Smart Compliance AI Auditing for Green Manufacturing

Abstract

The degradation of the environment, welfare inequalities, and water scarcity are forcing organizations to consider their implications for the economy, society, and governance. However, organizations increasingly struggle to cope with these sustainability aspects and their interrelatedness. Both have implications for compliance with a growing number of sustainability standards. An AI-empowered compliance audit framework for assessing sustainability in manufacturing infrastructure is proposed. The technological framework aims to implement the audit in a fully automated way by integrating audit-related data from manufacturers and simplifying the audit process. It is based on proprietary audit algorithms that rely on machine learning, natural language processing, and reasoning. Due to the digital audit provision and knowledge management capabilities, the framework highlights the need for proper governance, data integration, and aligning the interests of credit rating agencies, manufacturers, and the ecosystem.

Research structures are naturally susceptible to changes brought about by environmental and technological shifts. The pandemic illustrated how fragile the social structure is and how technology can help build resilience. It is widely acknowledged that improving environmental conditions, increasing social welfare, and developing adequate governance models are essential for sustainable long-term economic development. However, the interaction between these three aspects remains unclear. Balancing this relationship has become even more difficult for manufacturing organizations relying on natural resources. Water scarcity not only threatens the quality and quantity of essential goods but also imposes greater restrictions on the proper functioning of production plants, infrastructure, and employee well-being. Therefore, a particular focus on sustainable manufacturing infrastructure is crucial.