Model Digital Twin untuk Mewujudkan Industri Pengolahan Pangan Rendah Karbon: Pendekatan Baru Menuju Agroindustri Berkelanjutan

Abstract

The transition toward sustainable agroindustry requires innovative approaches capable of improving operational efficiency while reducing carbon emissions in the food processing industry. Although digital technologies have been widely adopted within the context of Industry 4.0, the utilization of digital twins to support decarbonization strategies in food processing remains limited and requires a more comprehensive conceptual framework. This article examines the role of digital twin models in achieving low-carbon food processing industries through the integration of real-time data, virtual simulation, and predictive decision-making. The study employs a systematic literature review by analyzing recent scholarly publications on digital twins, sustainable manufacturing, food systems, and carbon emissions management. The findings indicate that digital twin models can optimize energy consumption, reduce material losses, improve production efficiency, and facilitate continuous monitoring and control of carbon emissions. Furthermore, the integration of digital twins with cyber-physical systems and data analytics enables the development of a more adaptive, transparent, and sustainable agroindustrial ecosystem. This article argues that digital twins represent a strategic instrument for accelerating the transition toward low-carbon food processing industries and contributes a conceptual framework linking digital transformation with agroindustrial sustainability objectives. The findings provide theoretical implications for the advancement of digital agroindustry research and practical implications for industrial strategies and policy development aimed at decarbonizing the food sector.