A two-stage explainable artificial intelligence approach for classification-based job cycle time prediction

Recently, many methods based on artificial neural networks (ANNs) or deep neural networks (DNNs) have been proposed to accurately predict the cycle time of a job. However, the prediction mechanism of an ANN is difficult to understand and communicate for users, which limits its acceptability (or usefulness). To solve this problem, a two-stage explainable artificial intelligence (XAI) approach is proposed in this study to better explain a classification-based cycle time prediction method. In the proposed methodology, first, jobs are divided into several clusters. A scatter radar diagram is then designed to illustrate the classification result. Compared with existing XAI techniques, the scatter radar diagram meets more requirements for better interpretation. Subsequently, an ANN is constructed for each cluster to predict the cycle times of jobs in the cluster. A random forest is then constructed to approximate the prediction mechanism of the ANN. In existing practice, the random forest generates many decision rules to predict the cycle time of a job, which may cause confusion for the user. To solve this problem, a systematic procedure is established to re-organize these decision rules. In this way, the first few decision rules can provide most of the information, and the user does not have to read all the rules. The two-stage XAI approach has been applied to a real case from the literature to evaluate its effectiveness.

Automated summary

In this study, a two-stage explainable artificial intelligence (XAI) approach is proposed to better explain a classification-based cycle time prediction method. The approach involves dividing jobs into clusters and using scatter radar diagrams to illustrate the classification result. Artificial neural networks (ANNs) are constructed for each cluster to predict cycle times, and a random forest is used to approximate the prediction mechanism. A systematic procedure is also established to reorganize decision rules and provide clearer information to users.