Small and medium-sized enterprises (SMEs) face persistent challenges in maintaining industrial equipment due to a combination of factors such as limited resources, a shortage of skilled personnel, and inadequate IT infrastructure. Although equipment maintenance is a huge burden for SMEs, the literature lacks a tailored solution to be efficient, cost-effective, and sustainable. This study introduces a modular digital twin framework specifically designed for SME maintenance operations, integrating Microkernel Architecture, Service-Oriented Architecture, and a toolkit to identify the optimal features of digital twins based on organizational requirements. This approach ensures scalability, affordability, and adaptability in diverse SME contexts. Using three real-world SME cases in the agri-food sector, informed by expert interviews and evaluated through Monte Carlo simulation, the study finds that predictive and prescriptive modular digital twin strategies can substantially outperform reactive and preventive approaches under resource-constrained SME conditions, with scenario-based estimates indicating up to 83% reduction in average downtime-related cost and up to 42% reduction in average repair time. These quantitative gains are contingent on the simulated operating assumptions, particularly equipment criticality, technician response delays, and spare-part logistics. The findings provide a practical and scalable roadmap for SMEs seeking to adopt intelligent maintenance systems in a cost-effective manner. The proposed modular approach allows firms to begin with essential monitoring capabilities and gradually integrate predictive, prescriptive, and autonomous functions as their technological readiness evolves. This phased adoption strategy reduces investment risk, limits vendor lock-in, and supports incremental system upgrades, while remaining well suited to compliance-driven sectors such as food processing and agriculture.