The global supply chain landscape is being reshaped by the convergence of Industry 4.0 technologies, especially Artificial Intelligence (AI), which enables smarter, more resilient, and sustainable logistics and production systems. This study proposes a design science approach to developing an AI-enabled decision support system (DSS) tailored for supply chain resilience and environmental sustainability in emerging industrial contexts. By applying the Design Science Research Methodology (DSRM), we present a system composed of predictive analytics, real-time optimization, and digital twin simulation modules. A simulation-based case study in Indonesia’s fast-moving consumer goods (FMCG) sector shows that the DSS improved order fulfillment rate by 9%, reduced logistics costs by 18%, and lowered carbon emissions by 19%. The study contributes a validated and modular framework for smart SCM implementation aligned with the principles of Industry 4.0 and the UN Sustainable Development Goals (SDGs).