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Unraveling role of artificial intelligence capability through digital twin practices, lean six sigma and total quality management for smart manufacturing performance

MD Faiaz Zaman Dehan et al.

International Journal of Lean Six Sigma2026https://doi.org/10.1108/ijlss-09-2025-0251article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose This paper aims to examine the effect of Artificial Intelligence Capabilities (AIC) on Smart Manufacturing Performance (SMP) and, specifically, industry 4.0. It seeks to find out the mediating roles of Lean Six Sigma (LSS), Digital Twin Practices (DTP) and Total Quality Management (TQM) based on the Dynamic Capabilities View (DCV). Design/methodology/approach To gather information, the study took an empirical approach (survey) of 344 employees of medium to large Ready-Made Garment (RMG) firms in Bangladesh. In testing the direct and indirect effects of AIC on SMP, emphasis was put on testing the three mediators using Partial Least Squares-Structural Equation Modeling. Findings The findings indicate that AIC has a lot of influence on SMP either directly or indirectly via the mediating variables of DTP, LSS and TQM. Such operational structures can be used to convert the intelligence of AI as data into quantifiable promotions in terms of responsiveness, quality and flexibility in processes. Research limitations/implications The participants of the study are restricted to the RMG sector in Bangladesh, and this factor can limit generalizability. It might be possible to conduct such research in different industries, locations and contexts. Originality/value In this paper, the DCV is expanded to demonstrate how, when integrated into well-designed systems of improvement, AI can be viewed as a dynamic organizational capability rather than just one of the tools. It offers a process-oriented description of the role of digital transformation in manufacturing excellence in emerging economies.

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https://doi.org/https://doi.org/10.1108/ijlss-09-2025-0251

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@article{md2026,
  title        = {{Unraveling role of artificial intelligence capability through digital twin practices, lean six sigma and total quality management for smart manufacturing performance}},
  author       = {MD Faiaz Zaman Dehan et al.},
  journal      = {International Journal of Lean Six Sigma},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ijlss-09-2025-0251},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.