This chapter starts by defining the concept of cycle time modeling and analysis. Various cycle time modeling and analysis techniques and tools are then introduced. First, value stream mapping is applied to identify the operations of a job with the longest waiting times and should be improved first. Subsequently, after training the cycle time prediction model using machine learning or deep learning methods, a causal cycle time relationship analysis is performed to evaluate the impact of each job attribute on the cycle time forecast of a job, so as to find out the most important job attributes for the job. To this end, Shapely analysis and related tools are introduced with numerical examples and program codes. Subsequently, to explain and communicate the cycle time prediction mechanism and results, several explainable artificial intelligence (XAI) techniques and tools can be applied. First, for cycle time prediction problems involving big data, job classification is usually performed. Therefore, before predicting the cycle times, jobs need to be classified. To this end, traditional and XAI tools for explaining the job classification process and results are reviewed. Seven requirements that need to be met for excellent explanations are also listed. Subsequently, existing and XAI techniques and tools are applied to explain the cycle time prediction process and results, such as random forest-based incremental explanation. In addition, a systematic procedure is also established to confirm whether a trained cycle time prediction model conforms to domain knowledge, and on this basis, the improved random forest-based incremental explanation technique is proposed.