Providing A Model for Predicting and Detecting Destructive Processes to Prevent the Production of Waste and Defective Products: A Data Mining Approach

Today, most industries use statistical quality control tools to improve quality and reduce the defective products and waste, but the high volume of data requires the help of a powerful tool to control processes. One of the objectives of the present study is to predict defective products and prevent their production using data mining tools due to the high power in data analysis and its predictive nature, which is less used in the industry. In this study, the statistical population of all parts produced in 2017 by Shabrun Company. The statistical sample is 2400 pieces of radiators that were randomly selected from the production line. In the operational phases of data mining, three decision tree algorithms were used: C R Tree, Quest Tree and Chaid Tree. Using these algorithms, the most important criteria affecting quality control and rules leading to the quality of parts were determined. Comparative results showed that despite the validity of all three algorithms, the C R Tree algorithm had the highest accuracy. Adherence to the rules resulting from the implementation of these algorithms has led to the detection and prevention of waste generation, which has increased efficiency and prevented the loss of time and cost in this production unit.