Tree Like Decision Model in ESG Management for Textile Manufacturing

In ESG management areas, data consistency of textile manufacturing objects do not correspond to the structured information available in corporate ESG databases. This problem is partially associated with the fragmented level of implementation of digital monitoring technologies in the processes of sustainability management of manufacturing operations, as well as with the lack of adaptive support for promptly making changes to the decision-making framework by the governance relations structure of the organization. As a solution to these challenges, the integrated use of machine learning algorithms and tree-like decision modeling technologies is proposed. The article examines the development of ESG-driven decision models based on hierarchical analysis of sustainability indicators. It is proposed to use extended methodological frameworks and computational methods for processing "big data" in the environmental impact assessment of the textile sector. The article provides an extended evaluation of the efficiency of the decision support system, the principles of maintaining a structured basis for predictive analytics systems, and the technological process of preparing real-time ESG data for integration on the management platform. Using the example of textile supply chain assessment of the industry, the functioning of the technological framework on the use of AI-powered support in the optimization of sustainable production objects is shown.