Leveraging Digital Twin Technology to Improve Logistics Operations Efficiency in Saudi Food Manufacturing

Abstract

The complexity and uncertainty of logistics operations require organizations to improve data integration, process visibility, and decision-making. This study examines the effect of Digital Twin Technology on Logistics Operations Efficiency in food manufacturing companies in the Kingdom of Saudi Arabia. A quantitative cross-sectional design was adopted, and data were collected through an electronic questionnaire from 195 employees and managers working in food manufacturing companies in Saudi Arabia. Digital Twin Technology was examined through five dimensions: Real-Time Data Integration, Bidirectional Connectivity, Virtual Representation and Simulation Capability, Data Analytics and Predictive Capability, and Operational Services and Decision-Support Capability. Logistics Operations Efficiency was assessed in terms of resource and time utilization, reduced waiting and downtime, minimized delays and unproductive movements, and improved material flows. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that all five dimensions have positive and statistically significant effects on Logistics Operations Efficiency. Data Analytics and Predictive Capability showed the strongest effect (β = 0.27, p < 0.001), followed by Real-Time Data Integration (β = 0.24, p < 0.001). The overall Digital Twin Technology construct demonstrated a positive effect on Logistics Operations Efficiency (β = 0.72, p < 0.001), explaining 52% of its variance. The study provides empirical evidence supporting Digital Twin adoption to improve logistics efficiency in Saudi food manufacturing.