중소기업의 정보화 수준에 따른 비즈니스 프로세스 효율성 예측: 머신러닝 기반 분류 모델

In Korea, small and medium enterprises(SMEs) constitute 99% of the total business landscape, wielding significant influence on the nation's economy. Informatization has emerged as a potent tool for enhancing the productivity and efficiency of SMEs. However, the tangible impact of informatization on the business process efficiency of individual companies remains an open question. This study introduces a machine-learning-based model designed to predict the business process efficiency of SMEs based on their level of informatization. Various machine-learning techniques, including XGBoost, Random Forest, Decision Tree, and Artificial Neural Network, are employed and rigorously evaluated to identify the most effective model for our study. Our analysis, utilizing SHAP (SHapley Additive exPlanations), sheds light on crucial variables that significantly contribute to the predictive capabilities of the model.