Predictive Analytics for Sales Forecasting and Inventory Management
DOI:
https://doi.org/10.62802/7t6wq430Keywords:
Predictive analytics, sales forecasting, inventory management, demand forecasting, machine learning, time series analysis, operational efficiency, data-driven decision-making, profitability, real-time analysisAbstract
Predictive analytics has become a cornerstone of modern business operations, particularly in the domains of sales forecasting and inventory management. By leveraging historical data, machine learning algorithms, and statistical models, predictive analytics empowers businesses to anticipate future demand, optimize inventory levels, and enhance operational efficiency. This research explores the integration of predictive analytics into sales and inventory processes, focusing on its ability to reduce waste, minimize stockouts, and improve profitability. Key areas of investigation include the application of time series analysis, regression models, and AI-driven demand forecasting. The study also examines challenges such as data quality, integration complexities, and the need for real-time analysis in dynamic market conditions. Through case studies and industry insights, this research highlights best practices for implementing predictive analytics to achieve data-driven decision-making. Ultimately, it provides a roadmap for businesses to align predictive capabilities with strategic objectives, creating a more agile and competitive operational framework.
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