The study aims to develop a model for replacing human labor with industrial robots in the industrial production sector to promote sustainable growth. A mixed-methods approach was employed, combining qualitative interviews with nine experts and quantitative analysis based on survey data from 500 industrial enterprises. Structural Equation Modeling (SEM) confirmed that government policies, industrial Readiness, integration cooperation, and production potential are key determinants of successful robot adoption. Government policy exerted the most substantial direct influence on industrial Readiness and integration cooperation, which, in turn, positively affected production potential. The results emphasize the importance of collaborative strategies between public institutions, educational organizations, and private enterprises to enhance workforce capability and technological preparedness. The proposed Automated Robot–Human Labor Industrial Replacement (ARHLIR) Model offers both theoretical and practical implications for policymakers and industry leaders. The integration of policy support, organizational Readiness, and cooperative networks ensures that automation drives competitiveness, innovation, and long-term sustainability.

Industry plays a vital role in developing Thailand and supporting and driving businesses. The increase in industry benefits the national income. However, it also affects the environment. Industrial companies must adapt to reduce problems by developing organizational infrastructure, such as logistics activities or operations, which are the major systems and causes of environmental issues. Therefore, this study aims to study guidelines for logistic system and supply chain management for the environment in industrial business and develop a structural equation model (SEM). The study used a mixed-method research design; the qualitative method, in-depth interviews with nine experts and a focus group with 11 experts were used. Concerning the quantitative method, a questionnaire survey was used with 500 green industries. Descriptive statistics, inference statistics, and multivariate analysis were also used to analyze the data. The study found four essential aspects: (1) Customer orientation, (2) Green network, (3) Logistic process, and (4) Organization management. Finally, the hypothesis testing results found that in medium- and large-sized industries, the importance of the variables of the logistic system and supply chain management for the environment had no statistically significant difference at 0.05. The results from the analysis of the structural equation model found that the study passed the evaluation and was relevant to empirical data with a chi-square probability level of 0.066, a relative chi-square of 1.133, a goodness of fit index of 0.954, and root mean square error of approximation equal to 0.016.