The Role of Predictive Analytics in Inventory Management
DOI:
https://doi.org/10.36676/urr.v10.i4.1358Keywords:
Predictive analytics, inventory management, demand forecasting, data-driven insights, machine learning, stock optimizationAbstract
The use of predictive analytics has emerged as a game-changing instrument in the field of stock management. This tool provides a data-driven strategy to optimise stock levels, reduce costs, and improve operational efficiency. The purpose of this abstract is to investigate the role that predictive analytics plays in revolutionising how inventory management processes are carried out. This is accomplished by using historical data, statistical algorithms, and machine learning approaches to estimate future inventory requirements.
At its foundation, predictive analytics is doing an analysis of previous inventory data, sales trends, and external variables in order to forecast future demand with a high degree of precision. When it comes to inventory management, traditional methods sometimes rely on static models and gut reactions, which may result in problems such as overstocking or stockouts. Alternatively, predictive analytics offers a dynamic and data-informed viewpoint, which enables firms to alter their inventory strategy in a proactive manner as necessary. Companies are able to estimate demand changes based on seasonality, market trends, promotions, and other factors when they integrate predictive models into their operations. This results in more accurate inventory planning.
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