An explainable deep-learning approach for job cycle time prediction

Abstract

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, the prediction mechanism of a DNN is complex and difficult to communicate. This limits its acceptability (or practicability) in real-world applications. An explainable deep-learning approach is proposed to solve this problem in this study. This study proposes a classification and regression tree (CART) to explain the prediction mechanism of a DNN for job cycle time prediction. The predicted value of each branch in the CART is replaced by a fuzzy linear regression (FLR) equation that estimates the cycle time range to compensate for the insufficient explainability. The explainable deep-learning approach has been applied to a real-world study from the literature to evaluate its effectiveness. According to the experimental results, the explainability of the prediction mechanism of the DNN, measured in terms of root mean squared error (RMSE), using the CART was high. In addition, the proposed methodology was able to make local explanations.