The use of random forests to support the decision-making process for sustainable manufacturing

Abstract

Today's companies need to change the way they produce and consume goods to meet sustainability requirements. These companies are striving to increase production efficiency, while reducing the use of raw materials, reducing costs and reducing their impact on the environment. Intelligent decision support systems are an inseparable element supporting such action. The presented work the use the data mining method (random forests) to identify the parameters affecting the condition of the cutting blade is presented. The developed model was evaluated using indicators to assess the quality of classifications. The obtained results confirm the high quality of the predictive model. The proposed model can be used to support the decision-making process in determining the life of a cutting tool. This will reduce the consumption of raw materials and production waste, and thus reduce the company's environmental impact, which is consistent with the assumptions of sustainable production.