The Impact of Generative Artificial Intelligence and Lean Practices on Operational Efficiency in Manufacturing Supply Chains: Evidence from Firms

Abstract

The study focused on how well manufacturing companies are integrating generative AI into their processes and how this integration is affecting them in terms of operational efficiency and supply chain resilience, and the link between these variables. The quantitative approach used in this study was a cross-section, and the data obtained were in the form of questionnaires, which were structured and filled out by 348 manufacturing companies. Automotive companies (28.2%), food processing companies (24.7%) and manufacturing companies related to construction (21.3%) made up the sample. The descriptive statistics, reliability analysis, Pearson correlation analysis and hierarchical multiple regression analysis were applied. The Cronbach’s α value of all the measurement scales were in the range of 0.861 to 0.902, which were satisfactory. The results showed that the operational efficiency for generative AI capabilities is significantly and positively related to operational efficiency (β = 0.643, t = 8.914, p < 0.001). The operational efficiency for lean manufacturing maturity is significantly and positively related to operational efficiency (β = 0.512, t = 7.235, p < 0.001). The operational efficiency was the most important predictor of supply chain resilience (β = 0.588, t = 8.102 and p < 0.001). The interaction effect of generative AI capabilities and leadership support was also found to be significant (β = 0.274, t = 3.456, p = 0.012), indicating leadership support as another moderating factor. This was done by the direct effects model, which accounted for 62.3% of the operational efficiency.