Impact of Predictive Analytics and Ensemble Learning on Operational Efficiency and KPI Forecasting in U.S. Engineering Firms

Abstract

This study examined the impact of predictive analytics and ensemble learning on operational efficiency and KPI forecasting accuracy in U.S. engineering firms using a quantitative, cross-sectional design. Data were collected from 236 professionals across construction, manufacturing, and infrastructure sectors. The findings revealed strong and statistically significant relationships among the key variables. Correlation analysis indicated that predictive analytics adoption was highly associated with operational efficiency (r = 0.72) and KPI forecasting accuracy (r = 0.70), while ensemble learning integration showed the strongest relationship with forecasting accuracy (r = 0.74). Multiple regression results demonstrated that predictive analytics significantly influenced operational efficiency (β = 0.45, p < 0.001), whereas ensemble learning had a greater impact on KPI forecasting accuracy (β = 0.51, p < 0.001). The models explained 56% of the variance in operational efficiency and 62% in KPI forecasting accuracy, indicating substantial explanatory power. Sector-based analysis showed that manufacturing firms achieved the highest efficiency (M = 4.28), while construction firms reported the highest forecasting accuracy (M = 4.31). Additionally, firms with high analytics adoption demonstrated significantly better performance outcomes (efficiency M = 4.42; forecasting M = 4.39) compared to those with low adoption levels. The results confirmed that the integration of predictive analytics and ensemble learning enhanced both operational processes and forecasting reliability. Overall, the study provided strong empirical evidence that advanced analytics capabilities play a critical role in improving performance outcomes in engineering firms.