An Advanced Inventory Data Mining System for Business Intelligence

• Published in: 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService) pp. 210 - 217
• Main Authors: Qifeng Zhou, Bin Xia, Wei Xue, Chunqiu Zeng, Ruyuan Han, Tao Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Data analysis; Data mining; Forecasting; Inventory management; Predictive models; Time series analysis
• DOI: 10.1109/BigDataService.2017.36

Inventory management plays a critical role to track inventory levels, orders, and sales of the retailing business. Effective inventory management is a capability necessary to lead in the global marketplace. In the current retailing market, a huge amount of data regarding stocked items in inventory is generated and collected every day. Due to the increasing volume of transaction data and their correlated relations, it is often a non-trivial task to efficiently manage stocked goods, yet it is imperative to explore the underlying dependencies of the inventory items and give insights into implementing intelligent management systems. However, existing inventory management systems rely on statistical analysis of the historical inventory data, and have a limited capability of intelligent management. For example, they usually do not have the ability to forecast item demand and detect anomalous patterns of item inventory transactions. There is little work reported in implementing intelligent inventory management solutions to reveal hidden relations with integrated data-driven analysis. In this paper, we present an intelligent system, called iMiner, to facilitate managing enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data and incorporate the latest advances in data mining technologies. iMiner provides comprehensive support for conducting many inventory management tasks, such as forecasting inventory, detecting anomalous items, and analyzing inventory aging.