DIGITAL TWIN–ENABLED OPTIMIZATION OF ELECTRICAL, INSTRUMENTATION, AND CONTROL ARCHITECTURES IN SMART MANUFACTURING AND UTILITY-SCALE SYSTEMS

Abstract

This quantitative, multi-site study examined whether digital twin–enabled optimization was associated with measurable improvements in electrical, instrumentation, and control (EIC) architecture performance across smart manufacturing and utility-scale systems. The study used a longitudinal, multilevel panel design with clustered observations at asset, system, and site levels. The final analytic sample included 18 cases (10 manufacturing plants and 8 utility-scale segments), 96 systems, and 1,842 assets observed over 180 days, yielding 17,280 system-days after data coverage screening. Composite indices representing observability, synchronization quality, integration readiness, and data integrity demonstrated acceptable internal consistency (α = 0.78–0.88). Multilevel regression results indicated that optimization exposure was associated with lower downtime and improved continuity. Downtime decreased by 0.74 hours per system-month (95% CI −1.05 to −0.43, p < 0.001) and availability increased by 1.9 percentage points (95% CI 1.1 to 2.7, p < 0.001). Efficiency outcomes improved, with normalized loss intensity decreasing by 3.6% (95% CI −5.2 to −2.0, p < 0.001) and voltage deviation indices decreasing by 4.1% (95% CI −6.8 to −1.4, p = 0.003). Integrity outcomes improved, as interruption rates decreased (IRR = 0.86, 95% CI 0.79 to 0.94, p = 0.001) and misoperation-related events declined (IRR = 0.81, 95% CI 0.70 to 0.94, p = 0.006). Data quality improved, with completeness increasing by 1.5 percentage points (95% CI 0.9 to 2.1, p < 0.001) and alignment issue rates decreasing by 0.8 percentage points (95% CI −1.2 to −0.4, p < 0.001). Domain moderation indicated stronger downtime reductions in manufacturing and stronger integrity gains in utilities. Mediation evidence suggested that observability and synchronization quality explained a meaningful portion of reliability and integrity improvements. These findings establish digital twin–enabled optimization as a critical lever for architectural resilience, providing a data-driven framework for scaling EIC systems in high-criticality environments.