Exploratory Factor Analysis and Random Forest Modeling of ROI Determinants in Robotic Process Automation (RPA) Implementation

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This study investigates the determinants of Return on Investment (ROI) in enterprise Robotic Process Automation (RPA) implementations through an integrated analytical framework combining Exploratory Factor Analysis (EFA) and Random Forest regression modeling.Using project-level data comprising five key performance indicators Robots Deployed, Budget (USD), Annual Savings (USD), ROI (%), and Employee Hours Saved the research identifies underlying latent structures and evaluates the predictive power of financial and operational variables on automation returns.EFA revealed a two-factor solution explaining 72.71% of total variance, interpreted as Financial Performance and Operational Efficiency.The Random Forest model demonstrated excellent predictive accuracy (Mean R² = 0.962, MAE = 7.90) with stable crossvalidation performance (SD = 0.0055).Feature importance analysis identified Annual Savings (35.91%),Budget (32.13%), and Employee Hours Saved (27.29%) as the dominant ROI predictors, while Robots Deployed contributed only 4.68%.SHAP-based explainability confirmed that realized economic benefits and workforce productivity gains constitute the primary mechanisms driving ROI generation.Although the Kaiser-Meyer-Olkin measure (0.412) indicated limited common variance, Bartlett's Test of Sphericity was significant (² = 117,907.48,p < .001),supporting factor extraction.The findings suggest that successful RPA implementations should prioritize measurable financial outcomes and operational efficiency over automation scale expansion.This research contributes empirical evidence on RPA value creation and provides actionable guidance for organizations seeking to optimize automation investments.Conceptual evaluation of XGBoost indicated only marginal expected improvements, confirming the robustness of the ensemble learning approach.

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