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A Systematic Review on Predictive Analytics for Stock Management System
Abstract
The item list creates a major factor as a working capital for many commercial and industrial companies. For inventory, the system can include raw materials, finished goods, continuous operation, supplies and other accessories. To maintain the viability of business enterprises, the bottom engineering of stock is always required. Inventory management is designed to control the investment value of existing assets, the types of assets that are carried in a cell to meet production needs. Stock Exchange Management System is an application made using Python to provide easy-to-follow products, shares and inventory information as well as buying and selling information. This app also records shares currently available in the store. The controlling entity can view the sales report and purchase details of the products stored in the database. The classification and analysis involved in the system can help in predicting the sales details of a particular product in a specific database. This app can be used in any kind of store to modify the process of manually keeping records related to the stock keeping subject. All purchasing information can be automatically saved and provide instant access to archived records. It also highlights important reviews about the business so that growth can be easily measured and provides various reports that show related information so that important decisions can be taken. This application is supported to complete and, in some cases, reduces the complexity of the existing system. In addition, the program is designed in such a way that companies can perform tasks in a smooth and efficient manner. Applied machine learning techniques are used in this program that increase the effectiveness of tracking technology in inventory management and provide relevant information to aid in planning for the future.
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References
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