This chapter focuses on forecasting, which is an important function of manufacturing systems. Many operations and production activities such as cycle time forecasting, sales forecasting, unit cost reduction, predictive maintenance, yield learning, etc. are based on forecasting. This chapter takes job cycle time forecasting as an example. Artificial intelligence (AI) techniques have many applications in job cycle time prediction. Of these, artificial neural network (ANN) (or deep neural network, DNN) applications are most effective, but are difficult for factory workers to understand or communicate. To address this issue, existing explainable AI (XAI) techniques and tools for explaining the inference process and results of ANNs (or DNNs) are introduced. We first introduce XAI tools for visualizing operations in ANNs (or DNNs), such as ConvNetJS, TensorFlow, Seq2Seq, and MATLAB, and then mention XAI techniques for evaluating the impact, contribution, or importance of each input on the output, including partial derivatives, odd ratio, out-of-bag (OOB) predictor importance, recursive feature elimination (RFE), Shapely additive explanation value (SHAP). Subsequently, XAI techniques for approximating the relationship between the inputs and output of an ANN (or DNN), especially simpler machine learning techniques such as case-based reasoning (CBR), classification and regression trees (CART), random forest (RF), gradient boosting decision trees, eXtreme gradient boosting (XGBoost), and RF-based incremental interpretation are introduced. The application of each XAI technique is supplemented with simple examples and corresponding MATLAB codes, allowing readers to get started quickly.