DIGITAL TWIN-DRIVEN PROCESS MODELING FOR ENERGY EFFICIENCY AND LIFECYCLE OPTIMIZATION IN INDUSTRIAL FACILITIES

Abstract

This quantitative cross-sectional, case-based study investigates how digital twin driven process modeling supports energy efficiency and lifecycle optimization in industrial facilities. The problem addressed is the limited empirical evidence on whether digital twin capabilities translate into measurable improvements in plant level performance. Using a structured survey, data were collected from 180 cloud or enterprise facility cases, targeting operations, maintenance, energy and digitalization professionals. Key independent variables were digital twin implementation level, data integration and quality, real time analytics capability and process model fidelity, measured on Likert 5-point scales, while dependent variables captured perceived energy efficiency performance and lifecycle optimization performance. The analysis plan combined dehmmscriptive statistics, reliability testing, Pearson correlations and multiple regression with controls for facility size, industry type and equipment age. Results show moderate to high maturity of digital twin practices (means 3.55 to 3.92) and positive outcomes for energy and lifecycle performance (means 3.58 to 3.65). Digital twin dimensions were moderately to strongly correlated with energy and lifecycle indices (r = 0.42 to 0.57, p < 0.001). Regression models explained 49 percent of the variance in energy efficiency and 46 percent in lifecycle optimization; data integration and implementation were the strongest predictors of energy efficiency (β = 0.29 and 0.24), while real time analytics, implementation, data integration and model fidelity significantly predicted lifecycle performance (β = 0.27, 0.23, 0.21, 0.15). The findings imply that industrial facilities should prioritize robust data integration and embedded analytics when scaling digital twin initiatives to secure durable gains in energy efficiency and asset lifecycle performance.