The rapid digitalization of industrial processes under the Industry 5.0 paradigm has led to increased data availability and the need for innovative approaches to official short-term statistical surveys data collection. In this framework, the study explores the feasibility of implementing a large-scale Machine-to-Machine (M2M) data transmission approach for official statistics, leveraging specialized modules integrated into advanced Enterprise Resource Planning (ERP) platforms. Unlike traditional M2M methods based on ad hoc procedures, the proposed approach aims for widespread applicability. Key aspects of the study include assessing the statistical burden imposed by Istat’s short-term surveys, evaluating the digital maturity of Italian enterprises (particularly SMEs), and outlining the phases of an experimental trial. The trial focuses on the highly digitalized metallurgy sector, using industrial production volume as a reference variable. It involves identifying suitable and widespread ERP platforms, evaluating business acceptance, and comparing M2M data collection with traditional survey methods. The first results suggest that M2M-based statistical data collection can enhance efficiency, reduce response burdens, and improve data timeliness. However, challenges remain, including initial implementation costs, system heterogeneity, and business resistance. The study contributes to ongoing efforts to modernize official statistics through a multi-source approach, aligning with broader digital transformation trends in European industry.