Leveraging behavioral data mining for cost reduction and efficiency enhancement in manufacturing

In response to the call for sustainable development and seeking new break-throughs in achieving green transformation across the entire industrial chain and management processes, this study introduces the XGBoost regression method. It constructs a machine learning model using 140,566 historical data points collected from the data acquisition system. Additionally, it incorporates local climate factors such as average temperature, weather conditions, maximum temperature, minimum temperature, and wind speed. The model is thoroughly explored and analyzed. Experimental results confirm that the model developed in this study exhibits commendable accuracy and stability. It provides a strong theoretical foundation for achieving cost reduction and efficiency enhancement in factories, aligning with the principles of green development.