Most of large-scaled manufacturing company deals with more than fifties suppliers, which are located within the country and abroad. It is also challenging to manage once each partner has different characteristics. Thus, a proper supplier relationship management (SRM) program is needed. Most of the company has gathered huge number of data; however, they rarely process it optimally. It drives to a longer time consumed to determine strategic decisions making. On the other hand, the business competition is very dynamic. As consequences new approach to utilise the big data is highly demanded. Data analytic provides a solution to manage the SRM in easier, more efficient, and effective way. This research aims to implement data analytics on managing manufacturer and strategic partners relationship, which accommodates supplier performance criteria and its dynamic behaviours. It also considers both from theoretical definition and practical implications. The analysis uses basis consideration of activities within the SRM. It follows Cross Industry Standard Process for Data Mining (CRISP-DM) framework. One data clustering tool is applied to determine supplier classification and further decision. In the end, this study finds that by using relevant data sources, data analytic successfully shows a more comprehensive analysis on SRM activities in manufacturing company.