<p>Explainable Artificial Intelligence (XAI) offers a powerful approach for modeling complex factor relationships while simultaneously revealing their underlying mechanisms, making it particularly suitable for examining how globalization affects digital transformation in manufacturing. To address the research gap in applying XAI to understand the impact of globalization factors on digital transformation, this study develops a robust interpretable machine learning model to uncover the specific mechanisms through which these factors influence transformation velocity. Specifically, we identify the factors of globalization from the KOF framework in five dimensions–economic, social, political, dynamic capabilities, and firms–and use digital transformation velocity to dynamically measure digital transformation. A research dataset was constructed based on data from the manufacturing sector of Listed Chinese companies from 2013 to 2023, and an empirical study was conducted using an interpretable machine-learning approach. We find that (1) collecting globalization factors from the enterprise level and constructing a research model can train a robust machine-learning model; (2) the importance of globalization factors depends on different scenarios, the net effect, and the linkage effect of the factors; (3) unlike most of the factors, some factors inhibit the manufacturing industry’s digital transformation velocity; and(4)The impact of globalization factors on the velocity of digital transformation exhibits significant heterogeneity across both the geographic location and ownership structure of manufacturing enterprises.</p>