Yonga levha tesisi için uygulama: Kalite tahminlemesi ve dijital dönüşüm için web tabanlı karar destek sistemi

As it is same for most of the production procedures, a certain quality level must be derived in the particle board production. In the particleboard production, a series of samples taken from the production line for the quality analysis of the products produced. These samples are put to the test for analysis and observe whether the quality metrics are satisfying the needs or not. Sampling can be done after a set of production is completed. Laboratory tests samples for at least three hours. If the results of the test are out of acceptable limits, then the facility changes the production parameters, waits for new production output to gain new samples, tests new samples again. While quality testing procedures are running, the products that do not satisfy quality limits, cannot be delivered to customers, which results in crucial capacity loss. In this study a decision support system is developed to measure real-time effects of changes of production parameters on quality metrics by using machine learning based prediction models with live production data collected from production line. Decision support system developed in this study enables to predict three different quality metrics while margin of error is realized around 5%, on the average.