Business Process Mining is considered one of the emerging fields that focuses on analyzing Business Process Models (BPM), by extracting knowledge from event logs generated by various Supply chain Management information systems, such as ERP and PLM, for the purpose of auditing, monitoring, and analysis of business activities for future improvement and optimization throughout the entire lifecycle of such processes, from creation to conclusion. Such a framework will help enhance the accuracy of anomaly detection in the global Supply Chain, improve the multi-level business processes workflow, and optimize the processes in the Supply Chain in terms of security and automation. In this work, a Bidirectional Long Short-Term Memory (Bi-LSTM)-autoencoder Neural Network was utilized for the prediction of the execution of cases, through training and testing the model on event traces extracted from event logs related to a procurement business process model, which is one of the main components of the supplychain. The approach consisted of three phases: preprocessing the logs, classification, and categorization in addition to all the activities related to implementing the Bi-LSTM model, including network design, training, and model selection. Our results showed that our model was able to predict the next activity in the sequence as well as detect anomalous ones with over 80% accuracy on average.