Conventional manufacturing execution system (MES) reporting systems often suffer from fragmented sensor data management, labor-intensive report preparation, delayed decision-making, and limited capability for the intelligent interpretation of heterogeneous manufacturing information. Therefore, the objective of this study is to develop a sensor-assisted MES framework integrating large language models (LLMs) and multi-modal collaborative processing (MCP) to improve manufacturing information transparency, report generation efficiency, and intelligent operational decision-making. In this study, we propose a sensor-assisted MES framework integrating LLMs with MCP to accelerate digital transformation and improve intelligent decision-making in smart manufacturing environments. Conventional MES reporting processes often rely on manual data collection, fragmented information analysis, and delayed operational feedback, which reduce management responsiveness and limit enterprise operational performance. By combining advanced natural language processing, heterogeneous sensor data fusion, and Industrial Internet of Things (IIoT)-based monitoring, the proposed LLM+MCP framework enables the automated extraction, analysis, visualization, and synthesis of manufacturing information into intelligent executive reports. The framework integrates multiple sensing modalities, including time-series production signals, equipment operation records, process parameters, tabular manufacturing data, and unstructured operational logs, thereby supporting real-time monitoring, anomaly identification, and adaptive management decisions. Through the designed intelligent reporting and sensing architecture, manufacturing managers can rapidly obtain operational insights and optimize production scheduling, resource allocation, and quality control strategies. Experimental results demonstrate that the proposed system achieves more than 90% reduction in report generation time, including reductions from 60 to 5 min for daily reports, 180 to 15 min for weekly reports, and 480 to 30 min for monthly reports, while maintaining 99.9% system availability and stable response latency ranging from 5 to 60 s. From the results, we can confirm that the proposed sensor-integrated LLM+MCP framework effectively enhances manufacturing information transparency, accelerates enterprise digital transformation, improves intelligent decision-making mechanisms, and ultimately strengthens operational efficiency, management responsiveness, and overall enterprise performance in smart manufacturing systems. Although challenges remain regarding heterogeneous sensing-data quality, data consistency, and the reliability of AI-generated content, the proposed framework addresses these issues through multi-modal sensing integration, structured data preprocessing, and low-rank adaptation (LoRA)-based domain adaptation, thereby improving the robustness and practicality of intelligent manufacturing analytics.