Knowledge Discovery and Analytics in Process Reengineering: A Study of Port Clearance Processes

Due to the nature of the transactions involved in imports and the security threats associated with it, most nations have put agencies and machineries in place for regulating and monitoring the flow of trade. The objectives of these agencies include but not restricted to monitoring of trade transactions; revenue generation, facilitation of trade, policy and decision making, etc. Risk management is a major segment of the clearance process at the port and is consistently advocated by the world customs organization (WCO) and the world trade organization (WTO) respectively. In Nigeria, the Nigeria Customs Service (NCS) performs this function with the aid of Automated System for Customs Data (ASYCUDA) first implemented in 2006 during the modernization project of the Federal Government of Nigeria and now modernized as the Nigeria Customs Information System (NICIS). This study reviews the present imports clearance process to identify areas that may be remedied through process re-engineering using analytics. It adopts a hybrid methodology comprising descriptive methods, object-based and knowledge-based modeling with the aim of establishing a reliable model that could be integrated to advance the existing imports process and clearance process. The proposed model uses the k-means algorithm to discover pertinent patterns and trends with respect to areas that need enhancement thus helping the relevant authorities in achieving a competent and effective clearance process driven by knowledge-based process re-engineering.