Abstract: The mining and quantitative evaluation of smart manufacturing policies are conducive to the implementation of such policies, the formulation of subsequent supporting policies and the growth of China’s smart manufacturing sector, but the existing studies in this area are scarce. In this paper, based on the text mining and analysis of 38 smart manufacturing policies released by nine cities in the Greater Bay Area from 2014 to 2020, and by classifying the policy dimensions in the literature, we construct a PMC index model of smart manufacturing policies containing 9 primary variables and 39 secondary variables, and select one typical programmatic policy of each of the above nine cities for empirical evidence. It is found that these policies are generally at an excellent level, but still can be optimized in terms of policy strength, policy nature, policy timeliness, incentives, synergy objects, etc. The problem of duplication of focus among regional policies should also be paid attention to. The model provides a basis for the optimization of smart manufacturing policies in nine cities in the Greater Bay Area, and also provides a reference value of policy dimensions for other regions that are still in the exploration stage of smart manufacturing.

Key words: intelligent manufacturing policy, policy evaluation, text mining, PMC index model