This project represents the initial phase of developing a holistic, data driven formulation of service and supply chain optimisation problem. This is a prime prerequisite into understanding links between billions of data records generated from supply chain business processes. We performed an investigation of how several data relationship discovery algorithms can be combined to identify more comprehensive links between database tables. This could be used in providing database users/analysts with the opportunity to understand data relationships and the ability to extract insights from data for commercial advantages like reducing time-intensive analysis and exploration of data by domain experts. We investigated eight discovery algorithms to identify potential links between database tables in different ways based on different levels of database information. We further propose a framework for combining the discovery algorithms in a view to reduce the generalization of error of the discovery by an individual algorithm.