International Journal of Engineering and Information Systems (IJEAIS)   Year: 2021 | Volume: 5 | Issue: 4 | Page No.: 17-26 Overcoming Process Delays Using JustNN ahmoud Hatem Al-Dalu, Nizar Jalal Al-Banna, Yahya Izzat Abu al-Qumbuz Abstract: Printers are always seeking higher productivity by increasing their production rates and minimizing process delays. When process delays have known causes, they can be mitigated by acquiring causal rules from human experts and then applying sensors and automated real-time diagnostic devices to the process. However, for some delays the experts have only weak causal knowledge or none at all. In such cases, Artificial Neural Network (ANN) which is a sub field of Artificial Intelligence can collect training data and pre-process it. A proposed ANN model was applied on a collected dataset from UCI machine Learning Repository to predict process delays known as cylinder banding in rotogravure printing. The dataset consist of 40 features with 528 sample cases. The aim of the ANN model is to train it to be able to predict whether the each sample case is a cylinder band or not. The ANN model was trained and validated in JustNN tool. The accuracy rate of predicting cylinder band or not was 81.03%.