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Supply Chain Management (SCM) can be seen from the upstream, midstream and downstream supply chains. Efficiency can be increased by business strategies in all three sections. The fashion industry such as garment and textile is a very dynamic industry where fashion trends change rapidly in a short time. How can produce products that can adjust to consumer trends or needs become a strategy in increasing the efficiency of SCM. SCM downstream side needs, namely customers must be analyzed by analyzing customer experience such as social influence and analysis to avoid running out of stock, the two things are analyzed with certain attributes. Consumer needs can be explored based on customer purchase history and the phenomenon of current fashion trends. Online marketing, e-commerce, social media as trading media can provide knowledge. Methods related to data analysis such as big data analysis and social trends continue to develop. In this study, the concept of analytic data will be proposed by applying various factors such as social factors, social influences, business, trade, purchase history, collaborated with various techniques with the aim of knowing the needs of people in the fashion world. What data is relevant will be analyzed and processed by relevant methods with the result tendency community on certain fashion. This research will produce an analytical data model of social influence that focuses on the domain of the fashion world which is the input and recommendation for the downstream part of the Supply Chain Management System in order to increase the efficiency and effectiveness of SCM.