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Determining the needs of each item of a product that has many types is something that is difficult to do if done manually. If this happens it will be a problem in decision making by management. As experienced by Cileunyi Collection Store where as of October 2018 the number of goods reached 2,469 types / Stock Keeping Units. (SKU). Production staff find it difficult to analyze how much production needs of each item so that it influences the decision-making by management in determining capital decisions and storage space that must be allocated. In this study will explaine the proposed decision support system in the form software application to assist the process of clustering goods and provide recommendations for the procurement of goods based on predetermined criteria in the Cileunyi Collection Store. Two algorithmic approaches, Multiple Decision Making Criteria (MCDM) and Fuzzy C-Mean (FCM) are used. The MCDM algorithm is used to determine the order or priority in multi-criteria analysis and the FCM algorithm is used to cluster with the same membership. The results of study indicate that the MCDM algorithm and the FCM algorithm can provide recommendations for the procurement of goods with various specified criteria. Based on the calculation of the accuration of both algorithm, the MCDM algorithm reaches 90% and the FCM algorithm reaches 90%.