The Application of Data Mining in the Production Processes

TL;DR

This paper explores the application of data mining algorithms to analyze industrial production data, demonstrating that decision trees and ensemble methods like Random Forest and AdaBoost achieve the best accuracy in classifying production data.

Contribution

It evaluates seven data mining algorithms on production data, identifying decision trees and ensemble methods as most effective for handling mixed data types in industrial processes.

Findings

Decision trees and ensemble methods outperform other algorithms in accuracy.

Random Forest and AdaBoost achieve the highest ROC and accuracy.

Decision trees effectively handle both numerical and categorical data.

Abstract

Traditional statistical and measurements are unable to solve all industrial data in the right way and appropriate time. Open markets mean the customers are increased, and production must increase to provide all customer requirements. Nowadays, large data generated daily from different production processes and traditional statistical or limited measurements are not enough to handle all daily data. Improve production and quality need to analyze data and extract the important information about the process how to improve. Data mining applied successfully in the industrial processes and some algorithms such as mining association rules, and decision tree recorded high professional results in different industrial and production fields. The study applied seven algorithms to analyze production data and extract the best result and algorithm in the industry field. KNN, Tree, SVM, Random Forests, ANN, Na\"ive Bayes, and AdaBoost applied to classify data based on three attributes without neglect any variables whether this variable is numerical or categorical. The best results of accuracy and area under the curve (ROC) obtained from Decision tree and its ensemble algorithms (Random Forest and AdaBoost). Thus, a decision tree is an appropriate algorithm to handle manufacturing and production data especially this algorithm can handle numerical and categorical data.