Developing a machine learning integrated e-procurement system for Nigerian public procuring entities

Public procuring entities globally have been adopting the digitised approach in order to improve efficiency. However, existing systems have been found to be fragmented and cannot be generalised as they are country-specific. This study, therefore, designed and developed a web-based e-procurement system capturing the entire public procurement lifecycle including a machine learning component. The study adopted the RIPPLE and unified process methodologies of the system development lifecycle and developed one domain conceptual model, covering the entire procurement lifecycle. Then, static analysis conceptual models were developed to capture different processes of the procurement lifecycle. The study designed and developed the system architecture capturing physical architecture and user interface, and machine learning models for automated searching and classification of tender and spends using UNSPSC taxonomy. This study provides a fundamental step toward the automation of e-procurement systems for public procuring entities.