This study examines how digital manufacturing systems improve smart inventory management in a large Iraqi public industrial enterprise. A cross-sectional survey of employees (n = 123) conducted from April to August 2025 measured three capability dimensions—automation and robotics, data analytics and IoT integration, and digital integration and flexibility—and linked them to inventory outcomes. Reliability was strong (Cronbach’s alpha 0.88–0.94) and normality held (Kolmogorov–Smirnov p = 0.200 across constructs). Descriptive results showed high adoption levels for sensing, predictive analytics, and error-reducing automation. Multiple regression explained 79.6% of the variance in smart inventory management (R = 0.892, R² = 0.796, F = 248.65, p < 0.001). All three dimensions had positive, independent effects with standardized coefficients ranking: data analytics and IoT integration (β = 0.376), automation and robotics (β = 0.312), and digital integration and flexibility (β = 0.289). Diagnostics supported model adequacy (Durbin–Watson = 1.94, VIF < 2.0, Cook’s D max = 0.21). The findings indicate that a data-first capability stack—real-time sensing, predictive analytics, and governed dashboards—delivers the largest immediate gains in stock accuracy, replenishment, and cycle time, while stabilized execution and system-to-system coordination add complementary improvements. The research offers localized evidence of the general public manufacturers, as well as a step-wise roadmap that focuses on enterprise data layers, automation with a purpose, and integration of ERP-MES-WMS that institutionalizes the exception management and performance control.
Khawlah Radhi Athab (Wed,) studied this question.