The Application of Data Mining in the Production Processes
This paper addresses the challenge of analyzing large volumes of industrial production data to improve production and quality, providing a comparison of several algorithms for practitioners in manufacturing.
This study applied seven data mining algorithms (KNN, Decision Tree, SVM, Random Forests, ANN, Naïve Bayes, and AdaBoost) to analyze industrial production data. Decision Tree, Random Forest, and AdaBoost achieved the best results in terms of accuracy and area under the curve (ROC) for classifying production data based on three attributes.
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, Naïve 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.