Decision Support System for Inventory Management using Data Mining Techniques
Vivek Ware1, Bharathi H. N2
1Vivek Ware, Department of Computer Engineering, K J Somaiya College of Engineering Vidhyavihar, Mumbai, India.
2Bharathi H. N, Department of Computer Engineering, K J Somaiya College of Engineering Vidhyavihar, Mumbai, India.
Manuscript received on July 24, 2014. | Revised Manuscript received on August 14, 2014. | Manuscript published on August 30, 2014. | PP: 164-168 | Volume-3 Issue-6, August 2014. | Retrieval Number: F3368083614/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Timely identification of newly emerging trends is needed in business process. Data mining techniques are best suited for the classification, useful patterns extraction and predications which are very important for business support and decision making. Patterns from inventory data indicate market trends and can be used in forecasting which has great potential for decision making, strategic planning. Our objectives is to get better decision making for improving sale, services and quality, which is useful mechanism for business support, investment and surveillance. An approach is implemented for mining patterns of huge stock data to predict factors affecting the sale of products. For this divide the stock data in three different clusters on the basis of sold quantities i.e. Dead-Stock (DS), Slow-Moving (SM) and Fast- Moving (FM) using K-means algorithm or Hierarchical agglomerative algorithm. After that Most Frequent Pattern (MFP) algorithm is implemented to find frequencies of property values of the corresponding items. MFP provides frequent patterns of item attributes and also gives sales trend in a compact form. Clustering and MFP algorithm can generate more useful pattern from large stock data which is helpful to get item information for inventory.
Keywords: Most Frequent Patterns, Clustering, Decision Making.