Data Modeling and Analysis Based on the Automated Storage and Retrieval System

The Internet of things (IoT) is an important part of new generational information technology. It is versatile and be used in every trade. Such as the Automated Storage and Retrieval System (AS/RS) which is used in retail trade. The AS/RS follows protocol of communication by RFID technology to classify and count in order to achieve merchandises´ identification, location, tracking and management. Although the AS/RS has been popularized in China, most of companies still use EOQ model which easily causes sellout problem to analyze purchase quantity and storage issue. In order to resolves these flaws and optimizes the EOQ model to fit update of the AS/RS, this paper will use regression analysis to search relationship between purchase quantity and sale volume and make up the EOQ´s defect which ignores effect of purchase quantity. The optimized model solves problems about sellout and draggy sale, increasing efficiency of the Automated Storage and Retrieval System.