Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Sharma, S., Li, C., Singh, S., Ul Hussan, W., Salah, B., Saleem, W., & 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 (Switzerland), 14(12), 7452. doi:10.3390/su14127452.

Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Saraswat, S., Sharma, S., Li, C., Rajkumar, S., & 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, 6424869. doi:10.1155/2022/6424869.

Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Sharma, S., Li, C., & Di Bona, G. (2022). A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0. Mathematics, 10(3), 347. doi:10.3390/math10030347.

Abusaq, Z., Zahoor, S., Habib, M. S., Rehman, M., Mahmood, J., Kanan, M., & Mushtaq, R. T. (2023). Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study. Energies, 16(4). doi:10.3390/en16041972.

Kliestik, T., Nagy, M., & Valaskova, K. (2023). Global Value Chains and Industry 4.0 in the Context of Lean Workplaces for Enhancing Company Performance and Its Comprehension via the Digital Readiness and Expertise of Workforce in the V4 Nations. Mathematics, 11(3), 601. doi:10.3390/math11030601.

Queiroz, G. A., Alves Junior, P. N., & Costa Melo, I. (2022). Digitalization as an Enabler to SMEs Implementing Lean-Green? A Systematic Review through the Topic Modelling Approach. Sustainability (Switzerland), 14(21), 14089. doi:10.3390/su142114089.

Dumrak, J., & Zarghami, S. A. (2023). The role of artificial intelligence in lean construction management. Engineering, Construction and Architectural Management, 32(1), 131-155. doi:10.1108/ECAM-02-2022-0153.

Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1(6), 14. doi:10.1017/dce.2020.16.

Prieto, A. J., & Alarcón, L. F. (2023). Using Fuzzy Inference Systems for Lean Management Strategies in Construction Project Delivery. Journal of Construction Engineering and Management, 149(9), 12922. doi:10.1061/jcemd4.coeng-12922.

Aljawder, A., & Al-Karaghouli, W. (2024). The adoption of technology management principles and artificial intelligence for a sustainable lean construction industry in the case of Bahrain. Journal of Decision Systems, 33(2), 263–292. doi:10.1080/12460125.2022.2075529.

Bharanitharan, M., & Madhumita, G. (2022). Optimising Material Supply Services through AI based Office Automation. International Journal of Engineering Trends and Technology, 70(9), 9–19. doi:10.14445/22315381/IJETT-V70I9P202.

Okongwu, U., Lauras, M., Dupont, L., & Humez, V. (2012). A decision support system for optimising the order fulfilment process. Production Planning and Control, 23(8), 581–598. doi:10.1080/09537287.2011.566230.

Wilantara, D., Mulyono, N. B., & Winarso, D. (2022). Automate Short Cyclic Well Job Candidacy Using Artificial Neural Networks–Enabled Lean Six Sigma Approach: A Case Study in Oil and Gas Company. International Journal on Advanced Science, Engineering and Information Technology, 12(4), 1639–1649. doi:10.18517/ijaseit.12.4.12845.

Arana-Landín, G., Uriarte-Gallastegi, N., Landeta-Manzano, B., & Laskurain-Iturbe, I. (2023). The Contribution of Lean Management”Industry 4.0 Technologies to Improving Energy Efficiency. Energies, 16(5), 2124. doi:10.3390/en16052124.

Liu, S., Leat, M., Moizer, J., Megicks, P., & Kasturiratne, D. (2013). A decision-focused knowledge management framework to support collaborative decision making for lean supply chain management. International Journal of Production Research, 51(7), 2123–2137. doi:10.1080/00207543.2012.709646.

Lichtenthaler, U. (2020). Agile innovation: The complementarity of design thinking and lean startup. International Journal of Service Science, Management, Engineering, and Technology, 11(1), 157–167. doi:10.4018/IJSSMET.2020010110.

Raneri, S., Lecron, F., Hermans, J., & Fouss, F. (2023). Predictions through Lean startup? Harnessing AI-based predictions under uncertainty. International Journal of Entrepreneurial Behaviour and Research, 29(4), 886–912. doi:10.1108/IJEBR-07-2021-0566.

Chatzopoulos, C. G., & Weber, M. (2021). Digitization and Lean Customer Experience Management: Success Factors and Conditions, Pitfalls and Failures. International Journal of Industrial Engineering and Management, 12(2), 73–84. doi:10.24867/IJIEM-2021-2-278.

Tseng, M. L., Tran, T. P. T., Ha, H. M., Bui, T. D., & Lim, M. K. (2021). Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: a data driven analysis. Journal of Industrial and Production Engineering, 38(8), 581–598. doi:10.1080/21681015.2021.1950227.

Rossit, D. A., Tohmé, F., & Frutos, M. (2019). Production planning and scheduling in Cyber-Physical Production Systems: a review. International Journal of Computer Integrated Manufacturing, 32(4–5), 385–395. doi:10.1080/0951192X.2019.1605199.

Shahin, M., Chen, F. F., Bouzary, H., & Krishnaiyer, K. (2020). Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. International Journal of Advanced Manufacturing Technology, 107(5–6), 2927–2936. doi:10.1007/s00170-020-05124-0.

Javaid, M., Haleem, A., Singh, R. P., Rab, S., Suman, R., & Khan, S. (2022). Exploring relationships between Lean 4.0 and manufacturing industry. Industrial Robot, 49(3), 402–414. doi:10.1108/IR-08-2021-0184.

Alsadi, J., Antony, J., Mezher, T., Jayaraman, R., & Maalouf, M. (2023). Lean and Industry 4.0: A bibliometric analysis, opportunities for future research directions. Quality Management Journal, 30(1), 41–63. doi:10.1080/10686967.2022.2144785.

Amjad, M. S., Rafique, M. Z., Hussain, S., & Khan, M. A. (2020). A new vision of LARG Manufacturing ” A trail towards Industry 4.0. CIRP Journal of Manufacturing Science and Technology, 31, 377–393. doi:10.1016/j.cirpj.2020.06.012.

Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. doi:10.1016/j.joi.2017.08.007.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. doi:10.1016/j.jbusres.2021.04.070.

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. doi:10.1002/asi.21525.

Ahmi, A. (2022). Bibliometric analysis using R for non-coders. A practical handbook in conducting bibliometric analysis studies using Biblioshiny for Bibliometrix R package. Google Books: California, United States,

Sánchez, A. D., de la Cruz Del Río Rama, M., & García, J. í. (2017). Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS. European Research on Management and Business Economics, 23(1), 8–15. doi:10.1016/j.iedeen.2016.02.001.

Zupic, I., & Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472. doi:10.1177/1094428114562629.

Bradford, S. C., Egan, M. E., & Shera, J. H. (1953). A Review of the Present State of Librarianship and Documentation. In: S. C. Bradford, Documentation, 2nd ed. London: Lockwood, 11–45.

Coito, T., Viegas, J. L., Martins, M. S. E., Cunha, M. M., Figueiredo, J., Vieira, S. M., & Sousa, J. M. C. (2019). A novel framework for intelligent automation. IFAC-PapersOnLine, 52(13), 1825–1830. doi:10.1016/j.ifacol.2019.11.501.

Jain, V., Benyoucef, L., & Deshmukh, S. G. (2008). What's the buzz about moving from "lean” to "agile” integrated supply chains? A fuzzy intelligent agent-based approach. International Journal of Production Research, 46(23), 6649–6677. doi:10.1080/00207540802230462.

Lee, C. K. M., Ho, W., Ho, G. T. S., & Lau, H. C. W. (2011). Design and development of logistics workflow systems for demand management with RFID. Expert Systems with Applications, 38(5), 5428–5437. doi:10.1016/j.eswa.2010.10.012.

Hwang, R., & Katayama, H. (2009). A multi-decision genetic approach for workload balancing of mixed-model U-shaped assembly line systems. International Journal of Production Research, 47(14), 3797–3822. doi:10.1080/00207540701851772.

Lopez-Perez, D., De Domenico, A., Piovesan, N., Xinli, G., Bao, H., Qitao, S., & Debbah, M. (2022). A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning. IEEE Communications Surveys and Tutorials, 24(1), 653–697. doi:10.1109/COMST.2022.3142532.

Li, L. (2009). Bottleneck detection of complex manufacturing systems using a data-driven method. International Journal of Production Research, 47(24), 6929–6940. doi:10.1080/00207540802427894.

Vinodh, S., Sundararaj, G., Devadasan, S. R., Maharaja, R., Rajanayagam, D., & Goyal, S. K. (2008). DESSAC: A decision support system for quantifying and analysing agility. International Journal of Production Research, 46(23), 6759–6780. doi:10.1080/00207540802230439.

Pullan, T. T., Bhasi, M., & Madhu, G. (2013). Decision support tool for lean product and process development. Production Planning and Control, 24(6), 449–464. doi:10.1080/09537287.2011.633374.

Mehmood, U., Ujang, U., Azri, S., & Choon, T. L. (2024). Transforming high-rise residential maintenance: a 3D spatio-temporal model utilizing Industry 4.0 and lean principles. Facilities, 43(1-2, 32–53. doi:10.1108/F-06-2024-0085.

Lakhouil, H., & Soulhi, A. (2025). The fusion of Lean Manufacturing with Industry 4.0 technologies towards a new pillar for improving supply chain performance, the case of the automotive industry in Morocco. Data and Metadata, 4. doi:10.56294/dm2025473.

Arora, M., Ahmad, V., Kumar, R., & Singh, R. (2025). The Impact of Industry 4.0 Technologies on Operational Excellence Methods: Application of Fuzzy Topsis. EMJ - Engineering Management Journal, 1–12. doi:10.1080/10429247.2024.2443703.

Mukherjee, D., Lim, W. M., Kumar, S., & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research, 148, 101–115. doi:10.1016/j.jbusres.2022.04.042.

Dihan, M. S., Akash, A. I., Tasneem, Z., Das, P., Das, S. K., Islam, M. R., Islam, M. M., Badal, F. R., Ali, M. F., Ahamed, M. H., Abhi, S. H., Sarker, S. K., & Hasan, M. M. (2024). Digital twin: Data exploration, architecture, implementation and future. Heliyon, 10(5), 26503. doi:10.1016/j.heliyon.2024.e26503.

Acceptance Rate:	21%
Review Speed:	74 days
Issue Per Year:	6
Number of Volumes:	7
Number of Issues:	44
Number of Articles:	493
Number of Reviewers:	1187
Number of Contributors:	1394
Contributing Countries:	83
No. of WoS Citations:	2609
No. of Scopus Citations:	2936
No. of Google Citations:	4161
Google h-index:	29
Google i10-index:	126
Abstract Views:	681,807
PDF Download:	492,524

Artificial Intelligence for Lean Systems: Systematic Review, Antecedents, Conceptual Mapping, and Future Opportunities

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Editorial Pick

Effect of Addition of Alccofine on the Compressive Strength of Cement Mortar Cubes

Impediments of Green Finance Adoption System: Linking Economy and Environment

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Address

Contact Info:

Artificial Intelligence for Lean Systems: Systematic Review, Antecedents, Conceptual Mapping, and Future Opportunities

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

social

Journal Imprint

Journal Metrics

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Editorial Pick

Effect of Addition of Alccofine on the Compressive Strength of Cement Mortar Cubes

Impediments of Green Finance Adoption System: Linking Economy and Environment

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites