Business Process Mining is an emerging field dedicated to the analysis of Business Process Models (BPM) by deriving insights from transaction logs generated through various industrial and Supply Chain Management execution information systems, such as a ERP or an MES. This approach facilitates the examination, assessment, and improvement of business activities across the entire lifecycle, from initiation to completion. In this paper, we propose a framework to detect anomalies in business processes by analyzing event logs produced from purchasing transactions. The framework focuses on anomaly detection in the supply chain and aims to improve its accuracy, boost its workflow across multiple business process levels, and improve supply chain processes with an emphasis on security and automation. The suggested approach uses Ontology and designed SWRL rules to classify anomalies in business transactions. The framework has been evaluated using transaction logs from a simulated general a purchasing scenario, as an example of a business process mode, proving its capability to effectively identify and categorize anomalous business transactions.