Article Sidebar

Download
PDF
Statistic
Read Counter : 228 Download : 164

Main Article Content

Abstract

The convergence of artificial intelligence (AI) and LEAN manufacturing principles presents unprecedented opportunities for operational excellence while introducing complex risk management and sustainability challenges. Addressing the critical research gap in quantitative AI-LEAN integration models. This research develops an integrated framework for implementing AI-driven big data analytics in LEAN manufacturing equipment R&D, addressing the critical gap between technological capabilities and sustainable manufacturing practices. We used three research methods: theoretical modelling, empirical validation with the SECOM semiconductor dataset, and 12-month field testing across three manufacturing facilities. This mixed-methods approach quantifies the synergistic effects of AI-LEAN integration. The framework incorporates hierarchical risk taxonomy, real-time anomaly detection algorithms achieving 93.5% accuracy, and multidimensional sustainability metrics. Results demonstrate substantial improvements: 36.1% increase in overall equipment effectiveness, 58.9% reduction in setup times, and 31.4% decrease in carbon footprint, energy intensity reduced by 30%, employee safety incidents decreased by 67%, and job satisfaction increased by 15%, achieving synergistic optimization of environmental benefits and social value. Risk prediction models achieved 91-96% accuracy across different categories, while maintaining sub-50ms inference times for real-time applications. The AI-enhanced system outperformed traditional LEAN implementations by 1.81x in continuous improvement rates and achieved payback in 13 months versus 23 months for conventional approaches. Financial analysis reveals 319.4% ROI over five years, validating the economic viability alongside environmental benefits. This research establishes a replicable paradigm for sustainable smart manufacturing, demonstrating that advanced analytics can simultaneously enhance operational efficiency, risk management, and environmental stewardship while preserving LEAN's human-centric values.

Keywords

AI-LEAN integration Risk management Sustainability metrics Industry 4.0 Operational excellence

Article Details

Author Biographies

ChengHsien Tsai, Faculty of Business and Accountancy, Lincoln University College, 47301 Petaling Jaya, Selangor, Malaysia

Cheng-Hsien Tsai is currently pursuing a Doctoral degree in Malaysia. His research interests lie in lean management, risk management, and industrial management of automated smart factories. PhD Researcher at Lincoln University College, Malaysia.

Oyyappan Duraipandi, Faculty of Business and Accountancy, Lincoln University College, 47301 Petaling Jaya, Selangor, Malaysia

Prof. Dr. OYYAPPAN DURAIPANDI, is a professor at Lincoln University and the supervising professor of Cheng Hsien Tsai.

Dhakir Abbas Ali , Faculty of Business and Accountancy, Lincoln University College, 47301 Petaling Jaya, Selangor, Malaysia

Dhakir Abbas Ali ， is a professor of academic affairs at Lincoln University, responsible for reviewing papers, arranging and presiding over thesis defenses.

How to Cite
Tsai, C., Duraipandi, O., & Abbas Ali , D. (2025). Research on risk control and sustainability strategies of AI-driven big data analytics in LEAN manufacturing equipment R&D. Future Technology, 4(4), 267–281. Retrieved from https://fupubco.com/futech/article/view/502
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. Powell, D.J., Artificial intelligence in lean manufacturing: digitalization with a human touch? International Journal of Lean Six Sigma, 2024. 15(3): 719-729.
  2. Fountaine, T., B. McCarthy, and T. Saleh, Building the AI-powered organization. Harvard business review, 2019. 97(4): 62-73.
  3. Saraswat, P., R. Agrawal, and S.B. Rane, Technological integration of lean manufacturing with industry 4.0 toward lean automation: insights from the systematic review and further research directions. Benchmarking: An International Journal, 2024. https://doi.org/10.1108/BIJ-05-2023-0316.
  4. Hughes, L., et al., Perspectives on the future of manufacturing within the Industry 4.0 era. Production Planning & Control, 2022. 33(2-3): 138-158.
  5. Singla, L., K. preet Kaur, and N. Kaur, AI's Double-Edged Sword: Examining the Dark Side of AI in Human Lives, in Demystifying the Dark Side of AI in Business. 2024, IGI Global Scientific Publishing. 44-59.
  6. Söylemez, İ. and R. Ünlü, Artificial intelligence in production systems and integration with lean manufacturing, in Engineering Applications of AI and Swarm Intelligence. 2024, Springer. 235-252.
  7. Shahin, M., et al., Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network. The International Journal of Advanced Manufacturing Technology, 2024. 133(11): 5343-5419.
  8. Smith, J.J., et al., The Generative AI Ethics Playbook. arXiv preprint arXiv:2501.10383, 2024. https://doi.org/10.48550/arXiv.2501.10383.
  9. Garetti, M. and M. Taisch, Sustainable manufacturing: trends and research challenges. Production planning & control, 2012. 23(2-3): 83-104.
  10. Taylor, R., D. Baron, and D. Schmidt. The world in 2025-predictions for the next ten years. in 2015 10th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT). 2015. IEEE. 10.1109/IMPACT.2015.7365193.
  11. Chadha, U., et al., Synergizing Lean Six Sigma Framework Using Artificial Intelligence, Internet of Things, and Blockchain for Sustainable Manufacturing Excellence. Authorea Preprints, 2024. DOI: 10.36227/techrxiv.172565696.63123962/v1.
  12. Duhem, L., M. Benali, and G. Martin, Parametrization of a demand-driven operating model using reinforcement learning. Computers in industry, 2023. 147: 103874.
  13. Aziz, S. and M. Dowling, Machine learning and AI for risk management. Disrupting finance, 2019: 33-50.
  14. Cifone, F.D., et al., ‘Lean 4.0’: how can digital technologies support lean practices? International Journal of Production Economics, 2021. 241: 108258.
  15. Küfner, T., T.H.-J. Uhlemann, and B. Ziegler, Lean data in manufacturing systems: Using artificial intelligence for decentralized data reduction and information extraction. Procedia CIRP, 2018. 72: 219-224.
  16. Czvetkó, T., et al., Data-driven business process management-based development of Industry 4.0 solutions. CIRP journal of manufacturing science and technology, 2022. 36: 117-132.
  17. Rane, S.B., P.R. Potdar, and S. Rane, Development of project risk management framework based on industry 4.0 technologies. Benchmarking: An International Journal, 2021. 28(5): 1451-1481.
  18. Benraouane, S.A., AI Management System Certification According to the ISO/IEC 42001 Standard: How to Audit, Certify, and Build Responsible AI Systems. 2024: Productivity Press. https://doi.org/10.4324/9781003463979.
  19. Wirtz, B.W., J.C. Weyerer, and I. Kehl, Governance of artificial intelligence: A risk and guideline-based integrative framework. Government information quarterly, 2022. 39(4): 101685.
  20. Zhang, H., M. Song, and Y. Wang, Does AI-infused operations capability enhance or impede the relationship between information technology capability and firm performance? Technological Forecasting and Social Change, 2023. 191: 122517.
  21. Tashkinov, A.G., The implementation of lean and digital management techniques using artificial intelligence in industrial settings. Discover Artificial Intelligence, 2024. 4(1): 94.
  22. Cisterna, D., et al. Synergies between lean construction and artificial intelligence: AI driven continuous improvement process. in Proceedings of the 30th annual conference of the International Group for Lean Construction (IGLC30). 2022. DOI:10.24928/2022/0122.
  23. Piorkowski, D., M. Hind, and J. Richards, Quantitative ai risk assessments: Opportunities and challenges. arXiv preprint arXiv:2209.06317, 2022. https://doi.org/10.48550/arXiv.2209.06317.
  24. Saad, S.M., et al., Industry 4.0 and Lean Manufacturing–a systematic review of the state-of-the-art literature and key recommendations for future research. International Journal of Lean Six Sigma, 2024. 15(5): 997-1024.
  25. Ucar, A., M. Karakose, and N. Kırımça, Artificial intelligence for predictive maintenance applications: key components, trustworthiness, and future trends. Applied Sciences, 2024. 14(2): 898.
  26. Achouch, M., et al., On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied sciences, 2022. 12(16): 8081.
  27. Ghaithan, A.M., et al., Integrated impact of circular economy, industry 4.0, and lean manufacturing on sustainability performance of manufacturing firms. International journal of environmental research and public health, 2023. 20(6): 5119.
  28. Ciliberto, C., et al., Enabling the Circular Economy transition: A sustainable lean manufacturing recipe for Industry 4.0. Business Strategy and the Environment, 2021. 30(7): 3255-3272.
  29. Machado, C.G., M.P. Winroth, and E.H.D. Ribeiro da Silva, Sustainable manufacturing in Industry 4.0: an emerging research agenda. International Journal of Production Research, 2020. 58(5): 1462-1484.
  30. Kipper, L.M., et al., Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis. International Journal of Production Research, 2020. 58(6): 1605-1627.
  31. Buer, S.-V., et al., The complementary effect of lean manufacturing and digitalisation on operational performance. International Journal of Production Research, 2021. 59(7): 1976-1992.
  32. Lovelle, J., Mapping the value stream. IIE solutions, 2001. 33(2): 26-33.
  33. Wright, T.P., Factors affecting the cost of airplanes. Journal of the aeronautical sciences, 1936. 3(4): 122-128.
  34. Parasuraman, R., T.B. Sheridan, and C.D. Wickens, A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 2000. 30(3): 286-297.
  35. Kaplan, S. and B.J. Garrick, On the quantitative definition of risk. Risk analysis, 1981. 1(1): 11-27.
  36. Bagchi, S. and S.K. Mitra, The nonuniform discrete Fourier transform and its applications in signal processing. Vol. 463. 2012: Springer Science & Business Media.
  37. Guyon, I. and A. Elisseeff, An introduction to variable and feature selection. Journal of machine learning research, 2003. 3(Mar): 1157-1182.
  38. Sheng-jing, M., et al. An new evolutionary multi-objective optimization algorithm. in The 2003 Congress on Evolutionary Computation, 2003. CEC'03. 2003. IEEE.
  39. LeCun, Y., et al., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 2002. 86(11): 2278-2324.
  40. Breiman, L., Random forests. Machine learning, 2001. 45(1): 5-32.
  41. Freeman, K.P., J.R. Cook, and E.H. Hooijberg, Introduction to statistical quality control. Journal of the American Veterinary Medical Association, 2021. 258(7): 733-739.
  42. Metropolis, N. and S. Ulam, The monte carlo method. Journal of the American statistical association, 1949. 44(247): 335-341.
  43. Klöpffer, W., Life cycle assessment: From the beginning to the current state. Environmental science and pollution research, 1997. 4(4): 223-228.
  44. Elkington, J., Partnerships from cannibals with forks: The triple bottom line of 21st‐century business. Environmental quality management, 1998. 8(1): 37-51.