<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) face significant challenges in balancing supply and demand amidst the rapid volatility of the digital economy era. This condition is primarily driven by conventional, reactive decision-making and a failure to utilize social media data beyond mere marketing metrics, resulting in operational inefficiencies such as overstocking or stockouts. This research aims to address this gap by developing an Artificial Intelligence-Based Prescriptive Framework for MSME Supply-Demand Orchestration. Adopting a Design Science Research (DSR) methodology, specifically focusing on problem identification, objective definition, and conceptual artifact design, this study synthesizes insights from 100 recent articles (2020-2025). The proposed conceptual artifact introduces a novel three-tier system architecture: a Sensing Layer utilizing NLP (BERT) to extract hyper-local community engagement signals into a Social Engagement Index (SEI), a Reasoning Layer employing a Hybrid Neuro-Fuzzy engine to accommodate MSME data sparsity, and a Prescriptive Actuation Layer. The findings present a mechanism that transforms unstructured qualitative social sentiment into quantitative, actionable logistical commands (e.g., "Priority Restock"), demonstrating superiority over existing predictive or macro-level models by offering a closed-loop, hyper-local solution. In conclusion, this research successfully formulates a framework that shifts MSME management paradigms from being purely reactive to actively responsive to social signals, theoretically expanding Social-SCM integration and practically offering a pathway to mitigate inventory risks and enhance economic sustainability.]]>
